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2 IETF A. Taddei
3 Internet-Draft C. Wueest
4 Intended status: Informational K. Roundy
5 Expires: July 12, 2020 Symantec Corporation
6 D. Lazanski
7 Last Press Label
8 January 09, 2020
10 Capabilities and Limitations of an Endpoint-only Security Solution
11 draft-taddei-smart-cless-introduction-02
13 Abstract
15 In the context of existing, proposed and newly published protocols,
16 this draft RFC is to establish the capabilities and limitations of
17 endpoint-only security solutions and explore benefits and
18 alternatives to mitigate those limits with the support of real case
19 studies.
21 Status of This Memo
23 This Internet-Draft is submitted in full conformance with the
24 provisions of BCP 78 and BCP 79.
26 Internet-Drafts are working documents of the Internet Engineering
27 Task Force (IETF). Note that other groups may also distribute
28 working documents as Internet-Drafts. The list of current Internet-
29 Drafts is at https://datatracker.ietf.org/drafts/current/.
31 Internet-Drafts are draft documents valid for a maximum of six months
32 and may be updated, replaced, or obsoleted by other documents at any
33 time. It is inappropriate to use Internet-Drafts as reference
34 material or to cite them other than as "work in progress."
36 This Internet-Draft will expire on July 12, 2020.
38 Copyright Notice
40 Copyright (c) 2020 IETF Trust and the persons identified as the
41 document authors. All rights reserved.
43 This document is subject to BCP 78 and the IETF Trust's Legal
44 Provisions Relating to IETF Documents
45 (https://trustee.ietf.org/license-info) in effect on the date of
46 publication of this document. Please review these documents
47 carefully, as they describe your rights and restrictions with respect
48 to this document. Code Components extracted from this document must
49 include Simplified BSD License text as described in Section 4.e of
50 the Trust Legal Provisions and are provided without warranty as
51 described in the Simplified BSD License.
53 Table of Contents
55 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
56 2. Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . 4
57 3. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 5
58 4. Disclaimer . . . . . . . . . . . . . . . . . . . . . . . . . 6
59 5. Endpoints: definitions, models and scope . . . . . . . . . . 6
60 5.1. Internal representation of an endpoint . . . . . . . . . 7
61 5.2. Endpoints modeled in an end-to-end context . . . . . . . 8
62 6. Threat Landscape . . . . . . . . . . . . . . . . . . . . . . 9
63 7. Endpoint Security Capabilities . . . . . . . . . . . . . . . 11
64 8. What would be a perfect endpoint security solution? . . . . . 14
65 9. The defence-in-depth principle . . . . . . . . . . . . . . . 15
66 10. Endpoint Security Limits . . . . . . . . . . . . . . . . . . 16
67 10.1. No possibility to put an endpoint security add-on on the
68 UE . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
69 10.1.1. Not receiving any updates or functioning patches . . 18
70 10.1.2. Mirai IoT bot . . . . . . . . . . . . . . . . . . . 19
71 10.2. Endpoints may not see the malware on the endpoint . . . 21
72 10.2.1. LoJax UEFI rootkit . . . . . . . . . . . . . . . . . 21
73 10.2.2. SGX Malware . . . . . . . . . . . . . . . . . . . . 22
74 10.2.3. AMT Takeover . . . . . . . . . . . . . . . . . . . . 22
75 10.2.4. AMT case study (anonymised) . . . . . . . . . . . . 23
76 10.2.5. Users bypass the endpoint security . . . . . . . . . 24
77 10.3. Endpoints may miss information leakage attacks . . . . . 24
78 10.3.1. Meltdown/Specter . . . . . . . . . . . . . . . . . . 24
79 10.3.2. Network daemon exploits . . . . . . . . . . . . . . 24
80 10.3.3. SQL injection attacks . . . . . . . . . . . . . . . 25
81 10.3.4. Low and slow data exfiltration . . . . . . . . . . . 25
82 10.4. Suboptimality and gray areas . . . . . . . . . . . . . . 26
83 10.4.1. Stolen credentials . . . . . . . . . . . . . . . . . 26
84 10.4.2. Zero Day Vulnerability . . . . . . . . . . . . . . . 27
85 10.4.3. Port scan over the network . . . . . . . . . . . . . 27
86 10.4.4. DDoS attacks . . . . . . . . . . . . . . . . . . . . 28
87 11. Learnings from production data . . . . . . . . . . . . . . . 29
88 11.1. Endpoint only incidents . . . . . . . . . . . . . . . . 30
89 11.2. Security incidents detected primarily by network
90 security products . . . . . . . . . . . . . . . . . . . 31
91 11.2.1. Unauthorized external vulnerability scans . . . . . 31
92 11.2.2. Unauthorized internal vulnerability scans . . . . . 32
93 11.2.3. Malware downloads resulting in exposed endpoints . . 32
94 11.2.4. Exploit kit infections . . . . . . . . . . . . . . . 32
95 11.2.5. Attacks against servers . . . . . . . . . . . . . . 33
96 12. Regulatory Considerations . . . . . . . . . . . . . . . . . . 34
97 12.1. IoT Security . . . . . . . . . . . . . . . . . . . . . . 34
98 12.2. Network infrastructure . . . . . . . . . . . . . . . . . 35
99 12.3. Auditing and Assessment . . . . . . . . . . . . . . . . 35
100 12.4. Privacy Considerations . . . . . . . . . . . . . . . . . 36
101 13. Human Rights Considerations . . . . . . . . . . . . . . . . . 36
102 14. Security Considerations . . . . . . . . . . . . . . . . . . . 36
103 15. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 36
104 16. Informative References . . . . . . . . . . . . . . . . . . . 36
105 Appendix A. Contributors . . . . . . . . . . . . . . . . . . . . 41
106 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 42
108 1. Introduction
110 This Internet Draft aims to be a reference to the designers of
111 protocols on the capabilities and limitations of security solutions
112 on endpoint devices against malware and other attacks. As security
113 is entering a new phase in the arms race between attackers and
114 defenders, with many technical, economic and regulatory changes, and
115 with a significant increase in major data breaches, it is a good
116 moment to propose a systematic review and update on what is an old
117 and constantly evolving problem: endpoint security.
119 With the above context in mind this document will focus on the
120 capabilities and limitations of an endpoint-only security solution.
122 We want to explore a number of questions:
124 o What endpoint models do we have?
126 o What is the threat landscape under consideration?
128 o Can we differentiate security and privacy threats?
130 o What are common endpoint security capabilities?
132 o What would be an ideal endpoint security solution?
134 o What are the limits to endpoint security?
136 o What is real production data telling us?
138 o What can defence-in-depth help us with?
140 o What are the economic considerations?
142 o What are the regulatory considerations and constraints?
144 o What are the human rights considerations?
145 Our goal with this review is to describe the benefits and limitations
146 of endpoint security in the real world, rather than in the abstract.
147 We aim to highlight security limitations that cannot be addressed by
148 endpoint solutions and to suggest how these may be mitigated with the
149 concept of a defence-in-depth approach, in order to increase the
150 resilience against attacks and data breaches.
152 2. Abbreviations
154 In this section we provide main abbreviations expansions
156 ABAC Attribute Based Access Control
158 AI Artificial Intelligence
160 AMT Active Management Technology
162 C&C Command and Control
164 CFI Control Flow Integrity
166 CFG Control Flow Guard
168 DDoS Distributed Denial of Service
170 DEP Data Execution Prevention
172 DGA Domain Generating Algorithms
174 DLP Data Loss Prevention
176 DMARC Domain-based Message Authentication, Reporting and Conformance
178 DoS Denial of Service
180 EE Execution Environment
182 EDR Endpoint Detection and Response
184 EPP Endpoint Protection Platform
186 FP False Positive
188 HIPS Host Intrusion Prevention System
190 ICD Integrated Cyber Defence
192 ICMP Internet Control Message Protocol
193 IDS Intrusion Detection System
195 IoT Internet of Things
197 IPS Intrusion Prevention System
199 ML Machine Learning
201 MSS Managed Security Services
203 MSSP Managed Security Services Provider
205 NIST National Institute of Standards and Technology
207 NX No Execute Bit
209 P2P Peer to Peer
211 RAP Reuse Attack Protector
213 RBAC Role Based Access Control
215 RDP Remote Desktop Protocol
217 ROP Return Oriented Programming
219 SANS System Administration, Networking, and Security
221 SGX Software Guard eXtensions
223 SSH Secure SHell
225 UE User Equipment
227 UEFI Unified Extensible Firmware Interface
229 UX User Experience
231 VM Virtual Machine
233 XSS Cross Site Scripting
235 3. Definitions
237 In this section we provide definitions that are marked
239 o (L) Local to this document
240 o (G REFERENCE) Global and then will be preceded by a reference
242 DoS (L) Literally a Denial of Service. Not to be confused with a
243 Network DoS or DDoS.
245 Endpoint security capabilities (L) How to protect the endpoint with
246 three different aspects of protection:
248 o Prevention - The attack doesn't succeed by intrinsic or explicit
249 security capabilities.
251 o Detection - The attack is happening or has happened and is
252 recorded and/or signalled to another component for action.
254 o Mitigation - Once detected, the attack can be halted or its
255 effects can at least be reduced or reversed.
257 System (L) A system is a heterogeneous set of any IT capabilities
258 including hardware, software, endpoints (including IoT), networks,
259 data centers and platforms with no assumptions on deployment form
260 factor (physical, virtual, microservices), deployment scenario,
261 geographic distribution, or dispersion.
263 User Equipment (G ITU-T H.360) Equipment under the control of an End
264 User
266 4. Disclaimer
268 This document is a first draft and is incomplete on purpose. Indeed
269 there are several areas where there are different ways to develop
270 this draft and the authors are seeking for feedback and extended
271 collaboration. This is to be noted too, that this is the first draft
272 RFC for the authors and contributors, so, coaching and help will be
273 appreciated. Overall, 'a bon entendeur, salut'.
275 Comments are solicited and should be addressed to the authors.
277 5. Endpoints: definitions, models and scope
279 Endpoints are the origin and destination for a communication between
280 parties. This encompasses User Equipment (UE) and the Host at the
281 other end of the communication. Whilst it is recognized that these
282 two ends of the communication may represent a vast amount of diverse
283 endpoints, this document will set here a requirement for a uniform
284 way to describe the endpoints in order to work from an equally
285 uniform representation of what is called the Attack Surface. In the
286 same spirit as the IETF TEEP Working Group generalized its work, see
287 [TEEP], this document will rely on another document identified as
289 [I-D.draft-mcfadden-smart-endpoint-taxonomy-for-cless-00] in order to
290 represent the taxonomy of endpoints.
292 For example:
294 o The following would be considered as UEs: a smartphone, a smart
295 device, any IoT device, a laptop, a desktop, a workstation, etc.
297 o Hosts represent too, physical servers, virtual servers/machines,
298 etc.
300 We require a framework in order to define and model the endpoint
301 itself and the position of the endpoint in the network. In this
302 initial analysis we focus on endpoints that are User Equipment (UE)
303 rather than on hosts. In the future, with the help of
304 [I-D.draft-mcfadden-smart-endpoint-taxonomy-for-cless-00] we hope to
305 balance and unify the model.
307 In addition, we need two models for the endpoint, internally and in
308 an end-to-end context within the network. With this approach we
309 expect both models to help us cover all the attack surface and the
310 threat landscape and therefore help us list the capabilities and
311 limitations for endpoint security.
313 Indeed, this will help us understand point attacks versus composite
314 attacks within context, and, accordingly, understand holistically the
315 capabilities and the limitations of endpoint security. For example
316 to differentiate when only an application on the end point is
317 affected.
319 5.1. Internal representation of an endpoint
321 This section interfaces here with
322 [I-D.draft-mcfadden-smart-endpoint-taxonomy-for-cless-00] which
323 starts from the below internal representation of an endpoint which
324 could be generalized by the simple diagram below:
326 +----------------------------+
327 | Application |
328 +----------------------------+
329 | OS / Execution Environment |
330 +----------------------------+
331 | Hardware |
332 +----------------------------+
333 Today there are many combinations of Hardware, OS/EE pairing and
334 Application layers, offering the user a vast set of features with a
335 wide spectrum of capabilities.
337 Furthermore we can consider that an application running on a UE or a
338 host is an endpoint too, so we have multiple ways to read the above
339 diagram.
341 In essence we want to consider here endpoints including those which
342 have a variance in electrical power, computational power, memory,
343 disk, network interfaces, size, ownership, connectics, etc. and
344 therefore why we rely on
345 [I-D.draft-mcfadden-smart-endpoint-taxonomy-for-cless-00].
347 5.2. Endpoints modeled in an end-to-end context
349 A representation of endpoints in an end-to-end context could look
350 like the following diagram:
352 +-------+ +---------------------+---------+
353 | Human | <- (1) -> | Digital Persona | Application | <- (2) ->
354 +-------+ +-----------+-------------------+
355 | User Equipment |
356 +-------------------------------+
358 +----------------+ +----------------+
359 <- (2) -> | Network | <- (3) -> | Platform/Hosts |
360 | Infrastructure | +----------------+
361 +----------------+
363 1. Humans have a user experience (UX) with the UE, starting with an
364 explicit or implicit Digital Persona, engaging with an
365 application
367 2. The application will have sessions through a large Network
368 Infrastructure where we do not assume anything of the
369 infrastructure (could be landlines, mobile networks, satellites,
370 etc.) and those sessions reach
372 3. a Platform consisting of many Hosts either physical or virtual
373 and it ensures a large part of the end-to-end user experience.
375 In this end-to-end model we see that many other systems may have
376 interactions with the UE: the human, the UX, the digital persona, the
377 sessions, the intermediate network infrastructure, and the hosts and
378 application at the destination.
380 If we now look at security aspects of the above models, the threat
381 landscape is very large and the attack surface will cover all the
382 components and interactions at any level.
384 6. Threat Landscape
386 (Editor's note: this section will require a significant amount of
387 future development.)
389 Given the vast number of combinations that the above generic modeling
390 offers us, defining a threat landscape should be done carefully and
391 will require a systematic methodology.
393 Therefore this entire section will be developed through future
394 iterations of the document, in this initial version we will start
395 structuring an approach and then adjust this based on feedback.
397 There is no doubt that we want to cover typical known attacks such
398 as:
400 o Malware (Trojans, viruses, backdoors, bots, etc.)
402 o Adware and spyware
404 o Exploits
406 o Phishing
408 o Script based attacks
410 o Ransomware, local Denial of Service (DoS) attacks
412 o Denial of Service (DoS) attacks
414 o Malicious removable storage devices (USB)
416 o In memory attacks
418 o Rootkits and firmware attacks
420 o Scams and online fraud
422 o System abuse (staging/proxying)
423 o etc.
425 To illustrate the difficulty to define a good threat landscape, when
426 it comes to cryptojacking and coinmining that were on the rise, in
427 which category do they fall: malware? DoS? system abuse? or a
428 category on its own?
430 This is why we wanted to conduct a thorough gap analysis using
431 existing definitions and frameworks, but we couldn't find an existing
432 comprehensive and recognized taxonomy dedicated to the threat
433 landscape on endpoints. We found however different models in this
434 field, and have considered two. We are open to further suggestions.
436 Indeed both of the analysed frameworks contain threat landscape
437 descriptions:
439 o MITRE Common Attack Pattern Enumeration Classification (CAPEC).
440 See [CAPEC].
442 o MITRE ATT&CK. See [ATTACK].
444 These offer us interesting ways to assess the threat landscape:
446 o CAPEC offers a hierarchical view of attack patterns by domains
447 which can match some aspects of both of our above models, but we
448 will need to identify those attacks that fit exactly in our scope.
450 o ATT&CK offers a very straightforward categorized knowledge base of
451 attacks, but it concentrates on the entreprise attack chain, so we
452 will need to do some work to extract what we need.
454 We recognise however that these frameworks do not address all of the
455 threats that can affect the security of a system, for example they do
456 not cover; routing hijacking, flooding, selective blocking,
457 unauthorised modification of data sent to an endpoint, etc. Further
458 work to define categories of threats is therefore required.
460 As a further example, phishing should be included as an attack, but
461 whilst this is indeed an attack that will materialize on a device
462 through an application (email, webmail, etc.), the real target of
463 this attack is not the device, but the human behind the digital
464 persona.
466 Having a methodology of assessment is necessary here, because it will
467 help decide what is in scope vs. out of scope.
469 We are aware that once a method and the categories are fully defined
470 in this section, it will force a review of all the following sections
471 in the document. Whilst remapping will be necessary, it should not
472 drastically change the draft.
474 7. Endpoint Security Capabilities
476 In this section we try to define some endpoint security capabilities
477 (Editor's note: this section will require future development.)
479 In this version of the document we will start by developing a
480 framework to categorize and position endpoint security capabilities
481 with the goal of defining what an ideal endpoint security capability
482 would look like.
484 By endpoint security capabilities we mean how to protect the endpoint
485 against attacks. Protection has many meanings, we want to
486 distinguish three different aspects of protection:
488 o Prevention - The attack doesn't succeed by intrinsic or explicit
489 security capabilities.
491 o Detection - The attack is happening or has happened and is
492 recorded and/or signalled to another component for action.
494 o Mitigation - Once detected, the attack can be halted or its
495 effects can at least be reduced or reversed.
497 For example, prevention methods include keeping the software updated
498 and patching vulnerabilities, implementing measures to authenticate
499 the provenance of incoming data to stop the delivery of malicious
500 content, or choosing strong passwords. Detection methods include
501 inspecting logs or network traffic. Mitigation could include
502 deploying backups to recover from an attack with minimal disruption.
504 Our intention however is not just to consider each endpoint security
505 capability separately, but also the overall endpoint security
506 holistically with all its interdependencies. Indeed, we defined a
507 simple endpoint, but each layer may or may not have a certain
508 spectrum of intrinsic capabilities and there may be multiple ways to
509 provide add-on and third-party endpoint security capabilities,
510 allowing complex interactions between all of these components.
512 We define two different aspects of endpoint security capabilities and
513 their subdivisions as:
515 o (A) Intrinsic security capability can be built-into each of the
516 endpoint model layers
518 * (1) Hardware
519 * (2) OS/EE
521 * (3) Application
523 o (B) Add-on security capability can be
525 * (4) a component of the hardware
527 * (5) a component of the OS/EE
529 * (6) an application by itself
531 In (A) we relate to a 'security by design' intention of the
532 developers and they will intrinsically offer a security model and
533 security capabilities as part of their design. A typical example of
534 this is the authorization model.
536 In (B) a 3rd party is offering an additional security component which
537 was not necessarily considered when the Hardware, OS/EE or
538 Application were designed.
540 In the future we will review all the main categories of security
541 capabilities that are known to date and assess security capability
542 enablers like Artificial Intelligence (AI) and Machine Learning (ML).
543 For each category we will try to give a review on how effective the
544 capability is in securing the system.
546 With regard to (6), there are many available options for add-on
547 security capabilities offered by third-parties as applications on a
548 commercial or open-source basis. Gartner (see [GARTNERREPORT])
549 highlights the evolution of endpoint security towards two directions
550 as shown in [EPPEDR], [EPPSECURITY], [EPPGUIDE].
552 o Endpoint Protection Platform (EPP) as an integrated security
553 solution designed to detect and block threats at the device level.
555 o Endpoint Detection and Response (EDR) as a combination of next
556 generation tools to provide anomaly detection and alerting,
557 forensic analysis and endpoint remediation capabilities.
559 Among the security capabilities that we list, the endpoint can
560 perform the following:
562 o Intrinsic
564 * Software updates / patching
566 * Access Control (RBAC, ABAC, etc.)
567 * Authentication
569 * Authorization
571 * Detailed event logging
573 o Execution protection
575 * Exploit mitigation (file/memory)
577 * Tamper protection
579 * Whitelisting filter by signatures, signed code or other means
581 * System hardening and lockdown (HIPS, trusted boot, etc.)
583 o Malware protection
585 * Scanning - on access/on write/scheduled/quick scan (file/
586 memory)
588 * Reputation-based blocking on files or by ML
590 * Behavior-based detection - (heuristic based/ML)
592 * Rootkit and firmware detection
594 * Threat intelligence based detection (cloud-based/on premise)
596 * Static detection - generic, by emulation, by ML, by signature
598 o Attack/Exploit/Application Protection
600 * Application protection (browser, messaging clients, social
601 media, etc.)
603 + Disinformation Protection (anti-phishing, fake news, anti-
604 spam, etc.)
606 + Detection of unintended link location (URL blocklist, etc.)
608 + Memory exploit mitigation, e.g. browsers
610 * Network Protection (local firewall, IDS, IPS and local proxy)
611 inbound and outbound
613 * Detection of network manipulation (ARP, DNS, etc.)
614 * Data Loss Prevention and exfiltration detection (incl. covert
615 channels)
617 8. What would be a perfect endpoint security solution?
619 With all the above knowledge, let's consider what we could expect
620 from a perfect endpoint security 'system'. It would:
622 o find instantly accurate reputation for any file before it gets
623 executed and block it if needed.
625 o monitor any behavior on the endpoint, including inbound and
626 outbound network traffic, learn and identify normal behavior and
627 detect and block malicious actions, even if the attack is misusing
628 legitimate clean system tools or hiding with a rootkit.
630 o patch instantly across all devices/systems/OSes, including virtual
631 patching, meaning you can patch or shield an application even
632 before an official patch is released.
634 o exploit protection methods for all processes where applicable,
635 e.g. no execute bit (NX), data execution prevention (DEP), address
636 space layout randomization (ASLR), Control Flow Integrity Guard
637 (CFI/CFG), stack canaries, shadow stack, reuse attack protection
638 (RAP), etc. all of which are methods, which make it very difficult
639 to successfully run any exploit, even for zero day
640 vulnerabilities.
642 o detect attempts to re-route data to addresses other than those
643 which the user intended, e.g. detect incorrectly served DNS
644 entries, TLS connections to sites with invalid certificates, data
645 that is being proxied without explicit user consent, etc.
647 o have an emulator/sandbox/micro virtualization to execute code and
648 analyse the outcome and perform a roll back of all actions if
649 needed, e.g. for ransomware.
651 o allow the endpoint to communicate with the other endpoints in the
652 local network and globally, to learn from 'the crowd' and
653 dynamically update rules based on its findings.
655 o be in constant sync with all other endpoints deployed on a network
656 and other security solutions, run on any OS, with no delay
657 (including offline modes and on legacy systems).
659 o run from the OS/EE when possible.
661 o run as one of the first process on the OS/EE and protect itself
662 from any form of unwanted tampering.
664 o offers a reliable logging that can't be tampered with, even in the
665 event of system compromise.
667 o receive updates instantly from a trusted central entity.
669 9. The defence-in-depth principle
671 In this section we give a high level view of what we mean by
672 'defence-in-depth'.
674 Whilst endpoint security systems have good capabilities, sometimes it
675 is debatable and perhaps suboptimal to let the endpoint run the
676 capability alone or at all. It is generally considered good security
677 practice to adopt a defence-in-depth approach (see [USCERT]). The
678 Open Web Application Security Project group (OWASP) describes the
679 concept as follows: "The principle of defense-in-depth is that
680 layered security mechanisms increase security of the system as a
681 whole. If an attack causes one security mechanism to fail, other
682 mechanisms may still provide the necessary security to protect the
683 system." (see [OWASP])
685 Indeed there are many other constituencies as per our end-to-end
686 model that can participate in the defence process: The network, the
687 infrastructure itself, the platform, the human, the user experience
688 and in a hybrid of an on premise and cloud approach, an Integrated
689 Cyber Defence (ICD) of the entire chain.
691 The simple idea behind the concept is that "every little helps". If
692 the endpoint is not 100% secure itself, the detection chance can
693 increase with additional security capabilities from other entities.
694 We acknowledge that there are some case where adding an additional
695 component to the system may degrade the overall security level by
696 introducing new weaknesses.
698 There are various reference article in the industry highlighting
699 limitations of endpoint only solutions. For example this quote here,
700 which talks about multi-tier solutions: "There are limitations with
701 any endpoint protection solution, however, that can limit protection
702 to only the client layer. There is also a need for security above
703 the client layer, as endpoint protection products cannot intercept
704 traffic. Vendors will often sell a multi-tiered solution that
705 enables a network appliance to assist the endpoint protection client
706 by intercepting traffic between the attacker and the infected client.
707 Vendors will also sell solutions that monitor and intercept traffic
708 on internal or external network segments to protect the enterprise
709 from these threats. A prime example of the limitations of endpoint
710 protection software is infection via a phishing attack." [ADAPTURE].
712 Some sources point out that even the best solution might not get
713 deployed in the optimal way in a real world scenario as the
714 environment can be very complex: "While endpoint security has
715 improved significantly with the introduction of application
716 whitelisting and other technologies, our systems and devices are
717 simply too diverse and too interconnected to ensure that host
718 security can be deployed 100% ubiquitously and 100% effectively."
719 [NETTODAY]
721 On these grounds it is considered a good idea to follow a layered
722 approach when it comes to security. "In today's complex threat
723 environment, companies need to adopt a comprehensive, layered
724 approach to security, which is a challenging task in such as rapidly
725 evolving, crowded market." [HSTODAY]
727 It is important to comprehend the capabilities of endpoint security
728 solutions in this overall picture of the connected environment, which
729 includes other systems, networks and various protocols that are used
730 to interact with these entities. Understanding possible shortcomings
731 from single layered solutions can help counterbalance such weaknesses
732 in the architectural concept or the protocol design.
734 In order to quantify any potential benefits or limitations of the
735 various layered scenarios in regards to security a solid data set is
736 needed. This section requires statistics about proportions of
737 attacks that go undetected in various cases. We propose analysing
738 data for the following four cases:
740 o There is no security solution
742 o Security is only on the endpoint
744 o Security is only on the network
746 o Security is on both the endpoint and the network
748 However reconciling various statistics requires a lot of caution and
749 time, a methodology and consistent classification to avoid any
750 misinterpretation.
752 10. Endpoint Security Limits
754 The previous section defines an ideal endpoint security 'system',
755 however, from the real world, the expectation of what we can get from
756 an endpoint security solution will look more along the following
757 lines:
759 o may not be able to run at full capacity due to computational power
760 limits, battery life, performance, or policies (such as BYOD
761 restrictions in enterprise networks), etc.
763 o may not be able to run at full capacity as it slows down
764 performance too much.
766 o will miss some of the malware or attacks, regardless of detection
767 method used, like signatures, heuristics, machine learning (ML),
768 artificial intelligence (AI), etc.
770 o have some level of False Positives (FP).
772 o not monitoring or logging all activities on the system, e.g. due
773 to constraints of disk space or when a clean windows tool is being
774 triggered to do something malicious but the activity is not
775 logged. Such activity can be logged, but a decision needs to be
776 made if it's clean or not.
778 o have its own vulnerabilities or simple instabilities that could be
779 used to compromise the system.
781 o be tampered with by the user, e.g. disabled or reconfigured.
783 o be tampered with by the attacker, e.g. exceptions added or log
784 files wiped.
786 In the section below we review a number of these limitations through
787 real examples, step by step. Some limitations are absolute, and some
788 limitations result in a grey area or suboptimality for the solution.
790 10.1. No possibility to put an endpoint security add-on on the UE
792 UEs will vary a lot; by 2022, an estimated 29 billion devices will be
793 connected, with 18 billion of them related to IoT [ERICSSON]. Many
794 IoT products lack the capacity to install any endpoint security
795 capabilities, are unable to update the software, and it is not
796 possible to force the UE provider to improve or even offer an
797 intrinsic security capability.
799 We acknowledge that the numbers do vary significantly depending on
800 the source, for example:
802 o [STATISTA1] is showing the current trajectory of IoT devices from
803 25B to date to 40+B in 2022 and 75B in 2025.
805 o [ERICSSON] is more conservative and might requires an update, but
806 it was reaching 29B devices in 2022, with a nice breakdown between
807 device types and connectivity.
809 o [STATISTA2] is showing a breakdown by verticals and is even more
810 conservative than both of the above.
812 o [ENISA] it refers to a [GARTNERIOT] report from 2017 which sets a
813 trajectory to 20B devices by 2020.
815 In IoT we find UEs such as medical devices which are limited by
816 regulation, welding robots that can't be slowed down, smart light
817 bulbs which are limited by the processing power, etc. There are many
818 factors influencing whether endpoint security can be added to a UE:
820 o The UE is simply not powerful enough or the performance hit is too
821 high.
823 o Adding your own security will breach the warranty or will
824 invalidate a certification or a regulation (breach of validity).
826 o The UE needs to run in real-time and any delay introduced by a
827 security process might break the process.
829 o Some UEs are simply locked by design and the manufacturer does not
830 provide a security solution (e.g. smart TV, fitness tracker or
831 personal artificial assistants) see [CANDID1], [CANDID2].
833 In the future, a possible research problem would be to find hard data
834 on the exact proportion of IoT devices that are unable to run any
835 endpoint security add-on or that have no intrinsic security built-in.
837 The other hidden dimension here is the economical aspect. Many
838 manufacturer are reluctant to invest in IoT device security, because
839 it can significantly increases the cost of their solution and there
840 is the perception that they will lose market shares, as customers are
841 not prepared to pay the extra cost for added security.
843 10.1.1. Not receiving any updates or functioning patches
845 The endpoint security system may lack a built-in capability to be
846 patched or it may be connected to a network that prevents the process
847 of downloading updates automatically. For example stand-alone
848 medical systems or industrial systems in isolated network segments
849 often do not have a communication channel to the Internet.
851 Even if security updates are received, they typically will only be
852 periodically updated; hence there will be a window of opportunity for
853 an attacker, between the time the attack is first used, and the time
854 the attack is discovered/patched and the patch is deployed.
856 In addition updates and patches may themselves be malicious by
857 mistake, or on purpose if not properly authenticated, or if the
858 source of the updates has malicious intent. This could be part of a
859 software update supply chain attack or an elaborate attacker breaking
860 the update process, as for example seen with the Flamer group (see
861 [FLAMER]).
863 A recent survey found that fewer than 10% of consumer IoT companies
864 follow vulnerability disclosure guidelines at all, which is regarded
865 as a basic first step in patching vulnerabilities (see
866 [IOTPATCHING]). This indicates that many IoT devices do not have a
867 defined update process or may not even create patches for most of the
868 vulnerabilities.
870 Furthermore some endpoints system may reach the end of their support
871 period and therefore no longer receive any updates for the OS/EE or
872 the security solution due to missing licenses. However the systems
873 may remain in use and become increasingly vulnerable as time goes on
874 and new attacks are discovered.
876 10.1.2. Mirai IoT bot
878 Editor's note: we are going to experiment a new model to represent
879 examples showing the limits of endpoint security solutions therefore
880 the first table is the old format and the new table prepares the new
881 format so that we ca develop 2 new I-Ds one for endpoint model in
882 terms of attack surface and the other one in terms of attack
883 landscape and potential attack orchestrations. In this I-D we will
884 glue all the dots and describe the defense orchestration, yet, based
885 on a normative approach to terminology used so that we don't need to
886 describe it here
887 +-------------+-----------------------------------------------------+
888 | Description | A Mirai bot infecting various IoT devices through |
889 | | weak passwords over Telnet port TCP 23 and by using |
890 | | various vulnerabilities, for example the SonicWall |
891 | | GMS XML-RPC Remote Code Execution Vulnerability |
892 | | (CVE-2018-9866) on TCP port 21009. Once a device is |
893 | | compromised it will scan for further victims and |
894 | | then start a DoS attack. |
895 +-------------+-----------------------------------------------------+
896 | Simplified | Compromised device scans network for multiple open |
897 | attack | ports, attempts infection through weak password and |
898 | process | exploits, downloads more payload, starts DoS |
899 | | attack. |
900 | | |
901 | UE | No security tool present on majority of IoT |
902 | | devices, hence no detection possible. If a |
903 | | rudimentary security solution with limited |
904 | | capabilities such as outgoing firewall is present |
905 | | on the IoT device e.g. router, then it might be |
906 | | able to detect the outbound DoS attack and slow it |
907 | | down. |
908 | | |
909 | References | [MIRAI1][MIRAI2] |
910 +-------------+-----------------------------------------------------+
911 +---------------+---------------------------------------------------+
912 | Name | Mirai |
913 +---------------+---------------------------------------------------+
914 | Description | A device infection for participation into a |
915 | | botnet activity |
916 | | |
917 | Endpoint | IoT Devices |
918 | Targeted | |
919 | | |
920 | Attack | Telnet remote access; Weak default and existing |
921 | Surface | passwords; Code vulnerabilities in exposed |
922 | Categories | services |
923 | Involved | |
924 | | |
925 | Attack | Weak passwords over Telnet TCP port 23; SonicWall |
926 | Surface | GMS XML-RPC Remote Code Execution Vulnerability |
927 | Examples | (CVE-2018-9866) on TCP port 21009 |
928 | | |
929 | Attack | Deployment of a custom code or commands on the |
930 | Objective | device for participation in botnet activities |
931 | | |
932 | Attack | Botnet Deployment; DDoS |
933 | Category | |
934 | | |
935 | Attack | Exploit remote access weaknesses on the device to |
936 | Orchestration | deploy a bot on the device |
937 | | |
938 | Mitigation | If a rudimentary security solution with limited |
939 | | capabilities such as outgoing firewall is present |
940 | | on the IoT device e.g. router, then it might be |
941 | | able to detect the added bot or the outbound DoS |
942 | | attack and slow it down |
943 | | |
944 | Attack | Better password management; Uptodate patching |
945 | Surface | |
946 | Minimisation | |
947 | | |
948 | References | [MIRAI1][MIRAI2] |
949 +---------------+---------------------------------------------------+
951 10.2. Endpoints may not see the malware on the endpoint
953 10.2.1. LoJax UEFI rootkit
954 +-------------+-----------------------------------------------------+
955 | Description | A device compromised with the LoJax UEFI rootkit, |
956 | | which is active before the OS/EE is started, hence |
957 | | before the endpoint security is active. It can pass |
958 | | back a clean 'image' when the security solution |
959 | | tries to scan the UEFI. Infection can either happen |
960 | | offline with physical access or through a dropper |
961 | | malware from the OS/EE. |
962 +-------------+-----------------------------------------------------+
963 | UE | A perfect endpoint security could potentially |
964 | | detect the installation process if it is done from |
965 | | the OS/EE and not with physical modification or in |
966 | | the factory. Once the device is compromised the |
967 | | endpoint security solution can neither detect nor |
968 | | remove the rootkit. The endpoint solution may |
969 | | detect any of the exhibited behaviour, for example |
970 | | if the rootkit drops another malware onto the OS/EE |
971 | | at a later stage. |
972 | | |
973 | Reference | [LOJAX] |
974 +-------------+-----------------------------------------------------+
976 10.2.2. SGX Malware
978 +-------------+-----------------------------------------------------+
979 | Description | Malware can hide in the Intel Software Guard |
980 | | eXtensions (SGX) enclave chip feature. This is a |
981 | | hardware-isolated section of the CPU's processing |
982 | | memory. Code running inside the SGX can use return- |
983 | | oriented programming (ROP) to perform malicious |
984 | | actions. |
985 +-------------+-----------------------------------------------------+
986 | UE | Since the SGX feature is by design out of reach for |
987 | | the OS/EE, an endpoint security solution can |
988 | | neither detect nor remove any injected malware. A |
989 | | perfect endpoint security solution could |
990 | | potentially detect the installation process if it |
991 | | is done from the OS/EE and not with physical |
992 | | modification or in the factory. |
993 | | |
994 | References | [SGX1] [SGX2] |
995 +-------------+-----------------------------------------------------+
997 10.2.3. AMT Takeover
998 +-------------+-----------------------------------------------------+
999 | Description | A targeted attack group can remotely execute code |
1000 | | on a system through the Intel AMT (Active |
1001 | | Management Technology) vulnerability |
1002 | | (CVE-2017-5689) over TCP ports 16992/16993. This |
1003 | | provides full access to the computer, including |
1004 | | remote keyboard and monitor access. The attacker |
1005 | | can install malware, modify the system or steal |
1006 | | information. |
1007 +-------------+-----------------------------------------------------+
1008 | UE | The AMT is accessible even if the PC is turned off. |
1009 | | Therefore any endpoint security software installed |
1010 | | on the OS, would not be able to see this traffic |
1011 | | and therefore also not able to detect it. |
1012 | | |
1013 | References | [AMT1], [AMT2] |
1014 +-------------+-----------------------------------------------------+
1016 10.2.4. AMT case study (anonymised)
1018 An enterprise has a data center containing very sensitive data.
1019 Their workstations use a certain Intel chipset which integrates the
1020 AMT feature for remote computer maintenance. AMT is an interface for
1021 hardware management of the workstations, including transmission of
1022 screen content and keyboard and mouse input for remote maintenance.
1023 Communication with the management workstation is implemented by AMT
1024 through the network interface card (NIC) on the motherboard. The
1025 network packets generated in this way are invisible both to the main
1026 processor and thus to the OS running on the workstation. In autumn
1027 of 2015, it became known that some AMT-enabled computers had a flaw
1028 that allowed AMT's remote maintenance component to be activated and
1029 configured by attackers. This also worked when the workstations were
1030 switched off. The leakage of data through this vulnerability is
1031 elusive and difficult to detect. The identified threat situation led
1032 the organization to a new requirement implementing a method that can
1033 reliably detect this and similar vulnerabilities. In particular, the
1034 detection of rootkits and manipulated firmware, and this includes
1035 also (UEFI) BIOS - has also been a focus of their attention.
1037 The method used as a solution, compares the desired data packets
1038 generated by a client operating system - the user, with the data
1039 packets received on the switch port. If more data has been received
1040 on the switch port than was been sent by the operating system - the
1041 user, there is a strong possibility that something bad is happening -
1042 like for example an infection via modified firmware or by rootkit.
1044 10.2.5. Users bypass the endpoint security
1046 +-------------+-----------------------------------------------------+
1047 | Description | Endpoint security systems should not interfere with |
1048 | | the normal operation of the endpoint to the extent |
1049 | | that users become frustrated and want to disable |
1050 | | them or configure them to disable a significant |
1051 | | fraction of important security capabilities. |
1052 +-------------+-----------------------------------------------------+
1053 | UE | Add-on endpoint security is now bypassed or |
1054 | | disabled by the user. Unless the endpoint is under |
1055 | | monitored management or can prevent a user from |
1056 | | modifying the configuration, then this is shutting |
1057 | | down a significant fraction of the security |
1058 | | capabilities. |
1059 | | |
1060 | References | [NINESIGNS] |
1061 +-------------+-----------------------------------------------------+
1063 10.3. Endpoints may miss information leakage attacks
1065 Another aspect that endpoint security has issues in detecting are
1066 information disclosure or leakage attacks, especially on shared
1067 virtual/physical systems.
1069 10.3.1. Meltdown/Specter
1071 The Meltdown/Specter vulnerabilities and all its variants may allow
1072 reading of physical memory belonging to another virtual machine (VM)
1073 on the same physical system. This could reveal passwords,
1074 credentials, certificates etc. The trick is that an attacker can
1075 spin up his own VM on the same physical hardware. As this VM is
1076 controlled by the attacker, they will ensure that there is no
1077 endpoint security that detects the Meltdown exploit code when run.
1078 It is very difficult for the attacked VM to detect the memory read-
1079 outs. For know CPU vulnerabilities there are software patches
1080 available than can be applied. If it is an external service
1081 provider, it might not be in the power of the user to patch the
1082 physical system or to determine if this has been done by the
1083 provider.
1085 10.3.2. Network daemon exploits
1087 Other attack types, which leak memory data from a vulnerable web
1088 server, are quite difficult to detect for an endpoint security. For
1089 example the Heartbleed bug allows anyone on the Internet to read the
1090 memory of the systems protected by the vulnerable versions of the
1091 OpenSSL software. This could lead to credentials or keys being
1092 exposed. An endpoint solution needs to either patch the vulnerable
1093 application or monitor it for any signs of exploitation or data
1094 leakage and prevent the data from being exfiltrated.
1096 10.3.3. SQL injection attacks
1098 A SQL injection attack is an example of an attack that exploits the
1099 backend logic of an application. Typically this is a web application
1100 with access to a database. By encoding specific command characters
1101 into the query string, additional SQL commands can be triggered. A
1102 successful attack can lead to the content of the whole database being
1103 exposed to the attacker. There are other similar attacks that can be
1104 grouped together for the purpose of this task, such as command
1105 injection or cross site scripting (XSS). Although they are different
1106 attacks, they all at their core fail at input filtering and
1107 validation, leading to unwanted actions being performed.
1109 Applications that are vulnerable to SQL injections are very common
1110 and are not restricted to web applications. An endpoint solution
1111 needs to monitor all data entered into possible vulnerable
1112 applications. This should include data received from the network. A
1113 generic pattern matching for standard SQL injection attack strings
1114 can be applied to potentially block some of the attacks. In order to
1115 block all types of SQL injection attacks the endpoint solution should
1116 have some knowledge about the logic of the monitored application,
1117 which helps to determine how normal requests differ from attacks.
1118 Applications can be analysed at source code level for potential
1119 weaknesses, but dynamically patching is very difficult. See [SQL]
1121 10.3.4. Low and slow data exfiltration
1123 An endpoint security solution can detect low and slow data
1124 exfiltration, for example when interesting data sources are tracked
1125 and access to them is monitored. If the data source is not on the
1126 endpoint itself, e.g. a database in the network, then the received
1127 data needs to be tagged and its further use needs to be tracked. To
1128 make detection difficult, an attacker could decide to use an
1129 exfiltration process that sends only 10 bytes every Sunday to a
1130 legitimate cloud service. If that is not in the normal behavior
1131 pattern, then this anomaly could be detected by the endpoint. If the
1132 process that sends the data or the destination IP address have a bad
1133 reputation, then they could be stopped. Though it is very difficult
1134 to reliably block such an attack and most solutions have a specific
1135 threshold that needs to be exceeded before it is detected as an
1136 anomaly.
1138 10.4. Suboptimality and gray areas
1140 10.4.1. Stolen credentials
1142 Stolen credentials and misuse of system tools such as RDP, Telnet or
1143 SSH are a valid scenario during attacks. An attacker can use stolen
1144 credentials to remotely log into a system and access data or execute
1145 commands in this context like the legitimate user might do. An
1146 endpoint security solution can restrict access from specific IP
1147 addresses, but this is difficult in a dynamic environment and when an
1148 attacker might have already compromised a trusted device and misuse
1149 it as a stepping stone for lateral movement. The endpoint could
1150 perform additional checks of the source device, such as verifying
1151 installed applications and certain conditions. Again this will not
1152 work in all scenarios, e.g. a hijacked valid device during lateral
1153 movement.
1155 This means that the system will not be able to simply block the
1156 connection if the authentication with the stolen credentials
1157 succeeds. A multi factor authentication (MFA) could limit the use of
1158 stolen credentials, but depending on the system used and the
1159 determination of the attacker they might be able to bypass this
1160 hurdle as well e.g. cloning a SIM card to read text message codes.
1162 As a next step, a solution on the endpoint can monitor the behavior
1163 of the logged in user and determine if it represents expected normal
1164 behavior. Unfortunately, there is the chance for false positives
1165 that might block legitimate actions, hence the rules are usually not
1166 applied too tightly. The system can monitor for suspicious behavior,
1167 similar to malware detection, where every action is carefully
1168 analyzed and all activity is tracked. For example if the SSH user is
1169 adding all files to archives with passwords and then deletes the
1170 original files in the file explorer, then this could result in a
1171 ransomware case scenario. If only a few files are processed per
1172 hour, then this activity will be very difficult for the endpoint to
1173 distinguish from normal activity, in order to flag it as malicious.
1175 The problem of attackers blending in with normal activity is one of
1176 the biggest challenges with so called living off the land attack
1177 methods. The attacker chooses to keep their profile low by not
1178 installing any additional binary files on the system, but instead
1179 misuses legitimate system tools to carry out their malicious intent.
1180 This means that there is no malware file that could be identified and
1181 the detection relies solely on other methods such as behaviour based
1182 monitoring [LOTLSYMC].
1184 If information is shared across multiple endpoints, then each one
1185 could learn from the others and see how many connections came in from
1186 that source, what files were involved and what behavior the clients
1187 exhibited. This crowd wisdom approach would allow blocking rules to
1188 be applied after the first incident across multiple endpoints.
1190 10.4.2. Zero Day Vulnerability
1192 +-------------+-----------------------------------------------------+
1193 | Description | An attacker exploits a zero day vulnerability or |
1194 | | any recent vulnerability. |
1195 +-------------+-----------------------------------------------------+
1196 | UE | In theory this scenario could be handled by the |
1197 | | endpoint security: a) Once the intrinsic security |
1198 | | system has been patched, exploitation of the |
1199 | | vulnerability can be prevented. b) The add-on |
1200 | | security with enhanced capabilities or updated |
1201 | | methods can detect and mitigate the vulnerability. |
1202 | | It does not necessarily require the official patch. |
1203 | | |
1204 | Challenge | In practice many systems remain vulnerable to a |
1205 | | vulnerability months or even years after a security |
1206 | | fix has been released. Moreover there is a big gap |
1207 | | between when a vulnerability is disclosed and when |
1208 | | a security fix is available. Also there is a big |
1209 | | gap between when a security fix is available and |
1210 | | when the security fix is actually applied. A recent |
1211 | | study over three years, examined the patching time |
1212 | | of 12 client-side and 112 server-side applications |
1213 | | in enterprise hosts and servers. It took over 6 |
1214 | | months on average to patch 90% of the population |
1215 | | across all vulnerabilities. [NDSSPATCH]. We note |
1216 | | too: "The patching of servers is overall much worse |
1217 | | than the patching of client applications. On |
1218 | | average a server application remains vulnerable for |
1219 | | 7.5 months." |
1220 | | |
1221 | References | [ZERODAY1][ZERODAY2] |
1222 +-------------+-----------------------------------------------------+
1224 10.4.3. Port scan over the network
1226 An infected machine, let's say a Mirai bot on a router, is scanning a
1227 class B network for IP addresses with TCP port 80 open. The malware
1228 can slow it down to 1 IP address per 5 seconds (or any other
1229 threshold) and it can go in randomized order (like for example the
1230 nmap tool does) in order to make it difficult to find a sequential
1231 pattern. To increase detection difficulties, legitimate requests to
1232 existing web servers can be added in at random intervals.
1234 An endpoint solution might be able to detect this behaviour,
1235 depending on the threshold, but it will be difficult. At some point
1236 the pattern will be similar to browsing the web, so either the
1237 endpoint blocks the bot scanning and also the user from surfing, or
1238 it allows both.
1240 To make it even harder, the attacker can use a botnet that
1241 communicates over peer-to-peer (P2P) or a central command and control
1242 server (C&C) and then distribute the scan load over multiple hosts.
1243 This means each endpoint only scans a subset, let's say 100 IP
1244 addresses, but all 1,000 bots scan a total of 100,000 IP addresses.
1246 This attack is difficult to detect by a reasonable threshold on each
1247 endpoint individually. If the endpoints talk to each other and
1248 exchange information, then a collective decision can be made on the
1249 bigger picture of the bot traffic.
1251 Another option for the endpoint solution is to block the bot malware
1252 from operating on the computer, for example by detecting the
1253 installation, analyzing the behavior of the process or by preventing
1254 the binary from accessing the network. This includes blocking any
1255 form of communication for the process to its C&C server, regardless
1256 of if it is using a P2P network or misusing legitimate system tools
1257 or browsers to communicate with the Internet. Blocking indirect
1258 communication over system tools as part of living off the land
1259 tactics, can be very challenging.
1261 See [BOT]
1263 10.4.4. DDoS attacks
1265 For this example let us consider a botnet of 100,000 compromised
1266 computers and each one sends a burst of traffic to a remote target,
1267 for one second each, alternating in groups. This will generate some
1268 waves of pulse attack traffic. Similar comments can be made about
1269 overall pulsed DDoS attacks [PDDoS].
1271 A solution on the endpoint can attempt to detect the outgoing
1272 traffic. If the DoS attack is volume based and the time span of each
1273 pulse is large enough or the repeating frequency for each bot is
1274 high, then detection with thresholds on the endpoint is feasible. It
1275 is different, if it is an application layer DoS attack, where the
1276 logic of the receiving application is targeted, for example with too
1277 many search queries in HTTP GET requests. This would flood the
1278 backend server with intensive search requests, which can result in
1279 the web site no longer being responsive. Such attacks can succeed
1280 with a low amount of requests being sent, especially if its
1281 distributed over a botnet. This makes it very difficult for a single
1282 endpoint to detect such an ongoing attack, without knowledge from
1283 other endpoints or the network.
1285 Another option for the endpoint solution is to block the bot malware
1286 from operating on the computer, for example by detection the
1287 installation, analyzing the behavior of the process or by preventing
1288 the binary from accessing the network. This includes blocking any
1289 form of communication for the process to its C&C server, regardless
1290 of if it is using a P2P network or misusing legitimate system tools
1291 or browsers to communicate with the Internet. Blocking indirect
1292 communication over system tools as part of living off the land
1293 tactics, can be very challenging.
1295 11. Learnings from production data
1297 From the above limited considerations we can now check what we see
1298 from real production data using
1300 o the method described in [MONEYBALL]
1302 o the anonymised production data of Symantec MSS production for the
1303 past 3 months
1305 The core idea is to consider, based on all the imperfections we
1306 started to list above including the 'grey areas', that cybersecurity
1307 analysts are often presented with suspicious machine activity that
1308 does not conclusively indicate a compromise, resulting in undetected
1309 incidents or costly investigations into the most appropriate remedial
1310 actions.
1312 As Managed Security Services Providers (MSSP's) are confronted with
1313 these data quality issues, but also possess a wealth of cross-product
1314 security data that enables innovative solutions, we decided to use
1315 the Symantec MSS service for the past 3 months. The Symantec MSS
1316 service monitors over 100 security products from a wide variety of
1317 security vendors for hundreds of enterprise class customers from all
1318 verticals.
1320 We selected the subset of customers using the service that deploy
1321 both network and endpoint security products to determine which types
1322 of security incidents were most likely to be detected by endpoint
1323 products vs. network products. In doing so, we were particularly
1324 interested in identifying which categories of incidents are detected
1325 by endpoint products and not network products, and vice versa. Thus,
1326 we examined prevalent categories of incidents for which the only
1327 actionable security alerts were predominantly produced by one type of
1328 security product and not the other. To do so, we extracted all
1329 security incidents detected by Symantec MSS on behalf of hundreds of
1330 customers that deploy both network and endpoint security products,
1331 over a three-month period from December 2018 through the end of
1332 February 2019. We acknowledge that some attacks might have been
1333 blocked by the first product and therefore have never been seen by
1334 the next security solution, which influences the final numbers.
1336 With this in mind, we could identify incidents based on:
1338 +------------+------------------------------------------------------+
1339 | Severity | 4 - Emergency, 3 - Critical, 2 - Warning, 1 - |
1340 | | Informational |
1341 +------------+------------------------------------------------------+
1342 | Incident | Malicious Code, Deception Activity, Improper Usage, |
1343 | Category | Investigation, etc. |
1344 | | |
1345 | Incident | Trojan Horse Infection, Suspicious DGA Activity, |
1346 | Type | Suspicious Traffic, Suspicious URL Activity, |
1347 | | Backdoor infection, etc. |
1348 | | |
1349 | # network | Amount of network only security incidents |
1350 | incidents | |
1351 | | |
1352 | # all | What is the total amount of incidents on all |
1353 | incidents | security solutions |
1354 | | |
1355 | Percentage | Percentage of network security only incidents |
1356 +------------+------------------------------------------------------+
1358 We ended up with
1360 o Hundreds of thousands of security incidents
1362 o which we could categorize in 275 incident types by category and
1363 severity (triplets Severity-Category-Type)
1365 o out of which we searched how many incidents of each type were
1366 detected by a network security product and missed by deployed
1367 endpoint security products at least 75% of the time or vice versa
1369 11.1. Endpoint only incidents
1371 The categories of incidents that are detected primarily by endpoint
1372 security products are fairly intuitive. They consist primarily of
1373 detections of file-based threats and detection of malicious behaviors
1374 through monitoring of system and network behavior at the process
1375 level. The most prevalent of these behavioral detections include
1376 detections of suspicious URLs based on heuristics and blacklists of
1377 IP addresses or domain names. Since most of these alerts are not
1378 corroborated by network products, it seems probable that the
1379 blacklists associated with network products tend to be more focused
1380 on attacks while host-based intrusion prevention system alerts focus
1381 more on malware command and control traffic. Most other behavioral
1382 detections at the endpoint provide alerts based on system behavior
1383 that is deemed dangerous and symptomatic of malicious intent by a
1384 malicious or infected process. The highest severity incidents
1385 detected on endpoints are instances of post-compromise outbound
1386 network behavior that are symptomatic of command and control
1387 communications traffic, but these did not show up as being primarily
1388 detected by endpoint products as they are frequently corroborated by
1389 network-based alerts.
1391 11.2. Security incidents detected primarily by network security
1392 products
1394 Perhaps less intuitive are the results of examining categories of
1395 security incidents that are detected primarily by network security
1396 products and only rarely corroborated by endpoint security products.
1397 Below we provide details regarding incident categories for which a
1398 network security product produced a detection and for which there
1399 were no actionable endpoint alerts for at least 75% of the incidents
1400 in the category.
1402 In our study we found 32 incident type, category, and severity
1403 triplets of this type. The following categories critical incident
1404 types were reported by MSS customers, and we discuss each in turn in
1405 decreasing order of prevalence:
1407 11.2.1. Unauthorized external vulnerability scans
1409 Perhaps unsurprisingly, unauthorized external attempts to scan
1410 corporate resources for vulnerabilities and other purposes are
1411 detected in large volumes by a broad variety of network-focused
1412 security products. 79% of incidents of this type were detected by
1413 network security products with critical-severity alerts, these
1414 security incident detections are not accompanied by any actionable
1415 endpoint alerts, despite the fact that endpoint security products are
1416 deployed by these enterprises. This category of threats encompasses
1417 a broad variety of attacks, the most prevalent of which are the
1418 following: Horizontal scans, SQL injection attacks, password
1419 disclosure vulnerabilities, directory traversal attacks, and
1420 blacklist hits. Of these categories of detections, horizontal scans
1421 stand out as the category of detection that endpoint-security
1422 products are least likely to detect on their own.
1424 11.2.2. Unauthorized internal vulnerability scans
1426 Unauthorized internal vulnerability scans, though less frequent, are
1427 more alarming, as they are likely to represent possible post-
1428 compromise activity. We note that the Managed Security Service works
1429 with its customers to maintain lists of devices that are authorized
1430 to perform internal vulnerability scans, and their activity is
1431 reported separately at a lower levels of incident severity. 89% of
1432 detected unauthorized internal vulnerability scans are detected by
1433 network products without any corroborating actionable alerts from
1434 endpoint security products. As compared to unauthorized external
1435 scan incidents, internal hosts that perform vulnerability scans are
1436 far more active and the fraction of alerts that detect horizontal
1437 scans is higher, representing half of the total alerts generated.
1438 Alerts focused on Network-Behavior Anomaly Detection also appear for
1439 internal hosts.
1441 11.2.3. Malware downloads resulting in exposed endpoints
1443 This category of threats is generally detected by network security
1444 appliances. Despite these enterprises being purchasers of endpoint
1445 security products, 76% of the incidents detected by the network
1446 security products do not show a corresponding alert by an endpoint
1447 security product. A broad variety of network appliances contributed
1448 to the detection of a diverse collection of malware samples.
1450 11.2.4. Exploit kit infections
1452 This category of infections represents instances in which the
1453 customer's machines are exposed to exploit kits. These threats were
1454 detected by network appliances that extract suspicious URLs from
1455 network traffic taps and use a combination of sandbox technology and
1456 blacklists to identify websites that deploy a variety of exploit kits
1457 that were not being caught by endpoint security products. In this
1458 three month time period, the most prevalent categories of exploit
1459 kits detected involved redirections to the Magnitude exploit kit and
1460 exploit kits associated with phishing scams and attempts to expose
1461 users to fake Anti-Virus warnings and tools. A breakdown of the
1462 results is included below:
1464 +---------------------+-----------------------+
1465 | Severity | 3 - Critical |
1466 +---------------------+-----------------------+
1467 | Incident Category | Malicious Code |
1468 | | |
1469 | Incident Type | Exploit Kit Infection |
1470 | | |
1471 | # network incidents | 26 |
1472 | | |
1473 | # all incidents | 26 |
1474 | | |
1475 | Percentage | 100% |
1476 +---------------------+-----------------------+
1478 The network security product that detected these incidents produced
1479 the following alerts:
1481 o Advanced Malware Payloads
1483 o Exploit.Kit.FakeAV
1485 o Exploit.Kit.Magnitude
1487 o Exploit.Kit.MagnitudeRedirect
1489 o Exploit.Kit.PhishScams
1491 o HTMLMagnitudeLandingPage
1493 11.2.5. Attacks against servers
1495 In addition to detecting the aforementioned critical security
1496 incident categories, network security devices frequently detect a
1497 broad variety of attacks against servers that usually lack
1498 corroboration at the endpoint. Most server attacks are not matched
1499 by endpoint protection alerts: 62% are unmatched for critical
1500 incidents, and 88% are unmatched as lower severity incidents. This
1501 category of incidents is the most prevalent category of incidents
1502 detected primarily by network products, but they are usually rated
1503 lower in severity than the aforementioned classes of alerts as they
1504 are very commonplace. Even when these alerts are corroborated by
1505 endpoint protection alerts, the endpoint alerts are often low in
1506 severity, as in the case of file-based threats that appear to have
1507 been blocked or successfully cleaned up by an Anti-Virus solution.
1508 The challenge in taking action against server attacks is that it can
1509 be difficult to assess which of these attacks were successful in
1510 causing actual damage, and for this reason, for the fraction of
1511 server attacks that demonstrate corroborating endpoint security
1512 alerts, even if of low severity, should be examined. It is
1513 interesting to note the cooperative role played by both network and
1514 endpoint security devices in these instances.
1516 12. Regulatory Considerations
1518 This section will briefly look at the regulatory landscape and
1519 develop a specific view on the impact on endpoints with the goal to
1520 see what we might be able to learn.
1522 Legal requirements, compliance, regulatory frameworks and mandatory
1523 reporting are no longer separate from any security evaluation,
1524 process or requirement within an organisation, enterprise system or
1525 intranet. It is essential to look at the technical and regulatory
1526 approaches together. This section will look at two examples of legal
1527 requirements and guidance:
1529 (1) IoT security (2) Network infrastructure
1531 This section is by no means complete, but it does a discussion on
1532 this aspect of endpoint and ecosystem regulation.
1534 12.1. IoT Security
1536 IoT security regulation is emerging in the form of voluntary
1537 frameworks and self-assessments that relate to endpoint security
1538 issues.. These frameworks focus first on the end point, or mobile
1539 device, in the IoT environment and then on the holistic ecosystem
1540 itself.
1542 In 2017 the National Institute of Standards and Technology released
1543 its draft IoT Cybersecurity Framework based on consultations and
1544 interviews with all stakeholders over several years previously
1545 [NISTIOTP]. Some of the themes which emerged was the need for IoT
1546 governance, assessment frameworks, review of all aspects of the IoT
1547 ecosystem and a process for coordinated vulnerability disclosure
1548 inside an organisation. As evidenced by the 2018 Endpoint Protection
1549 and Response Survey by SANS, only 47% of organisations know that
1550 their endpoints have been breached and a further 20% are unsure
1551 [EPRSANS]. So a systemic approach from NIST was welcomed and the
1552 NIST framework became the gold standard for national IoT security
1553 frameworks.
1555 Other IoT security frameworks include the Singapore IoT Cyber
1556 Security Guide from January 2019 and the UK's Secure by Design or The
1557 Government's Code of Practice for Consumer Internet of Things (IoT)
1558 Security for manufacturers, with guidance for consumers on smart
1559 devices at home [IMDAIOTG], [SBDGOVUK]. Once again both look at
1560 securing the IoT device or endpoint, but also security for the entire
1561 value chain of the IoT system. The Singapore framework makes the
1562 point about the entire system clear, "Similar to any system, an IoT
1563 system is as secure as its weakest link. It is thus important to
1564 ensure that proper security considerations and measures are put in
1565 place for both the implementation and operational stages of the
1566 deployment of any IoT system." [IMDAIOTG] Finally, the IoT Security
1567 Foundation, the GSMA and the Internet Society have all released their
1568 own frameworks for IoT security. All have similar characteristics
1569 which focus on the entire value chain and ecosystem, but also on
1570 vulnerability disclosure and checklist assessments. What makes each
1571 of these approaches slightly different is the differing perspectives
1572 of the organization advocating it. The GSMA is the mobile trade
1573 association and so it focuses on mobile devices while the Internet
1574 Society focuses on the Internet ecosystem and a multistakeholder
1575 approach. Systematically underpinning all the frameworks is the
1576 holistic approach with voluntary best practices and implementation
1577 based on the needs of the user or organisation adopting the framework
1578 [IOTSFCF], [GSMAIOT], [ISTRUST].
1580 12.2. Network infrastructure
1582 In Europe, the Network and Information Security Directive, which was
1583 passed in July 2016, require implementation by each European member
1584 state with a threefold aim. First, to put into place a national
1585 strategy for network and infrastructure security including best
1586 practices, guidelines, training and stakeholder consultations.
1587 Second, to coordinate national CSIRTs with CERT-EU and third to
1588 provide incident control and response systems for critical
1589 infrastructure and digital services [EURLEX]. This Directive
1590 demonstrates the importance give across the EU to network resilience
1591 and incident reporting. While securing the endpoint is acknowledged,
1592 the focus is on ensuring the security of European interoperable
1593 networks. In short, the importance of the security of the network
1594 including incident response shows that it isn't only the endpoints
1595 that should be the focus of the regulation and legal frameworks.
1597 12.3. Auditing and Assessment
1599 This section will talk about other risk assessment and auditing
1600 regulatory requirements beyond the NIS directive.
1602 One example of risk assessment as a regulatory requirement is the New
1603 York State law 23 NYCRR 500 of the Regulations of the Superintendent
1604 of Financial Services (Cybersecurity Requirements for Financial
1605 Services Companies). Among the requirements, audit, risk assessment
1606 and risk reporting are included like,
1607 (2) include audit trails designed to detect and respond to
1608 Cybersecurity Events that have a reasonable likelihood of materially
1609 harming any material part of the normal operations of the Covered
1610 Entity. [NYCYBER]
1612 12.4. Privacy Considerations
1614 We may consider a specific focus on privacy in the future.
1616 13. Human Rights Considerations
1618 This section may develop a specific view of requirements, limits and
1619 constraints coming from Human Rights perspective on endpoint
1620 security.
1622 14. Security Considerations
1624 This document is about Security Considerations
1626 15. IANA Considerations
1628 This document has no actions for IANA
1630 16. Informative References
1632 [ADAPTURE]
1633 Cullen, T., "Limits of endpoint only", July 2017,
1634 .
1637 [AMT1] Khandelwal, S., "Explained - How Intel AMT Vulnerability
1638 Allows to Hack Computers Remotely", May 2017,
1639 .
1642 [AMT2] Symantec, ., "Web Attack Intel AMT Privilege Escalation
1643 CVE-2017-5689", 2017,
1644 .
1647 [ATTACK] "MITRE ATT&CK", n.d., .
1649 [BOT] Marinho, R., "Exploring a P2P transient botnet - From
1650 Discovery to Enumeration", July 2017,
1651 .
1654 [CANDID1] Wueest, C., "How my TV got infected with ransomware and
1655 what you can learn about it", November 2015,
1656 .
1659 [CANDID2] Dickson, B., "Millions of smart TVs are vulnerable to
1660 hackers", February 2014,
1661 .
1663 [CAPEC] "MITRE CAPEC", n.d.,
1664 .
1666 [ENISA] ENISA, ., "Baseline Security Recommendations for IoT in
1667 the context of Critical Information Infrastructures",
1668 November 2017, .
1671 [EPPEDR] Redscan, ., "EPP and EDR - What's the difference?", June
1672 2018, .
1675 [EPPGUIDE]
1676 "IT Pro's Guide to Endpoint Protection", n.d.,
1677 .
1680 [EPPSECURITY]
1681 Hunt, J., "Advantages and Disadvantages of Three Top
1682 Endpoint Security Vendors", n.d.,
1683 .
1686 [EPRSANS] Neely, L., "Endpoint Protection and Response A SANS
1687 Survey", June 2018, .
1690 [EPTAXONOMY]
1691 MacFadden, M., "Endpoint Taxonomy for CLESS", July 2019,
1692 .
1695 [ERICSSON]
1696 Ericsson, ., "Internet of Things forecast", n.d.,
1697 .
1700 [EURLEX] EUP, ., "Directive (EU) 2016/1148", July 2016,
1701 .
1704 [FLAMER] Symantec, ., "W32.Flamer Microsoft Windows Update Man-in-
1705 the-Middle", June 2012,
1706 .
1709 [GARTNERIOT]
1710 Van der Meulen, R., "Gartner Says 8.4 Billion Connected
1711 Things Will be in Use in 2017, Up 31 percent from 2016",
1712 February 2017, .
1716 [GARTNERREPORT]
1717 Crotty, J., "New Gartner Report Redefines Endpoints
1718 Protection for 2018", January 2018,
1719 .
1722 [GSMAIOT] GSMA, ., "GSMA IoT Security Guidelines and Assessment",
1723 n.d., .
1726 [HSTODAY] Hstoday, ., "Layered Approach Critical to Effective
1727 Endpoint Protection", October 2016,
1728 .
1732 [I-D.draft-mcfadden-smart-endpoint-taxonomy-for-cless-00]
1733 McFadden, M., "Endpoint Taxonomy for CLESS", draft-
1734 mcfadden-smart-endpoint-taxonomy-for-cless-00 (work in
1735 progress), July 2019.
1737 [IMDAIOTG]
1738 IMDA, ., "IMDA IoT Cyber Security Guide", January 2019,
1739 .
1744 [IOTPATCHING]
1745 Rogers, D., "Handling vulnerabilities as an IoT vendor",
1746 December 2018, .
1750 [IOTSFCF] IoTSF, ., "IoT Security Compliance Framework", December
1751 2018, .
1754 [ISTRUST] ISOC, ., "Internet of Things (IoT) Trust Framework v2.5",
1755 May 2018,
1756 .
1759 [LOJAX] ESET, ., "LoJax First UEFI rootkit found in the wild,
1760 courtesy of the Sednit group", September 2018,
1761 .
1764 [LOTLSYMC]
1765 Wueest, C., "Living off the land and fileless attack
1766 techniques", July 2017,
1767 .
1771 [MIRAI1] Symantec, ., "Mirai, what you need to know about the
1772 botnet behind recent major DDoS attacks", October 2016,
1773 .
1776 [MIRAI2] Krebsonsecurity, ., "19 Mirai Botnet Authors Avoid Jail
1777 Time", September 2018,
1778 .
1780 [MONEYBALL]
1781 Roundy, K., "Predicting Cyber Threats with Virtual
1782 Security Products. ACSAC", 2017,
1783 .
1786 [NDSSPATCH]
1787 Caballero, J., "Mind Your Own Business A Longitudinal
1788 Study of Threats and Vulnerabilities in Enterprises",
1789 February 2019, .
1793 [NETTODAY]
1794 Dix, J., "Layered Security Defenses What layer is most
1795 critical network or endpoint", July 2011,
1796 .
1800 [NINESIGNS]
1801 Smith, K., "9 signs your endpoint security isn't working",
1802 May 2017, .
1805 [NISTIOTP]
1806 NIST, ., "NIST Cybersecurity for IoT Program", November
1807 2016, .
1810 [NYCYBER] NYCRR, ., "See 3 NYCRR 500 of the Regulations of the
1811 Superintendent of Financial Services (Cybersecurity
1812 Requirements for Financial Services Companies)", n.d.,
1813 .
1816 [OWASP] OWASP, ., "Defense in depth definition", August 2015,
1817 .
1819 [PDDoS] Seals, T., "Pulse-Wave DDoS Attacks Mark a New Tactics in
1820 Q2", October 2017, .
1823 [SBDGOVUK]
1824 UK, GOV., "Secure by Design", February 2019,
1825 .
1828 [SGX1] Claburn, T., "Intel SGX safe room easily trashed by white-
1829 hat hacking marauders Enclave malware demoed", February
1830 2019, .
1833 [SGX2] Cimpanu, C., "Researchers hide malware in Intel SGX
1834 enclaves", February 2019, .
1837 [SQL] Cobb, M., "SQL injection detection tools and prevention
1838 strategies", November 2009,
1839 .
1842 [STATISTA1]
1843 Statista, ., "Internet of Things (IoT) connected devices
1844 installed base worldwide from 2015 to 2025 (in billions)",
1845 n.d., .
1848 [STATISTA2]
1849 Statista, ., "Size of Internet of Things market worldwide
1850 in 2014 and 2020 by industry (in billion U.S dollars)",
1851 n.d., .
1855 [TEEP] Cam-Winget, N., "Trust Execution Environment Protocol",
1856 March 2018, .
1858 [USCERT] Michael, C., "Principles of defense-in-depth", September
1859 2005, .
1863 [ZERODAY1]
1864 McHugh, J., "Windows of Vulnerability A Case Study
1865 Analysis", 2000, .
1868 [ZERODAY2]
1869 Plattner, B., "Large-Scale Vulnerability Analysis",
1870 September 2006, .
1873 Appendix A. Contributors
1875 o Arnaud Taddei
1876 Symantec
1877 arnaud_taddei@symantec.com
1879 o Bret Jordan
1880 Symantec
1881 bret_jordan@symantec.com
1883 o Candid Wueest
1884 Symantec
1885 candid_wueest@symantec.com
1887 o Chris Larsen
1888 Symantec
1889 chris_larsen@symantec.com
1891 o Andre Engel
1892 Symantec
1893 andre_ngel@symantec.com
1895 o Kevin Roundy
1896 Symantec
1897 kevin_roundy@symantec.com
1899 o Yuqiong Sun
1900 Symantec
1901 Yuqiong_Sun@symantec.com
1903 o David Wells
1904 Symantec
1905 David_Wells@symantec.com
1907 o Dominique Lazanski
1908 Last Press Label
1909 dml@lastpresslabel.com
1911 Authors' Addresses
1913 Arnaud Taddei
1914 Symantec Corporation
1915 350 Ellis Street
1916 Mountain View CA 94043
1917 USA
1919 Email: arnaud_taddei@symantec.com
1921 Candid Wueest
1922 Symantec Corporation
1923 350 Ellis Street
1924 Mountain View CA 94043
1925 USA
1927 Email: candid_wueest@symantec.com
1929 Kevin A. Roundy
1930 Symantec Corporation
1931 350 Ellis Street
1932 Mountain View CA 94043
1933 USA
1935 Email: kevin_roundy@symantec.com
1936 Dominique Lazanski
1937 Last Press Label
1938 Flat 1, 109A Columbia Road
1939 London E2 7RL
1940 UK
1942 Email: dml@lastpresslabel.com