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2 IETF A. Taddei
3 Internet-Draft C. Wueest
4 Intended status: Informational K. Roundy
5 Expires: September 26, 2019 Symantec Corporation
6 D. Lazanski
7 Last Press Label
8 March 25, 2019
10 Capabilities and Limitations of an Endpoint-only Security Solution
11 draft-taddei-smart-cless-introduction-00
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 September 26, 2019.
38 Copyright Notice
40 Copyright (c) 2019 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 . . . . . . . . . . . . . . . . . . . . . . 8
63 7. Endpoint Security Capabilities . . . . . . . . . . . . . . . 10
64 8. What would be a perfect endpoint security solution? . . . . . 13
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 . . . 19
72 10.2.1. LoJax UEFI rootkit . . . . . . . . . . . . . . . . . 19
73 10.2.2. SGX Malware . . . . . . . . . . . . . . . . . . . . 20
74 10.2.3. AMT Takeover . . . . . . . . . . . . . . . . . . . . 20
75 10.2.4. AMT case study (anonymised) . . . . . . . . . . . . 21
76 10.2.5. Users bypass the endpoint security . . . . . . . . . 22
77 10.3. Endpoints may miss information leakage attacks . . . . . 22
78 10.3.1. Meltdown/Specter . . . . . . . . . . . . . . . . . . 22
79 10.3.2. Network daemon exploits . . . . . . . . . . . . . . 22
80 10.3.3. SQL injection attacks . . . . . . . . . . . . . . . 23
81 10.3.4. Low and slow data exfiltration . . . . . . . . . . . 23
82 10.4. Suboptimality and gray areas . . . . . . . . . . . . . . 24
83 10.4.1. Stolen credentials . . . . . . . . . . . . . . . . . 24
84 10.4.2. Zero Day Vulnerability . . . . . . . . . . . . . . . 25
85 10.4.3. Port scan over the network . . . . . . . . . . . . . 25
86 10.4.4. DDoS attacks . . . . . . . . . . . . . . . . . . . . 26
87 11. Learnings from production data . . . . . . . . . . . . . . . 27
88 11.1. Endpoint only incidents . . . . . . . . . . . . . . . . 28
89 11.2. Security incidents detected primarily by network
90 security products . . . . . . . . . . . . . . . . . . . 29
91 11.2.1. Unauthorized external vulnerability scans . . . . . 29
92 11.2.2. Unauthorized internal vulnerability scans . . . . . 30
93 11.2.3. Malware downloads resulting in exposed endpoints . . 30
94 11.2.4. Exploit kit infections . . . . . . . . . . . . . . . 30
95 11.2.5. Attacks against servers . . . . . . . . . . . . . . 31
96 12. Regulatory Considerations . . . . . . . . . . . . . . . . . . 32
97 12.1. IoT Security . . . . . . . . . . . . . . . . . . . . . . 32
98 12.2. Network infrastructure . . . . . . . . . . . . . . . . . 33
99 12.3. Auditing and Assessment . . . . . . . . . . . . . . . . 33
100 12.4. Privacy Considerations . . . . . . . . . . . . . . . . . 34
101 13. Human Rights Considerations . . . . . . . . . . . . . . . . . 34
102 14. Security Considerations . . . . . . . . . . . . . . . . . . . 34
103 15. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 34
104 16. Informative References . . . . . . . . . . . . . . . . . . . 34
105 Appendix A. Contributors . . . . . . . . . . . . . . . . . . . . 39
106 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 40
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. More work to model the various
282 endpoint types would be helpful for this draft (in the same spirit as
283 the IETF TEEP Working Group generalized its work, see [TEEP]).
285 We require a framework in order to define and model the endpoint
286 itself and the position of the endpoint in the network. In this
287 initial analysis we focus on endpoints that are User Equipment (UE)
288 rather than on hosts. In the future, we hope to balance and unify
289 the model.
291 For example:
293 o The following would be considered as UEs: a smartphone, a smart
294 device, any IoT device, a laptop, a desktop, a workstation, etc.
296 o Hosts represent too, physical servers, virtual servers/machines,
297 etc.
299 We need two models for the endpoint, internally and in an end-to-end
300 context within the network. With this approach we expect both models
301 to help us cover all the threat landscape and capabilities for
302 endpoint security. This will help us understand point attacks versus
303 composite attacks within context, and, accordingly, understand
304 holistically the capabilities and the limitations of endpoint
305 security. For example to differentiate when only an application on
306 the end point is affected.
308 5.1. Internal representation of an endpoint
310 An internal representation of an endpoint could be generalized by the
311 simple diagram below:
313 +----------------------------+
314 | Application |
315 +----------------------------+
316 | OS / Execution Environment |
317 +----------------------------+
318 | Hardware |
319 +----------------------------+
321 Today there are many combinations of Hardware, OS/EE pairing and
322 Application layers, offering the user a vast set of features with a
323 wide spectrum of capabilities.
325 Furthermore we can consider that an application running on a UE or a
326 host is an endpoint too, so we have multiple ways to read the above
327 diagram.
329 In essence we want to consider here endpoints including those which
330 have a variance in electrical power, computational power, memory,
331 disk, network interfaces, size, ownership, etc.
333 5.2. Endpoints modeled in an end-to-end context
335 A representation of endpoints in an end-to-end context could look
336 like the following diagram:
338 +-------+ +---------------------+---------+
339 | Human | <- (1) -> | Digital Persona | Application | <- (2) ->
340 +-------+ +-----------+-------------------+
341 | User Equipment |
342 +-------------------------------+
344 +----------------+ +----------------+
345 <- (2) -> | Network | <- (3) -> | Platform/Hosts |
346 | Infrastructure | +----------------+
347 +----------------+
349 1. Humans have a user experience (UX) with the UE, starting with an
350 explicit or implicit Digital Persona, engaging with an
351 application
353 2. The application will have sessions through a large Network
354 Infrastructure where we do not assume anything of the
355 infrastructure (could be landlines, mobile networks, satellites,
356 etc.) and those sessions reach
358 3. a Platform consisting of many Hosts either physical or virtual
359 and it ensures a large part of the end-to-end user experience.
361 In this end-to-end model we see that many other systems may have
362 interactions with the UE: the human, the UX, the digital persona, the
363 sessions, the intermediate network infrastructure, and the hosts and
364 application at the destination.
366 If we now look at security aspects of the above models, the threat
367 landscape is very large and the attack surface will cover all the
368 components and interactions at any level.
370 6. Threat Landscape
372 (Editor's note: this section will require a significant amount of
373 future development.)
374 Given the vast number of combinations that the above generic modeling
375 offers us, defining a threat landscape should be done carefully and
376 will require a systematic methodology.
378 Therefore this entire section will be developed through future
379 iterations of the document, in this initial version we will start
380 structuring an approach and then adjust this based on feedback.
382 There is no doubt that we want to cover typical known attacks such
383 as:
385 o Malware (Trojans, viruses, backdoors, bots, etc.)
387 o Adware and spyware
389 o Exploits
391 o Phishing
393 o Script based attacks
395 o Ransomware, local Denial of Service (DoS) attacks
397 o Denial of Service (DoS) attacks
399 o Malicious removable storage devices (USB)
401 o In memory attacks
403 o Rootkits and firmware attacks
405 o Scams and online fraud
407 o System abuse (staging/proxying)
409 o etc.
411 To illustrate the difficulty to define a good threat landscape, when
412 it comes to cryptojacking and coinmining that were on the rise, in
413 which category do they fall: malware? DoS? system abuse? or a
414 category on its own?
416 This is why we wanted to conduct a thorough gap analysis using
417 existing definitions and frameworks, but we couldn't find an existing
418 comprehensive and recognized taxonomy dedicated to the threat
419 landscape on endpoints. We found however different models in this
420 field, and have considered two. We are open to further suggestions.
422 Indeed both of the analysed frameworks contain threat landscape
423 descriptions:
425 o MITRE Common Attack Pattern Enumeration Classification (CAPEC).
426 See [CAPEC].
428 o MITRE ATT&CK. See [ATTACK].
430 These offer us interesting ways to assess the threat landscape:
432 o CAPEC offers a hierarchical view of attack patterns by domains
433 which can match some aspects of both of our above models, but we
434 will need to identify those attacks that fit exactly in our scope.
436 o ATT&CK offers a very straightforward categorized knowledge base of
437 attacks, but it concentrates on the entreprise attack chain, so we
438 will need to do some work to extract what we need.
440 We recognise however that these frameworks do not address all of the
441 threats that can affect the security of a system, for example they do
442 not cover; routing hijacking, flooding, selective blocking,
443 unauthorised modification of data sent to an endpoint, etc. Further
444 work to define categories of threats is therefore required.
446 As a further example, phishing should be included as an attack, but
447 whilst this is indeed an attack that will materialize on a device
448 through an application (email, webmail, etc.), the real target of
449 this attack is not the device, but the human behind the digital
450 persona.
452 Having a methodology of assessment is necessary here, because it will
453 help decide what is in scope vs. out of scope.
455 We are aware that once a method and the categories are fully defined
456 in this section, it will force a review of all the following sections
457 in the document. Whilst remapping will be necessary, it should not
458 drastically change the draft.
460 7. Endpoint Security Capabilities
462 In this section we try to define some endpoint security capabilities
463 (Editor's note: this section will require future development.)
465 In this version of the document we will start by developing a
466 framework to categorize and position endpoint security capabilities
467 with the goal of defining what an ideal endpoint security capability
468 would look like.
470 By endpoint security capabilities we mean how to protect the endpoint
471 against attacks. Protection has many meanings, we want to
472 distinguish three different aspects of protection:
474 o Prevention - The attack doesn't succeed by intrinsic or explicit
475 security capabilities.
477 o Detection - The attack is happening or has happened and is
478 recorded and/or signalled to another component for action.
480 o Mitigation - Once detected, the attack can be halted or its
481 effects can at least be reduced or reversed.
483 For example, prevention methods include keeping the software updated
484 and patching vulnerabilities, implementing measures to authenticate
485 the provenance of incoming data to stop the delivery of malicious
486 content, or choosing strong passwords. Detection methods include
487 inspecting logs or network traffic. Mitigation could include
488 deploying backups to recover from an attack with minimal disruption.
490 Our intention however is not just to consider each endpoint security
491 capability separately, but also the overall endpoint security
492 holistically with all its interdependencies. Indeed, we defined a
493 simple endpoint, but each layer may or may not have a certain
494 spectrum of intrinsic capabilities and there may be multiple ways to
495 provide add-on and third-party endpoint security capabilities,
496 allowing complex interactions between all of these components.
498 We define two different aspects of endpoint security capabilities and
499 their subdivisions as:
501 o (A) Intrinsic security capability can be built-into each of the
502 endpoint model layers
504 * (1) Hardware
506 * (2) OS/EE
508 * (3) Application
510 o (B) Add-on security capability can be
512 * (4) a component of the hardware
514 * (5) a component of the OS/EE
516 * (6) an application by itself
518 In (A) we relate to a 'security by design' intention of the
519 developers and they will intrinsically offer a security model and
520 security capabilities as part of their design. A typical example of
521 this is the authorization model.
523 In (B) a 3rd party is offering an additional security component which
524 was not necessarily considered when the Hardware, OS/EE or
525 Application were designed.
527 In the future we will review all the main categories of security
528 capabilities that are known to date and assess security capability
529 enablers like Artificial Intelligence (AI) and Machine Learning (ML).
530 For each category we will try to give a review on how effective the
531 capability is in securing the system.
533 With regard to (6), there are many available options for add-on
534 security capabilities offered by third-parties as applications on a
535 commercial or open-source basis. Gartner (see [GARTNERREPORT])
536 highlights the evolution of endpoint security towards two directions
537 as shown in [EPPEDR], [EPPSECURITY], [EPPGUIDE].
539 o Endpoint Protection Platform (EPP) as an integrated security
540 solution designed to detect and block threats at the device level.
542 o Endpoint Detection and Response (EDR) as a combination of next
543 generation tools to provide anomaly detection and alerting,
544 forensic analysis and endpoint remediation capabilities.
546 Among the security capabilities that we list, the endpoint can
547 perform the following:
549 o Intrinsic
551 * Software updates / patching
553 * Access Control (RBAC, ABAC, etc.)
555 * Authentication
557 * Authorization
559 * Detailed event logging
561 o Execution protection
563 * Exploit mitigation (file/memory)
565 * Tamper protection
566 * Whitelisting filter by signatures, signed code or other means
568 * System hardening and lockdown (HIPS, trusted boot, etc.)
570 o Malware protection
572 * Scanning - on access/on write/scheduled/quick scan (file/
573 memory)
575 * Reputation-based blocking on files or by ML
577 * Behavior-based detection - (heuristic based/ML)
579 * Rootkit and firmware detection
581 * Threat intelligence based detection (cloud-based/on premise)
583 * Static detection - generic, by emulation, by ML, by signature
585 o Attack/Exploit/Application Protection
587 * Application protection (browser, messaging clients, social
588 media, etc.)
590 + Disinformation Protection (anti-phishing, fake news, anti-
591 spam, etc.)
593 + Detection of unintended link location (URL blocklist, etc.)
595 + Memory exploit mitigation, e.g. browsers
597 * Network Protection (local firewall, IDS, IPS and local proxy)
598 inbound and outbound
600 * Detection of network manipulation (ARP, DNS, etc.)
602 * Data Loss Prevention and exfiltration detection (incl. covert
603 channels)
605 8. What would be a perfect endpoint security solution?
607 With all the above knowledge, let's consider what we could expect
608 from a perfect endpoint security 'system'. It would:
610 o find instantly accurate reputation for any file before it gets
611 executed and block it if needed.
613 o monitor any behavior on the endpoint, including inbound and
614 outbound network traffic, learn and identify normal behavior and
615 detect and block malicious actions, even if the attack is misusing
616 legitimate clean system tools or hiding with a rootkit.
618 o patch instantly across all devices/systems/OSes, including virtual
619 patching, meaning you can patch or shield an application even
620 before an official patch is released.
622 o exploit protection methods for all processes where applicable,
623 e.g. no execute bit (NX), data execution prevention (DEP), address
624 space layout randomization (ASLR), Control Flow Integrity Guard
625 (CFI/CFG), stack canaries, shadow stack, reuse attack protection
626 (RAP), etc. all of which are methods, which make it very difficult
627 to successfully run any exploit, even for zero day
628 vulnerabilities.
630 o detect attempts to re-route data to addresses other than those
631 which the user intended, e.g. detect incorrectly served DNS
632 entries, TLS connections to sites with invalid certificates, data
633 that is being proxied without explicit user consent, etc.
635 o have an emulator/sandbox/micro virtualization to execute code and
636 analyse the outcome and perform a roll back of all actions if
637 needed, e.g. for ransomware.
639 o allow the endpoint to communicate with the other endpoints in the
640 local network and globally, to learn from 'the crowd' and
641 dynamically update rules based on its findings.
643 o be in constant sync with all other endpoints deployed on a network
644 and other security solutions, run on any OS, with no delay
645 (including offline modes and on legacy systems).
647 o run from the OS/EE when possible.
649 o run as one of the first process on the OS/EE and protect itself
650 from any form of unwanted tampering.
652 o offers a reliable logging that can't be tampered with, even in the
653 event of system compromise.
655 o receive updates instantly from a trusted central entity.
657 9. The defence-in-depth principle
659 In this section we give a high level view of what we mean by
660 'defence-in-depth'.
662 Whilst endpoint security systems have good capabilities, sometimes it
663 is debatable and perhaps suboptimal to let the endpoint run the
664 capability alone or at all. It is generally considered good security
665 practice to adopt a defence-in-depth approach (see [USCERT]). The
666 Open Web Application Security Project group (OWASP) describes the
667 concept as follows: "The principle of defense-in-depth is that
668 layered security mechanisms increase security of the system as a
669 whole. If an attack causes one security mechanism to fail, other
670 mechanisms may still provide the necessary security to protect the
671 system." (see [OWASP])
673 Indeed there are many other constituencies as per our end-to-end
674 model that can participate in the defence process: The network, the
675 infrastructure itself, the platform, the human, the user experience
676 and in a hybrid of an on premise and cloud approach, an Integrated
677 Cyber Defence (ICD) of the entire chain.
679 The simple idea behind the concept is that "every little helps". If
680 the endpoint is not 100% secure itself, the detection chance can
681 increase with additional security capabilities from other entities.
682 We acknowledge that there are some case where adding an additional
683 component to the system may degrade the overall security level by
684 introducing new weaknesses.
686 There are various reference article in the industry highlighting
687 limitations of endpoint only solutions. For example this quote here,
688 which talks about multi-tier solutions: "There are limitations with
689 any endpoint protection solution, however, that can limit protection
690 to only the client layer. There is also a need for security above
691 the client layer, as endpoint protection products cannot intercept
692 traffic. Vendors will often sell a multi-tiered solution that
693 enables a network appliance to assist the endpoint protection client
694 by intercepting traffic between the attacker and the infected client.
695 Vendors will also sell solutions that monitor and intercept traffic
696 on internal or external network segments to protect the enterprise
697 from these threats. A prime example of the limitations of endpoint
698 protection software is infection via a phishing attack." [ADAPTURE].
700 Some sources point out that even the best solution might not get
701 deployed in the optimal way in a real world scenario as the
702 environment can be very complex: "While endpoint security has
703 improved significantly with the introduction of application
704 whitelisting and other technologies, our systems and devices are
705 simply too diverse and too interconnected to ensure that host
706 security can be deployed 100% ubiquitously and 100% effectively."
707 [NETTODAY]
709 On these grounds it is considered a good idea to follow a layered
710 approach when it comes to security. "In today's complex threat
711 environment, companies need to adopt a comprehensive, layered
712 approach to security, which is a challenging task in such as rapidly
713 evolving, crowded market." [HSTODAY]
715 It is important to comprehend the capabilities of endpoint security
716 solutions in this overall picture of the connected environment, which
717 includes other systems, networks and various protocols that are used
718 to interact with these entities. Understanding possible shortcomings
719 from single layered solutions can help counterbalance such weaknesses
720 in the architectural concept or the protocol design.
722 In order to quantify any potential benefits or limitations of the
723 various layered scenarios in regards to security a solid data set is
724 needed. This section requires statistics about proportions of
725 attacks that go undetected in various cases. We propose analysing
726 data for the following four cases:
728 o There is no security solution
730 o Security is only on the endpoint
732 o Security is only on the network
734 o Security is on both the endpoint and the network
736 However reconciling various statistics requires a lot of caution and
737 time, a methodology and consistent classification to avoid any
738 misinterpretation.
740 10. Endpoint Security Limits
742 The previous section defines an ideal endpoint security 'system',
743 however, from the real world, the expectation of what we can get from
744 an endpoint security solution will look more along the following
745 lines:
747 o may not be able to run at full capacity due to computational power
748 limits, battery life, performance, or policies (such as BYOD
749 restrictions in enterprise networks), etc.
751 o may not be able to run at full capacity as it slows down
752 performance too much.
754 o will miss some of the malware or attacks, regardless of detection
755 method used, like signatures, heuristics, machine learning (ML),
756 artificial intelligence (AI), etc.
758 o have some level of False Positives (FP).
760 o not monitoring or logging all activities on the system, e.g. due
761 to constraints of disk space or when a clean windows tool is being
762 triggered to do something malicious but the activity is not
763 logged. Such activity can be logged, but a decision needs to be
764 made if it's clean or not.
766 o have its own vulnerabilities or simple instabilities that could be
767 used to compromise the system.
769 o be tampered with by the user, e.g. disabled or reconfigured.
771 o be tampered with by the attacker, e.g. exceptions added or log
772 files wiped.
774 In the section below we review a number of these limitations through
775 real examples, step by step. Some limitations are absolute, and some
776 limitations result in a grey area or suboptimality for the solution.
778 10.1. No possibility to put an endpoint security add-on on the UE
780 UEs will vary a lot; by 2022, an estimated 29 billion devices will be
781 connected, with 18 billion of them related to IoT [ERICSSON]. Many
782 IoT products lack the capacity to install any endpoint security
783 capabilities, are unable to update the software, and it is not
784 possible to force the UE provider to improve or even offer an
785 intrinsic security capability.
787 We acknowledge that the numbers do vary significantly depending on
788 the source, for example:
790 o [STATISTA1] is showing the current trajectory of IoT devices from
791 25B to date to 40+B in 2022 and 75B in 2025.
793 o [ERICSSON] is more conservative and might requires an update, but
794 it was reaching 29B devices in 2022, with a nice breakdown between
795 device types and connectivity.
797 o [STATISTA2] is showing a breakdown by verticals and is even more
798 conservative than both of the above.
800 o [ENISA] it refers to a [GARTNERIOT] report from 2017 which sets a
801 trajectory to 20B devices by 2020.
803 In IoT we find UEs such as medical devices which are limited by
804 regulation, welding robots that can't be slowed down, smart light
805 bulbs which are limited by the processing power, etc. There are many
806 factors influencing whether endpoint security can be added to a UE:
808 o The UE is simply not powerful enough or the performance hit is too
809 high.
811 o Adding your own security will breach the warranty or will
812 invalidate a certification or a regulation (breach of validity).
814 o The UE needs to run in real-time and any delay introduced by a
815 security process might break the process.
817 o Some UEs are simply locked by design and the manufacturer does not
818 provide a security solution (e.g. smart TV, fitness tracker or
819 personal artificial assistants) see [CANDID1], [CANDID2].
821 In the future, a possible research problem would be to find hard data
822 on the exact proportion of IoT devices that are unable to run any
823 endpoint security add-on or that have no intrinsic security built-in.
825 The other hidden dimension here is the economical aspect. Many
826 manufacturer are reluctant to invest in IoT device security, because
827 it can significantly increases the cost of their solution and there
828 is the perception that they will lose market shares, as customers are
829 not prepared to pay the extra cost for added security.
831 10.1.1. Not receiving any updates or functioning patches
833 The endpoint security system may lack a built-in capability to be
834 patched or it may be connected to a network that prevents the process
835 of downloading updates automatically. For example stand-alone
836 medical systems or industrial systems in isolated network segments
837 often do not have a communication channel to the Internet.
839 Even if security updates are received, they typically will only be
840 periodically updated; hence there will be a window of opportunity for
841 an attacker, between the time the attack is first used, and the time
842 the attack is discovered/patched and the patch is deployed.
844 In addition updates and patches may themselves be malicious by
845 mistake, or on purpose if not properly authenticated, or if the
846 source of the updates has malicious intent. This could be part of a
847 software update supply chain attack or an elaborate attacker breaking
848 the update process, as for example seen with the Flamer group (see
849 [FLAMER]).
851 A recent survey found that fewer than 10% of consumer IoT companies
852 follow vulnerability disclosure guidelines at all, which is regarded
853 as a basic first step in patching vulnerabilities (see
854 [IOTPATCHING]). This indicates that many IoT devices do not have a
855 defined update process or may not even create patches for most of the
856 vulnerabilities.
858 Furthermore some endpoints system may reach the end of their support
859 period and therefore no longer receive any updates for the OS/EE or
860 the security solution due to missing licenses. However the systems
861 may remain in use and become increasingly vulnerable as time goes on
862 and new attacks are discovered.
864 10.1.2. Mirai IoT bot
866 +-------------+-----------------------------------------------------+
867 | Description | A Mirai bot infecting various IoT devices through |
868 | | weak passwords over Telnet port TCP 23 and by using |
869 | | various vulnerabilities, for example the SonicWall |
870 | | GMS XML-RPC Remote Code Execution Vulnerability |
871 | | (CVE-2018-9866) on TCP port 21009. Once a device is |
872 | | compromised it will scan for further victims and |
873 | | then start a DoS attack. |
874 +-------------+-----------------------------------------------------+
875 | Simplified | Compromised device scans network for multiple open |
876 | attack | ports, attempts infection through weak password and |
877 | process | exploits, downloads more payload, starts DoS |
878 | | attack. |
879 | | |
880 | UE | No security tool present on majority of IoT |
881 | | devices, hence no detection possible. If a |
882 | | rudimentary security solution with limited |
883 | | capabilities such as outgoing firewall is present |
884 | | on the IoT device e.g. router, then it might be |
885 | | able to detect the outbound DoS attack and slow it |
886 | | down. |
887 | | |
888 | References | [MIRAI1][MIRAI2] |
889 +-------------+-----------------------------------------------------+
891 10.2. Endpoints may not see the malware on the endpoint
893 10.2.1. LoJax UEFI rootkit
894 +-------------+-----------------------------------------------------+
895 | Description | A device compromised with the LoJax UEFI rootkit, |
896 | | which is active before the OS/EE is started, hence |
897 | | before the endpoint security is active. It can pass |
898 | | back a clean 'image' when the security solution |
899 | | tries to scan the UEFI. Infection can either happen |
900 | | offline with physical access or through a dropper |
901 | | malware from the OS/EE. |
902 +-------------+-----------------------------------------------------+
903 | UE | A perfect endpoint security could potentially |
904 | | detect the installation process if it is done from |
905 | | the OS/EE and not with physical modification or in |
906 | | the factory. Once the device is compromised the |
907 | | endpoint security solution can neither detect nor |
908 | | remove the rootkit. The endpoint solution may |
909 | | detect any of the exhibited behaviour, for example |
910 | | if the rootkit drops another malware onto the OS/EE |
911 | | at a later stage. |
912 | | |
913 | Reference | [LOJAX] |
914 +-------------+-----------------------------------------------------+
916 10.2.2. SGX Malware
918 +-------------+-----------------------------------------------------+
919 | Description | Malware can hide in the Intel Software Guard |
920 | | eXtensions (SGX) enclave chip feature. This is a |
921 | | hardware-isolated section of the CPU's processing |
922 | | memory. Code running inside the SGX can use return- |
923 | | oriented programming (ROP) to perform malicious |
924 | | actions. |
925 +-------------+-----------------------------------------------------+
926 | UE | Since the SGX feature is by design out of reach for |
927 | | the OS/EE, an endpoint security solution can |
928 | | neither detect nor remove any injected malware. A |
929 | | perfect endpoint security solution could |
930 | | potentially detect the installation process if it |
931 | | is done from the OS/EE and not with physical |
932 | | modification or in the factory. |
933 | | |
934 | References | [SGX1] [SGX2] |
935 +-------------+-----------------------------------------------------+
937 10.2.3. AMT Takeover
938 +-------------+-----------------------------------------------------+
939 | Description | A targeted attack group can remotely execute code |
940 | | on a system through the Intel AMT (Active |
941 | | Management Technology) vulnerability |
942 | | (CVE-2017-5689) over TCP ports 16992/16993. This |
943 | | provides full access to the computer, including |
944 | | remote keyboard and monitor access. The attacker |
945 | | can install malware, modify the system or steal |
946 | | information. |
947 +-------------+-----------------------------------------------------+
948 | UE | The AMT is accessible even if the PC is turned off. |
949 | | Therefore any endpoint security software installed |
950 | | on the OS, would not be able to see this traffic |
951 | | and therefore also not able to detect it. |
952 | | |
953 | References | [AMT1], [AMT2] |
954 +-------------+-----------------------------------------------------+
956 10.2.4. AMT case study (anonymised)
958 An enterprise has a data center containing very sensitive data.
959 Their workstations use a certain Intel chipset which integrates the
960 AMT feature for remote computer maintenance. AMT is an interface for
961 hardware management of the workstations, including transmission of
962 screen content and keyboard and mouse input for remote maintenance.
963 Communication with the management workstation is implemented by AMT
964 through the network interface card (NIC) on the motherboard. The
965 network packets generated in this way are invisible both to the main
966 processor and thus to the OS running on the workstation. In autumn
967 of 2015, it became known that some AMT-enabled computers had a flaw
968 that allowed AMT's remote maintenance component to be activated and
969 configured by attackers. This also worked when the workstations were
970 switched off. The leakage of data through this vulnerability is
971 elusive and difficult to detect. The identified threat situation led
972 the organization to a new requirement implementing a method that can
973 reliably detect this and similar vulnerabilities. In particular, the
974 detection of rootkits and manipulated firmware, and this includes
975 also (UEFI) BIOS - has also been a focus of their attention.
977 The method used as a solution, compares the desired data packets
978 generated by a client operating system - the user, with the data
979 packets received on the switch port. If more data has been received
980 on the switch port than was been sent by the operating system - the
981 user, there is a strong possibility that something bad is happening -
982 like for example an infection via modified firmware or by rootkit.
984 10.2.5. Users bypass the endpoint security
986 +-------------+-----------------------------------------------------+
987 | Description | Endpoint security systems should not interfere with |
988 | | the normal operation of the endpoint to the extent |
989 | | that users become frustrated and want to disable |
990 | | them or configure them to disable a significant |
991 | | fraction of important security capabilities. |
992 +-------------+-----------------------------------------------------+
993 | UE | Add-on endpoint security is now bypassed or |
994 | | disabled by the user. Unless the endpoint is under |
995 | | monitored management or can prevent a user from |
996 | | modifying the configuration, then this is shutting |
997 | | down a significant fraction of the security |
998 | | capabilities. |
999 | | |
1000 | References | [NINESIGNS] |
1001 +-------------+-----------------------------------------------------+
1003 10.3. Endpoints may miss information leakage attacks
1005 Another aspect that endpoint security has issues in detecting are
1006 information disclosure or leakage attacks, especially on shared
1007 virtual/physical systems.
1009 10.3.1. Meltdown/Specter
1011 The Meltdown/Specter vulnerabilities and all its variants may allow
1012 reading of physical memory belonging to another virtual machine (VM)
1013 on the same physical system. This could reveal passwords,
1014 credentials, certificates etc. The trick is that an attacker can
1015 spin up his own VM on the same physical hardware. As this VM is
1016 controlled by the attacker, they will ensure that there is no
1017 endpoint security that detects the Meltdown exploit code when run.
1018 It is very difficult for the attacked VM to detect the memory read-
1019 outs. For know CPU vulnerabilities there are software patches
1020 available than can be applied. If it is an external service
1021 provider, it might not be in the power of the user to patch the
1022 physical system or to determine if this has been done by the
1023 provider.
1025 10.3.2. Network daemon exploits
1027 Other attack types, which leak memory data from a vulnerable web
1028 server, are quite difficult to detect for an endpoint security. For
1029 example the Heartbleed bug allows anyone on the Internet to read the
1030 memory of the systems protected by the vulnerable versions of the
1031 OpenSSL software. This could lead to credentials or keys being
1032 exposed. An endpoint solution needs to either patch the vulnerable
1033 application or monitor it for any signs of exploitation or data
1034 leakage and prevent the data from being exfiltrated.
1036 10.3.3. SQL injection attacks
1038 A SQL injection attack is an example of an attack that exploits the
1039 backend logic of an application. Typically this is a web application
1040 with access to a database. By encoding specific command characters
1041 into the query string, additional SQL commands can be triggered. A
1042 successful attack can lead to the content of the whole database being
1043 exposed to the attacker. There are other similar attacks that can be
1044 grouped together for the purpose of this task, such as command
1045 injection or cross site scripting (XSS). Although they are different
1046 attacks, they all at their core fail at input filtering and
1047 validation, leading to unwanted actions being performed.
1049 Applications that are vulnerable to SQL injections are very common
1050 and are not restricted to web applications. An endpoint solution
1051 needs to monitor all data entered into possible vulnerable
1052 applications. This should include data received from the network. A
1053 generic pattern matching for standard SQL injection attack strings
1054 can be applied to potentially block some of the attacks. In order to
1055 block all types of SQL injection attacks the endpoint solution should
1056 have some knowledge about the logic of the monitored application,
1057 which helps to determine how normal requests differ from attacks.
1058 Applications can be analysed at source code level for potential
1059 weaknesses, but dynamically patching is very difficult. See [SQL]
1061 10.3.4. Low and slow data exfiltration
1063 An endpoint security solution can detect low and slow data
1064 exfiltration, for example when interesting data sources are tracked
1065 and access to them is monitored. If the data source is not on the
1066 endpoint itself, e.g. a database in the network, then the received
1067 data needs to be tagged and its further use needs to be tracked. To
1068 make detection difficult, an attacker could decide to use an
1069 exfiltration process that sends only 10 bytes every Sunday to a
1070 legitimate cloud service. If that is not in the normal behavior
1071 pattern, then this anomaly could be detected by the endpoint. If the
1072 process that sends the data or the destination IP address have a bad
1073 reputation, then they could be stopped. Though it is very difficult
1074 to reliably block such an attack and most solutions have a specific
1075 threshold that needs to be exceeded before it is detected as an
1076 anomaly.
1078 10.4. Suboptimality and gray areas
1080 10.4.1. Stolen credentials
1082 Stolen credentials and misuse of system tools such as RDP, Telnet or
1083 SSH are a valid scenario during attacks. An attacker can use stolen
1084 credentials to remotely log into a system and access data or execute
1085 commands in this context like the legitimate user might do. An
1086 endpoint security solution can restrict access from specific IP
1087 addresses, but this is difficult in a dynamic environment and when an
1088 attacker might have already compromised a trusted device and misuse
1089 it as a stepping stone for lateral movement. The endpoint could
1090 perform additional checks of the source device, such as verifying
1091 installed applications and certain conditions. Again this will not
1092 work in all scenarios, e.g. a hijacked valid device during lateral
1093 movement.
1095 This means that the system will not be able to simply block the
1096 connection if the authentication with the stolen credentials
1097 succeeds. A multi factor authentication (MFA) could limit the use of
1098 stolen credentials, but depending on the system used and the
1099 determination of the attacker they might be able to bypass this
1100 hurdle as well e.g. cloning a SIM card to read text message codes.
1102 As a next step, a solution on the endpoint can monitor the behavior
1103 of the logged in user and determine if it represents expected normal
1104 behavior. Unfortunately, there is the chance for false positives
1105 that might block legitimate actions, hence the rules are usually not
1106 applied too tightly. The system can monitor for suspicious behavior,
1107 similar to malware detection, where every action is carefully
1108 analyzed and all activity is tracked. For example if the SSH user is
1109 adding all files to archives with passwords and then deletes the
1110 original files in the file explorer, then this could result in a
1111 ransomware case scenario. If only a few files are processed per
1112 hour, then this activity will be very difficult for the endpoint to
1113 distinguish from normal activity, in order to flag it as malicious.
1115 The problem of attackers blending in with normal activity is one of
1116 the biggest challenges with so called living off the land attack
1117 methods. The attacker chooses to keep their profile low by not
1118 installing any additional binary files on the system, but instead
1119 misuses legitimate system tools to carry out their malicious intent.
1120 This means that there is no malware file that could be identified and
1121 the detection relies solely on other methods such as behaviour based
1122 monitoring [LOTLSYMC].
1124 If information is shared across multiple endpoints, then each one
1125 could learn from the others and see how many connections came in from
1126 that source, what files were involved and what behavior the clients
1127 exhibited. This crowd wisdom approach would allow blocking rules to
1128 be applied after the first incident across multiple endpoints.
1130 10.4.2. Zero Day Vulnerability
1132 +-------------+-----------------------------------------------------+
1133 | Description | An attacker exploits a zero day vulnerability or |
1134 | | any recent vulnerability. |
1135 +-------------+-----------------------------------------------------+
1136 | UE | In theory this scenario could be handled by the |
1137 | | endpoint security: a) Once the intrinsic security |
1138 | | system has been patched, exploitation of the |
1139 | | vulnerability can be prevented. b) The add-on |
1140 | | security with enhanced capabilities or updated |
1141 | | methods can detect and mitigate the vulnerability. |
1142 | | It does not necessarily require the official patch. |
1143 | | |
1144 | Challenge | In practice many systems remain vulnerable to a |
1145 | | vulnerability months or even years after a security |
1146 | | fix has been released. Moreover there is a big gap |
1147 | | between when a vulnerability is disclosed and when |
1148 | | a security fix is available. Also there is a big |
1149 | | gap between when a security fix is available and |
1150 | | when the security fix is actually applied. A recent |
1151 | | study over three years, examined the patching time |
1152 | | of 12 client-side and 112 server-side applications |
1153 | | in enterprise hosts and servers. It took over 6 |
1154 | | months on average to patch 90% of the population |
1155 | | across all vulnerabilities. [NDSSPATCH]. We note |
1156 | | too: "The patching of servers is overall much worse |
1157 | | than the patching of client applications. On |
1158 | | average a server application remains vulnerable for |
1159 | | 7.5 months." |
1160 | | |
1161 | References | [ZERODAY1][ZERODAY2] |
1162 +-------------+-----------------------------------------------------+
1164 10.4.3. Port scan over the network
1166 An infected machine, let's say a Mirai bot on a router, is scanning a
1167 class B network for IP addresses with TCP port 80 open. The malware
1168 can slow it down to 1 IP address per 5 seconds (or any other
1169 threshold) and it can go in randomized order (like for example the
1170 nmap tool does) in order to make it difficult to find a sequential
1171 pattern. To increase detection difficulties, legitimate requests to
1172 existing web servers can be added in at random intervals.
1174 An endpoint solution might be able to detect this behaviour,
1175 depending on the threshold, but it will be difficult. At some point
1176 the pattern will be similar to browsing the web, so either the
1177 endpoint blocks the bot scanning and also the user from surfing, or
1178 it allows both.
1180 To make it even harder, the attacker can use a botnet that
1181 communicates over peer-to-peer (P2P) or a central command and control
1182 server (C&C) and then distribute the scan load over multiple hosts.
1183 This means each endpoint only scans a subset, let's say 100 IP
1184 addresses, but all 1,000 bots scan a total of 100,000 IP addresses.
1186 This attack is difficult to detect by a reasonable threshold on each
1187 endpoint individually. If the endpoints talk to each other and
1188 exchange information, then a collective decision can be made on the
1189 bigger picture of the bot traffic.
1191 Another option for the endpoint solution is to block the bot malware
1192 from operating on the computer, for example by detecting the
1193 installation, analyzing the behavior of the process or by preventing
1194 the binary from accessing the network. This includes blocking any
1195 form of communication for the process to its C&C server, regardless
1196 of if it is using a P2P network or misusing legitimate system tools
1197 or browsers to communicate with the Internet. Blocking indirect
1198 communication over system tools as part of living off the land
1199 tactics, can be very challenging.
1201 See [BOT]
1203 10.4.4. DDoS attacks
1205 For this example let us consider a botnet of 100,000 compromised
1206 computers and each one sends a burst of traffic to a remote target,
1207 for one second each, alternating in groups. This will generate some
1208 waves of pulse attack traffic. Similar comments can be made about
1209 overall pulsed DDoS attacks [PDDoS].
1211 A solution on the endpoint can attempt to detect the outgoing
1212 traffic. If the DoS attack is volume based and the time span of each
1213 pulse is large enough or the repeating frequency for each bot is
1214 high, then detection with thresholds on the endpoint is feasible. It
1215 is different, if it is an application layer DoS attack, where the
1216 logic of the receiving application is targeted, for example with too
1217 many search queries in HTTP GET requests. This would flood the
1218 backend server with intensive search requests, which can result in
1219 the web site no longer being responsive. Such attacks can succeed
1220 with a low amount of requests being sent, especially if its
1221 distributed over a botnet. This makes it very difficult for a single
1222 endpoint to detect such an ongoing attack, without knowledge from
1223 other endpoints or the network.
1225 Another option for the endpoint solution is to block the bot malware
1226 from operating on the computer, for example by detection the
1227 installation, analyzing the behavior of the process or by preventing
1228 the binary from accessing the network. This includes blocking any
1229 form of communication for the process to its C&C server, regardless
1230 of if it is using a P2P network or misusing legitimate system tools
1231 or browsers to communicate with the Internet. Blocking indirect
1232 communication over system tools as part of living off the land
1233 tactics, can be very challenging.
1235 11. Learnings from production data
1237 From the above limited considerations we can now check what we see
1238 from real production data using
1240 o the method described in [MONEYBALL]
1242 o the anonymised production data of Symantec MSS production for the
1243 past 3 months
1245 The core idea is to consider, based on all the imperfections we
1246 started to list above including the 'grey areas', that cybersecurity
1247 analysts are often presented with suspicious machine activity that
1248 does not conclusively indicate a compromise, resulting in undetected
1249 incidents or costly investigations into the most appropriate remedial
1250 actions.
1252 As Managed Security Services Providers (MSSP's) are confronted with
1253 these data quality issues, but also possess a wealth of cross-product
1254 security data that enables innovative solutions, we decided to use
1255 the Symantec MSS service for the past 3 months. The Symantec MSS
1256 service monitors over 100 security products from a wide variety of
1257 security vendors for hundreds of enterprise class customers from all
1258 verticals.
1260 We selected the subset of customers using the service that deploy
1261 both network and endpoint security products to determine which types
1262 of security incidents were most likely to be detected by endpoint
1263 products vs. network products. In doing so, we were particularly
1264 interested in identifying which categories of incidents are detected
1265 by endpoint products and not network products, and vice versa. Thus,
1266 we examined prevalent categories of incidents for which the only
1267 actionable security alerts were predominantly produced by one type of
1268 security product and not the other. To do so, we extracted all
1269 security incidents detected by Symantec MSS on behalf of hundreds of
1270 customers that deploy both network and endpoint security products,
1271 over a three-month period from December 2018 through the end of
1272 February 2019. We acknowledge that some attacks might have been
1273 blocked by the first product and therefore have never been seen by
1274 the next security solution, which influences the final numbers.
1276 With this in mind, we could identify incidents based on:
1278 +------------+------------------------------------------------------+
1279 | Severity | 4 - Emergency, 3 - Critical, 2 - Warning, 1 - |
1280 | | Informational |
1281 +------------+------------------------------------------------------+
1282 | Incident | Malicious Code, Deception Activity, Improper Usage, |
1283 | Category | Investigation, etc. |
1284 | | |
1285 | Incident | Trojan Horse Infection, Suspicious DGA Activity, |
1286 | Type | Suspicious Traffic, Suspicious URL Activity, |
1287 | | Backdoor infection, etc. |
1288 | | |
1289 | # network | Amount of network only security incidents |
1290 | incidents | |
1291 | | |
1292 | # all | What is the total amount of incidents on all |
1293 | incidents | security solutions |
1294 | | |
1295 | Percentage | Percentage of network security only incidents |
1296 +------------+------------------------------------------------------+
1298 We ended up with
1300 o Hundreds of thousands of security incidents
1302 o which we could categorize in 275 incident types by category and
1303 severity (triplets Severity-Category-Type)
1305 o out of which we searched how many incidents of each type were
1306 detected by a network security product and missed by deployed
1307 endpoint security products at least 75% of the time or vice versa
1309 11.1. Endpoint only incidents
1311 The categories of incidents that are detected primarily by endpoint
1312 security products are fairly intuitive. They consist primarily of
1313 detections of file-based threats and detection of malicious behaviors
1314 through monitoring of system and network behavior at the process
1315 level. The most prevalent of these behavioral detections include
1316 detections of suspicious URLs based on heuristics and blacklists of
1317 IP addresses or domain names. Since most of these alerts are not
1318 corroborated by network products, it seems probable that the
1319 blacklists associated with network products tend to be more focused
1320 on attacks while host-based intrusion prevention system alerts focus
1321 more on malware command and control traffic. Most other behavioral
1322 detections at the endpoint provide alerts based on system behavior
1323 that is deemed dangerous and symptomatic of malicious intent by a
1324 malicious or infected process. The highest severity incidents
1325 detected on endpoints are instances of post-compromise outbound
1326 network behavior that are symptomatic of command and control
1327 communications traffic, but these did not show up as being primarily
1328 detected by endpoint products as they are frequently corroborated by
1329 network-based alerts.
1331 11.2. Security incidents detected primarily by network security
1332 products
1334 Perhaps less intuitive are the results of examining categories of
1335 security incidents that are detected primarily by network security
1336 products and only rarely corroborated by endpoint security products.
1337 Below we provide details regarding incident categories for which a
1338 network security product produced a detection and for which there
1339 were no actionable endpoint alerts for at least 75% of the incidents
1340 in the category.
1342 In our study we found 32 incident type, category, and severity
1343 triplets of this type. The following categories critical incident
1344 types were reported by MSS customers, and we discuss each in turn in
1345 decreasing order of prevalence:
1347 11.2.1. Unauthorized external vulnerability scans
1349 Perhaps unsurprisingly, unauthorized external attempts to scan
1350 corporate resources for vulnerabilities and other purposes are
1351 detected in large volumes by a broad variety of network-focused
1352 security products. 79% of incidents of this type were detected by
1353 network security products with critical-severity alerts, these
1354 security incident detections are not accompanied by any actionable
1355 endpoint alerts, despite the fact that endpoint security products are
1356 deployed by these enterprises. This category of threats encompasses
1357 a broad variety of attacks, the most prevalent of which are the
1358 following: Horizontal scans, SQL injection attacks, password
1359 disclosure vulnerabilities, directory traversal attacks, and
1360 blacklist hits. Of these categories of detections, horizontal scans
1361 stand out as the category of detection that endpoint-security
1362 products are least likely to detect on their own.
1364 11.2.2. Unauthorized internal vulnerability scans
1366 Unauthorized internal vulnerability scans, though less frequent, are
1367 more alarming, as they are likely to represent possible post-
1368 compromise activity. We note that the Managed Security Service works
1369 with its customers to maintain lists of devices that are authorized
1370 to perform internal vulnerability scans, and their activity is
1371 reported separately at a lower levels of incident severity. 89% of
1372 detected unauthorized internal vulnerability scans are detected by
1373 network products without any corroborating actionable alerts from
1374 endpoint security products. As compared to unauthorized external
1375 scan incidents, internal hosts that perform vulnerability scans are
1376 far more active and the fraction of alerts that detect horizontal
1377 scans is higher, representing half of the total alerts generated.
1378 Alerts focused on Network-Behavior Anomaly Detection also appear for
1379 internal hosts.
1381 11.2.3. Malware downloads resulting in exposed endpoints
1383 This category of threats is generally detected by network security
1384 appliances. Despite these enterprises being purchasers of endpoint
1385 security products, 76% of the incidents detected by the network
1386 security products do not show a corresponding alert by an endpoint
1387 security product. A broad variety of network appliances contributed
1388 to the detection of a diverse collection of malware samples.
1390 11.2.4. Exploit kit infections
1392 This category of infections represents instances in which the
1393 customer's machines are exposed to exploit kits. These threats were
1394 detected by network appliances that extract suspicious URLs from
1395 network traffic taps and use a combination of sandbox technology and
1396 blacklists to identify websites that deploy a variety of exploit kits
1397 that were not being caught by endpoint security products. In this
1398 three month time period, the most prevalent categories of exploit
1399 kits detected involved redirections to the Magnitude exploit kit and
1400 exploit kits associated with phishing scams and attempts to expose
1401 users to fake Anti-Virus warnings and tools. A breakdown of the
1402 results is included below:
1404 +---------------------+-----------------------+
1405 | Severity | 3 - Critical |
1406 +---------------------+-----------------------+
1407 | Incident Category | Malicious Code |
1408 | | |
1409 | Incident Type | Exploit Kit Infection |
1410 | | |
1411 | # network incidents | 26 |
1412 | | |
1413 | # all incidents | 26 |
1414 | | |
1415 | Percentage | 100% |
1416 +---------------------+-----------------------+
1418 The network security product that detected these incidents produced
1419 the following alerts:
1421 o Advanced Malware Payloads
1423 o Exploit.Kit.FakeAV
1425 o Exploit.Kit.Magnitude
1427 o Exploit.Kit.MagnitudeRedirect
1429 o Exploit.Kit.PhishScams
1431 o HTMLMagnitudeLandingPage
1433 11.2.5. Attacks against servers
1435 In addition to detecting the aforementioned critical security
1436 incident categories, network security devices frequently detect a
1437 broad variety of attacks against servers that usually lack
1438 corroboration at the endpoint. Most server attacks are not matched
1439 by endpoint protection alerts: 62% are unmatched for critical
1440 incidents, and 88% are unmatched as lower severity incidents. This
1441 category of incidents is the most prevalent category of incidents
1442 detected primarily by network products, but they are usually rated
1443 lower in severity than the aforementioned classes of alerts as they
1444 are very commonplace. Even when these alerts are corroborated by
1445 endpoint protection alerts, the endpoint alerts are often low in
1446 severity, as in the case of file-based threats that appear to have
1447 been blocked or successfully cleaned up by an Anti-Virus solution.
1448 The challenge in taking action against server attacks is that it can
1449 be difficult to assess which of these attacks were successful in
1450 causing actual damage, and for this reason, for the fraction of
1451 server attacks that demonstrate corroborating endpoint security
1452 alerts, even if of low severity, should be examined. It is
1453 interesting to note the cooperative role played by both network and
1454 endpoint security devices in these instances.
1456 12. Regulatory Considerations
1458 This section will briefly look at the regulatory landscape and
1459 develop a specific view on the impact on endpoints with the goal to
1460 see what we might be able to learn.
1462 Legal requirements, compliance, regulatory frameworks and mandatory
1463 reporting are no longer separate from any security evaluation,
1464 process or requirement within an organisation, enterprise system or
1465 intranet. It is essential to look at the technical and regulatory
1466 approaches together. This section will look at two examples of legal
1467 requirements and guidance:
1469 (1) IoT security (2) Network infrastructure
1471 This section is by no means complete, but it does a discussion on
1472 this aspect of endpoint and ecosystem regulation.
1474 12.1. IoT Security
1476 IoT security regulation is emerging in the form of voluntary
1477 frameworks and self-assessments that relate to endpoint security
1478 issues.. These frameworks focus first on the end point, or mobile
1479 device, in the IoT environment and then on the holistic ecosystem
1480 itself.
1482 In 2017 the National Institute of Standards and Technology released
1483 its draft IoT Cybersecurity Framework based on consultations and
1484 interviews with all stakeholders over several years previously
1485 [NISTIOTP]. Some of the themes which emerged was the need for IoT
1486 governance, assessment frameworks, review of all aspects of the IoT
1487 ecosystem and a process for coordinated vulnerability disclosure
1488 inside an organisation. As evidenced by the 2018 Endpoint Protection
1489 and Response Survey by SANS, only 47% of organisations know that
1490 their endpoints have been breached and a further 20% are unsure
1491 [EPRSANS]. So a systemic approach from NIST was welcomed and the
1492 NIST framework became the gold standard for national IoT security
1493 frameworks.
1495 Other IoT security frameworks include the Singapore IoT Cyber
1496 Security Guide from January 2019 and the UK's Secure by Design or The
1497 Government's Code of Practice for Consumer Internet of Things (IoT)
1498 Security for manufacturers, with guidance for consumers on smart
1499 devices at home [IMDAIOTG], [SBDGOVUK]. Once again both look at
1500 securing the IoT device or endpoint, but also security for the entire
1501 value chain of the IoT system. The Singapore framework makes the
1502 point about the entire system clear, "Similar to any system, an IoT
1503 system is as secure as its weakest link. It is thus important to
1504 ensure that proper security considerations and measures are put in
1505 place for both the implementation and operational stages of the
1506 deployment of any IoT system." [IMDAIOTG] Finally, the IoT Security
1507 Foundation, the GSMA and the Internet Society have all released their
1508 own frameworks for IoT security. All have similar characteristics
1509 which focus on the entire value chain and ecosystem, but also on
1510 vulnerability disclosure and checklist assessments. What makes each
1511 of these approaches slightly different is the differing perspectives
1512 of the organization advocating it. The GSMA is the mobile trade
1513 association and so it focuses on mobile devices while the Internet
1514 Society focuses on the Internet ecosystem and a multistakeholder
1515 approach. Systematically underpinning all the frameworks is the
1516 holistic approach with voluntary best practices and implementation
1517 based on the needs of the user or organisation adopting the framework
1518 [IOTSFCF], [GSMAIOT], [ISTRUST].
1520 12.2. Network infrastructure
1522 In Europe, the Network and Information Security Directive, which was
1523 passed in July 2016, require implementation by each European member
1524 state with a threefold aim. First, to put into place a national
1525 strategy for network and infrastructure security including best
1526 practices, guidelines, training and stakeholder consultations.
1527 Second, to coordinate national CSIRTs with CERT-EU and third to
1528 provide incident control and response systems for critical
1529 infrastructure and digital services [EURLEX]. This Directive
1530 demonstrates the importance give across the EU to network resilience
1531 and incident reporting. While securing the endpoint is acknowledged,
1532 the focus is on ensuring the security of European interoperable
1533 networks. In short, the importance of the security of the network
1534 including incident response shows that it isn't only the endpoints
1535 that should be the focus of the regulation and legal frameworks.
1537 12.3. Auditing and Assessment
1539 This section will talk about other risk assessment and auditing
1540 regulatory requirements beyond the NIS directive.
1542 One example of risk assessment as a regulatory requirement is the New
1543 York State law 23 NYCRR 500 of the Regulations of the Superintendent
1544 of Financial Services (Cybersecurity Requirements for Financial
1545 Services Companies). Among the requirements, audit, risk assessment
1546 and risk reporting are included like,
1547 (2) include audit trails designed to detect and respond to
1548 Cybersecurity Events that have a reasonable likelihood of materially
1549 harming any material part of the normal operations of the Covered
1550 Entity. [NYCYBER]
1552 12.4. Privacy Considerations
1554 We may consider a specific focus on privacy in the future.
1556 13. Human Rights Considerations
1558 This section may develop a specific view of requirements, limits and
1559 constraints coming from Human Rights perspective on endpoint
1560 security.
1562 14. Security Considerations
1564 This document is about Security Considerations
1566 15. IANA Considerations
1568 This document has no actions for IANA
1570 16. Informative References
1572 [ADAPTURE]
1573 Cullen, T., "Limits of endpoint only", July 2017,
1574 .
1577 [AMT1] Khandelwal, S., "Explained - How Intel AMT Vulnerability
1578 Allows to Hack Computers Remotely", May 2017,
1579 .
1582 [AMT2] Symantec, ., "Web Attack Intel AMT Privilege Escalation
1583 CVE-2017-5689", 2017,
1584 .
1587 [ATTACK] "MITRE ATT&CK", n.d., .
1589 [BOT] Marinho, R., "Exploring a P2P transient botnet - From
1590 Discovery to Enumeration", July 2017,
1591 .
1594 [CANDID1] Wueest, C., "How my TV got infected with ransomware and
1595 what you can learn about it", November 2015,
1596 .
1599 [CANDID2] Dickson, B., "Millions of smart TVs are vulnerable to
1600 hackers", February 2014,
1601 .
1603 [CAPEC] "MITRE CAPEC", n.d.,
1604 .
1606 [ENISA] ENISA, ., "Baseline Security Recommendations for IoT in
1607 the context of Critical Information Infrastructures",
1608 November 2017, .
1611 [EPPEDR] Redscan, ., "EPP and EDR - What's the difference?", June
1612 2018, .
1615 [EPPGUIDE]
1616 "IT Pro's Guide to Endpoint Protection", n.d.,
1617 .
1620 [EPPSECURITY]
1621 Hunt, J., "Advantages and Disadvantages of Three Top
1622 Endpoint Security Vendors", n.d.,
1623 .
1626 [EPRSANS] Neely, L., "Endpoint Protection and Response A SANS
1627 Survey", June 2018, .
1630 [ERICSSON]
1631 Ericsson, ., "Internet of Things forecast", n.d.,
1632 .
1635 [EURLEX] EUP, ., "Directive (EU) 2016/1148", July 2016,
1636 .
1639 [FLAMER] Symantec, ., "W32.Flamer Microsoft Windows Update Man-in-
1640 the-Middle", June 2012,
1641 .
1644 [GARTNERIOT]
1645 Van der Meulen, R., "Gartner Says 8.4 Billion Connected
1646 Things Will be in Use in 2017, Up 31 percent from 2016",
1647 February 2017, .
1651 [GARTNERREPORT]
1652 Crotty, J., "New Gartner Report Redefines Endpoints
1653 Protection for 2018", January 2018,
1654 .
1657 [GSMAIOT] GSMA, ., "GSMA IoT Security Guidelines and Assessment",
1658 n.d., .
1661 [HSTODAY] Hstoday, ., "Layered Approach Critical to Effective
1662 Endpoint Protection", October 2016,
1663 .
1667 [IMDAIOTG]
1668 IMDA, ., "IMDA IoT Cyber Security Guide", January 2019,
1669 .
1675 [IOTPATCHING]
1676 Rogers, D., "Handling vulnerabilities as an IoT vendor",
1677 December 2018, .
1681 [IOTSFCF] IoTSF, ., "IoT Security Compliance Framework", December
1682 2018, .
1685 [ISTRUST] ISOC, ., "Internet of Things (IoT) Trust Framework v2.5",
1686 May 2018,
1687 .
1690 [LOJAX] ESET, ., "LoJax First UEFI rootkit found in the wild,
1691 courtesy of the Sednit group", September 2018,
1692 .
1695 [LOTLSYMC]
1696 Wueest, C., "Living off the land and fileless attack
1697 techniques", July 2017,
1698 .
1702 [MIRAI1] Symantec, ., "Mirai, what you need to know about the
1703 botnet behind recent major DDoS attacks", October 2016,
1704 .
1707 [MIRAI2] Krebsonsecurity, ., "19 Mirai Botnet Authors Avoid Jail
1708 Time", September 2018,
1709 .
1711 [MONEYBALL]
1712 Roundy, K., "Predicting Cyber Threats with Virtual
1713 Security Products. ACSAC", 2017,
1714 .
1717 [NDSSPATCH]
1718 Caballero, J., "Mind Your Own Business A Longitudinal
1719 Study of Threats and Vulnerabilities in Enterprises",
1720 February 2019, .
1724 [NETTODAY]
1725 Dix, J., "Layered Security Defenses What layer is most
1726 critical network or endpoint", July 2011,
1727 .
1731 [NINESIGNS]
1732 Smith, K., "9 signs your endpoint security isn't working",
1733 May 2017, .
1736 [NISTIOTP]
1737 NIST, ., "NIST Cybersecurity for IoT Program", November
1738 2016, .
1741 [NYCYBER] NYCRR, ., "See 3 NYCRR 500 of the Regulations of the
1742 Superintendent of Financial Services (Cybersecurity
1743 Requirements for Financial Services Companies)", n.d.,
1744 .
1747 [OWASP] OWASP, ., "Defense in depth definition", August 2015,
1748 .
1750 [PDDoS] Seals, T., "Pulse-Wave DDoS Attacks Mark a New Tactics in
1751 Q2", October 2017, .
1754 [SBDGOVUK]
1755 UK, GOV., "Secure by Design", February 2019,
1756 .
1759 [SGX1] Claburn, T., "Intel SGX safe room easily trashed by white-
1760 hat hacking marauders Enclave malware demoed", February
1761 2019, .
1764 [SGX2] Cimpanu, C., "Researchers hide malware in Intel SGX
1765 enclaves", February 2019, .
1768 [SQL] Cobb, M., "SQL injection detection tools and prevention
1769 strategies", November 2009,
1770 .
1773 [STATISTA1]
1774 Statista, ., "Internet of Things (IoT) connected devices
1775 installed base worldwide from 2015 to 2025 (in billions)",
1776 n.d., .
1779 [STATISTA2]
1780 Statista, ., "Size of Internet of Things market worldwide
1781 in 2014 and 2020 by industry (in billion U.S dollars)",
1782 n.d., .
1786 [TEEP] Cam-Winget, N., "Trust Execution Environment Protocol",
1787 March 2018, .
1789 [USCERT] Michael, C., "Principles of defense-in-depth", September
1790 2005, .
1794 [ZERODAY1]
1795 McHugh, J., "Windows of Vulnerability A Case Study
1796 Analysis", 2000, .
1799 [ZERODAY2]
1800 Plattner, B., "Large-Scale Vulnerability Analysis",
1801 September 2006, .
1804 Appendix A. Contributors
1806 o Arnaud Taddei
1807 Symantec
1808 arnaud_taddei@symantec.com
1810 o Bret Jordan
1811 Symantec
1812 bret_jordan@symantec.com
1814 o Candid Wueest
1815 Symantec
1816 candid_wueest@symantec.com
1818 o Chris Larsen
1819 Symantec
1820 chris_larsen@symantec.com
1822 o Andre Engel
1823 Symantec
1824 andre_ngel@symantec.com
1826 o Kevin Roundy
1827 Symantec
1828 kevin_roundy@symantec.com
1830 o Yuqiong Sun
1831 Symantec
1832 Yuqiong_Sun@symantec.com
1834 o David Wells
1835 Symantec
1836 David_Wells@symantec.com
1838 o Dominique Lazanski
1839 Last Press Label
1840 dml@lastpresslabel.com
1842 Authors' Addresses
1844 Arnaud Taddei
1845 Symantec Corporation
1846 350 Ellis Street
1847 Mountain View CA 94043
1848 USA
1850 Email: arnaud_taddei@symantec.com
1852 Candid Wueest
1853 Symantec Corporation
1854 350 Ellis Street
1855 Mountain View CA 94043
1856 USA
1858 Email: candid_wueest@symantec.com
1860 Kevin A. Roundy
1861 Symantec Corporation
1862 350 Ellis Street
1863 Mountain View CA 94043
1864 USA
1866 Email: kevin_roundy@symantec.com
1867 Dominique Lazanski
1868 Last Press Label
1869 Flat 1, 109A Columbia Road
1870 London E2 7RL
1871 UK
1873 Email: dml@lastpresslabel.com