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Internet Engineering Task Force                                J. Fabini
Internet-Draft                                                   TU Wien
Intended status: Informational                          November 4, 2019
Expires: May 7, 2020

         Communication Network Perspective on Malware Lifecycle


   Today's systems, networks, and protocols are complex and include
   unknown vulnerabilities that adversaries can exploit.  The large-
   scale deployment of network security protocols establishes an
   additional threat by implementing a substrate for hidden
   communications like covert or subliminal channels.  The resulting
   ecosystem builds a convenient platform for malicious, automated
   software (malware) to infiltrate critical infrastructures, to
   gradually infect large parts of the system and to coordinate
   distributed malware operation.

   Based on the observation that malware depends on network
   communications to discover, propagate, coordinate, and unleash its
   functionality, this memo recommends methods to identify potential
   interfaces and interactions between malware and protocols.  It
   proposes a generic malware lifecycle model that defines a set of
   generic malware states and possible transitions between these states.
   Coordinated activities of distributed malware can be mapped to state
   transitions in malware instances, supporting the identification of
   (potentially hidden) network communication as a trigger for actions
   and hints on protocols that enabled the communication.  Eventually,
   the proposed model aims at supporting the identification of
   architectures, protocols, interfaces, and points in time that a)
   either inhibit hidden malware communication or b) allow for optimized
   detection of anomalies as main prerequisite for timely

   While earlier work focused on protecting single hosts from
   compromise, this memo adopts a holistic view and considers the health
   of the overall networked system to be of highest priority.  Presuming
   vulnerable systems, we stress that components or subsystems must be
   disconnected on suspected infection in an attempt to continue (even
   partial) operation of the overall (non-infected) system after the
   disconnect.  Containment - the isolation of an infected subsystem -
   becomes an essential security feature in the context of critical
   infrastructures that influences on deployed protocols, interfaces and

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Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
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   Internet-Drafts are draft documents valid for a maximum of six months
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   This Internet-Draft will expire on May 7, 2020.

Copyright Notice

   Copyright (c) 2019 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   (https://trustee.ietf.org/license-info) in effect on the date of
   publication of this document.  Please review these documents
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   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
     1.1.  Requirements Language . . . . . . . . . . . . . . . . . .   4
   2.  Generic Malware Lifecycle . . . . . . . . . . . . . . . . . .   4
     2.1.  Access  . . . . . . . . . . . . . . . . . . . . . . . . .   6
     2.2.  Infection . . . . . . . . . . . . . . . . . . . . . . . .   6
     2.3.  Discovery . . . . . . . . . . . . . . . . . . . . . . . .   7
     2.4.  Propagation . . . . . . . . . . . . . . . . . . . . . . .   7
     2.5.  Control . . . . . . . . . . . . . . . . . . . . . . . . .   8
     2.6.  Trigger . . . . . . . . . . . . . . . . . . . . . . . . .   8
     2.7.  Attack  . . . . . . . . . . . . . . . . . . . . . . . . .   8
     2.8.  Cleanup . . . . . . . . . . . . . . . . . . . . . . . . .   9
   3.  Mapping the Lifecycle Model to Real Malware . . . . . . . . .   9
     3.1.  Case study: Stuxnet . . . . . . . . . . . . . . . . . . .  10
       3.1.1.  Access  . . . . . . . . . . . . . . . . . . . . . . .  11

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       3.1.2.  Infection . . . . . . . . . . . . . . . . . . . . . .  11
       3.1.3.  Discovery . . . . . . . . . . . . . . . . . . . . . .  11
       3.1.4.  Propagation . . . . . . . . . . . . . . . . . . . . .  11
       3.1.5.  Control . . . . . . . . . . . . . . . . . . . . . . .  12
       3.1.6.  Trigger . . . . . . . . . . . . . . . . . . . . . . .  12
       3.1.7.  Attack  . . . . . . . . . . . . . . . . . . . . . . .  12
       3.1.8.  Cleanup . . . . . . . . . . . . . . . . . . . . . . .  12
       3.1.9.  Discussion: Stuxnet . . . . . . . . . . . . . . . . .  12
   4.  Future work . . . . . . . . . . . . . . . . . . . . . . . . .  13
   5.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  13
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  13
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .  14
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  14
     8.1.  Normative References  . . . . . . . . . . . . . . . . . .  14
     8.2.  Informative References  . . . . . . . . . . . . . . . . .  14
   Author's Address  . . . . . . . . . . . . . . . . . . . . . . . .  15

1.  Introduction

   A central guideline of the IETF security area's activity focus is
   summarized in RFC 3552 [RFC3552]: "Protecting against an attack when
   one of the end-systems has been compromised is extraordinarily
   difficult".  This statement is still valid today but must be seen in
   a historical context: in times of monolithic systems, the main goal
   of security is (or was) to protect one's own networked end system
   (PC, server) against compromise.  This implies a worst case scenario
   and "game over" in case of a system compromise.  In a distributed
   context, one single compromised system can be fatal whenever relying
   on a chain of trust, which is a common security policy within closed
   (corporate or enterprise) networks.

   However, architectures and protocols have evolved.  Emerging critical
   infrastructures consist of ensembles of hundreds, thousands or tens
   of thousands of identical networked systems like for instance smart
   meters or other Internet of Things (IoT) devices.  These systems all
   run identical software and identical firmware on top of identical
   hardware, all of them being potentially subject to identical
   vulnerabilities.  Likewise, most personal computers that are
   connected to the Internet run one of a few operating system
   alternatives, including Microsoft Windows, Apple MacOS, or various
   Linux distributions.  Portable software and common Application
   Programming Interfaces (APIs) increase the likelihood that one
   vulnerability affects multiple platforms.

   When viewing a system as a complex set of components and relations
   (Rechtin [CBCS]), there are cases when vital system functions can be
   performed even in the case when some subsystems (components or links)
   have been compromised.  Therefore, today's security concepts and

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   research must support (a) identification and (b) containment, i.e.,
   isolation, of compromised subsystems at an architectural and protocol
   level.  It is important to note that these requirements *extend* (and
   by no means contradict) the requirements stated in RFC 3552 [RFC3552]
   with respect to the importance of protecting systems against

   On this purpose, this memo proposes to enlarge the scope of systems
   security, starting from two main prerequisites, namely that (a) any
   system (single end node, component, link) is vulnerable and (b)
   malware must communicate to propagate, to discover and to coordinate
   its distributed instances.  Section 2 proposes a generic malware
   lifecycle model consisting of malware states and transitions.  This
   generic state diagram is subsequently mapped to existing malware
   implementations to infer on malware communication needs, as well as
   on potential interfaces and protocols that malware may use for
   discovery, infection, propagation, and control through available
   network paths.  By monitoring these interfaces, systems can detect
   patterns of - potentially hidden - communications as an anomalous
   component of the network traffic.  Subsequent analysis of available
   architectures, interfaces, and protocols can help in identifying
   anomalous communications and stopping it in order to prevent malware
   from propagation and execution.

   We consider the identification of systematic and design shortcomings
   of architectures and protocols with respect to hidden communications
   to be an essential component of the security-by-design concept.  A
   first step is the definition of metrics and methods that can assess
   the degree to which protocols under investigation support -- or
   prevent -- hidden communications.  The ability to evaluate protocols
   and choose the ones that are proven to be covert-channel free enables
   system architects to close existing gaps for hidden malware

   Todo: the terms used in this memo should be eventually aligned to

1.1.  Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   document are to be interpreted as described in RFC 2119 [RFC2119].

2.  Generic Malware Lifecycle

   The state diagram depicted in Figure 1 illustrates a generic malware
   lifecycle model.  A graphical representation of the diagram along
   with a detailed description can be found in the original publication

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   [GML] or its pre-published version at

   Generic Stages of Malware Lifecycle.

   +==============+    +==============+
   | +----------+ |    +=======+======+   +==================+
   | |Scan:     | |            |          | INFECTION        |
   | |Blind,    | |            v          | +--------------+ |
   | |Topology, | |   +========+======+   | |Exploit:      | |
   | |Passive,..| |   |   ACCESS      +<--+ |Vulnerability,| |
   | +----------+ +<--+ +-----------+ |   | |Zero-day,     | |
   +==============+   | |Physical,  | +-->+ |Payload, ...  | |
                |     | |Network,   | |   | +--------------+ |
                v     | |Passive,   | |   +==================+
   +==============+   | |Persistent,| |
   | PROPAGATION  +-->+ |...        | |
   | +----------+ |   | +-----------+ |
   | | lateral, | |   |               |    +================+
   | | vertical,| |   |               +--> | ATTACK         |
   | | ...      | |   |               |    | +------------+ |
   | +----------+ |   |               |    | |Disruption, | |
   +==============+   |               |    | |Destruction,| |
              +-------+               |    | |Theft,      | |
              |       +===============+    | |Extortion,  | |
              v        ^                   | |Repurpose,  | |
   +===========+       | +=============+   | |...         | |
   | CONTROL   +-------+ | TRIGGER     +-->+ +------------+ |
   | +-------+ |         | +---------+ |   +================+
   | |C&C,   | |         | |External,| |           |
   | |Update,| |         | |Internal,| |           v
   | |Module,| |         | |...      | |      +=========+
   | |...    | |         | +---------+ +----->+ CLEANUP |
   | +-------+ |         +=============+      +=========+

   <--------------------------> <--------------------------->
        NETWORK DOMAIN                   HOST DOMAIN

   An extended graphical representation of this diagram along with
   detailed descriptions can be found in [GML].

                                 Figure 1

   Malware activity in Figure 1 revolves around the concept of access to
   abstract resources.  Essential from an defender's monitoring
   perspective is that, depending on their implementation and target,

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   malware variants differ substantially in their use of communication
   networks.  Common to many recent malware is that it encrypts
   communication, attempts to obfuscate it as legitimate traffic, and/or
   uses hidden communication channels to stay unobserved.
   Aggressiveness and "noise" that malware generates while propagating,
   infecting and attacking differs substantially between malware types.

   This is why this memo focuses on evaluating protocols, interfaces and
   architectures with respect to their ability to inhibit or support
   hidden communications.  The proposed generic lifecycle model can
   identify the malware's need for communication to trigger state
   changes. ( Internal: provide hints to anomaly detection systems?
   estimated amount of data as an order of magnitude: transferring a
   malware update or additional malware modules requires more data
   transfer than a single command. )

   The following subsections discuss briefly the generic stages of
   malware lifecycle in line with [GML].

2.1.  Access

   Starting point of malware operation is the so-called patient zero,
   denoting a device or method that triggers the initial infection
   within the system under observation.  Examples for access options
   include, but are not limited to physical access (e.g., through a
   compromised USB stick inserted into a computer, through hard drive
   replacement or through starting from a temporary boot device),
   network access (e.g., as part of existing connections, or through a
   hidden communication channel), application access (e.g., by sending a
   legitimate email with compromised payload), or persistent access (for
   instance an intentional or unintentional backdoor that is installed
   by firmware or BIOS).

   The patient zero may depend on qualified (human) support to bypass
   existing security barriers and gain access to the system.  This may
   be, e.g., a staff member plugging a compromised USB stick into a
   computer to infiltrate an air-gapped system, or an employee of the
   computer manufacturer who adds a backdoor to the computer BIOS,
   firmware or software.  Once it has gained access to the target
   system, the malware can start its operation.

   Todo: Extend, discuss options.

2.2.  Infection

   Having gained (temporary) access to the system, malware depends on
   system vulnerabilities to support its attempt to infect the system
   and install itself persistently.  Examples include the exploit of

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   backdoors, zero-day vulnerabilities or execution of a malicious email

   Todo: Extend, discuss options.

2.3.  Discovery

   Once the local system is infected, malware has several options.  The
   most common malware strategy is to first discover new potential
   victims that are reachable via the communication network.
   Alternatives for discovery differ in terms of communication verbosity
   and range from blind scans to passive monitoring of incoming network
   connections and many variants in between.  Blind scans are the most
   aggressive but also the most verbose variant of discovery, malware
   actively scanning ranges of IPv4 or IPv6 addresses like, e.g., the
   current subnet or all IPv4 addresses.  In typical networks monitoring
   devices can easily detect these blind scans because of the high
   volume of additional illegitimate traffic.  Adding some more
   intelligence to the discovery process results in targeted scans to
   decrease the amount of traffic that is needed for probing.  Examples
   include the support for distributed scan lists that record already
   scanned (and infected) devices, or a prioritization of the scan
   process to prefer system-critical devices like, e.g., the standard
   gateway.  Most stealthy and most difficult to detect is malware that
   monitors passively its local network interfaces on incoming and
   outgoing traffic to infer on the network topology and potential
   targets.  However, this stealthiness comes at the cost of reduced
   malware propagation speed and is typical for complex attack patterns.

   It is worth mentioning that the supported IP address version has
   substantial impact on the discovery strategy that malware may use or
   prefer.  Whenever targeting IPv4 addresses, distributed malware can
   scan the entire Internet within reasonable time.  The large address
   space of IPv6 and the resulting sparse population of subnets will
   likely result in malware to prefer targeted active scans or passive
   scanning for the discovery process.

   Todo: Extend, discuss options.

2.4.  Propagation

   Following the discovery of a potential victim, malware attempts to
   propagate over existing communication channels to gain access to
   these victims and install new instances of itself in the network.

   Todo: Extend, discuss options.

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2.5.  Control

   All presented malware activities or state changes happen either
   autonomously, which is typical for early malware variants, or guided
   by some command & control infrastructures that recent malware
   variants prefer to allow for later malware modification and
   coordinated attacks.  Examples of the latter variant include malware
   that supports remotely controlled updates, loading of new modules and
   distributed C&C structures.  Such functionality facilitates the
   update of encryption keys, communication patterns and functionality,
   as well as the support for new communication protocols.  Eventually,
   this functionality enables offerings business models of "malware as a
   service": botnet owners may operate infrastructures of compromised
   devices that customers can rent and use to execute their custom-
   tailored malicious code.

   Todo: Extend, discuss options.

2.6.  Trigger

   Triggers are essential for supporting the coordination of
   functionality in distributed malware instances, typical example being
   the launch of a coordinated DDoS attack.  Explicit control
   communication (command) is one option for an external trigger, other
   less suspicious options include the setting of conditions that
   distributed malware instances can observe.  Examples include timers
   (some malware variants implementing explicit time synchronization
   with dedicated time servers for improved accuracy) but also
   availability of specific servers at specific domain names, etc.

   Internal triggers are typically hard-coded into the malware or its
   modules and support it in targeting and focusing its attacks.  These
   triggers can, for instance, control malware to launch its attacks on
   specific hardware- or software systems only, or can limit its actions
   to specific IP address ranges and/or DNS domains.

   Todo: Extend, discuss options.

2.7.  Attack

   Once successfully propagated, malware can start its damaging
   functionality that ranges from destruction and disruption to theft or

   Todo: Extend, discuss options.

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2.8.  Cleanup

   Recent malware variants focusing on stealthy operation include hidden
   communication and cleanup functionality to remove themselve from
   infected systems.  The cleanup starts either on completing the attack
   or on external triggers after accomplishing their goal.

   Todo: Extend, discuss options.

3.  Mapping the Lifecycle Model to Real Malware

   This section maps the known behavior of well-studied, prototypical
   malware variants to the Malware Lifecycle Model.  Eventually, this
   mapping aims at identifying malware communication needs and
   behavioral patterns that automated processes can use to discover
   unknown malware.

   Central observation with respect to the Malware Lifecycle Model's
   applicability is that malware has huge incentives to communicate, and
   that monitoring devices can detect this communication as anomaly.  In
   particular, network communication is a key component for malware to
   unleash its full destructive potential.  Infecting systems remotely
   and automating and coordinating their distributed activities using
   network communications brings huge benefits to malware authors.  Most
   notably, being physically located in distinct geographical,
   jurisdictional, and/or legislational regions supports networked
   operations while minimizing the risk of being prosecuted for the
   results of these actions.

   Bridging the air gap to an isolated system is conditioned by physical
   access to the system.  Options include access to the system or to
   parts of it, either during the manufacturing process (e.g., by
   compromising a computer's BIOS and adding a backdoor) or later on,
   during installation or operation (e.g., by inserting a compromised
   USB drive into the system).  From a malware author's perspective, the
   physical access alternative has severe drawbacks.  First, the need
   for physical access may leave traces that help in identifying the
   originator.  Second, the lack of updates and coordination: malware
   must be fully functional at the time of first infection, updates for
   it depending on recurring physical access to the system.  However,
   even in the case of air-gapped systems malware may subsequently
   attempt to discover and infect locally connected systems (as
   exhibited for instance by Stuxnet).  These communication attempts may
   be monitored and detected.

   Summarizing, the main incentives for malware to communicate include
   the following:

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   o  Network-based malware coordination and control: the closer
      coordinated distributed malware instances can act, the higher the
      potential severity of their aggregated actions (for instance in
      the case of DDoS attacks).  Malware may use coordination to reduce
      network traffic, too (for example by maintaining scan lists when
      scanning for new victims).

   o  Network-based update: the complexity and sophistication of today's
      malware increases the effort for its programming.  This drives the
      trend for modular malware that can install a minimum persistent
      foothold, update itself and can load novel functionality on demand
      as additional modules.  Malware update can support malware authors
      by protecting their assets in the case of malware identification
      and/or takeover attempts by competing organizations.  In such
      cases, malware updates can support in the modifications of keys,
      change of encryption algorithms, use of novel obfuscation methods,

   o  Network-based discovery of potential infection targets and
      propagation: Scanning for infection candidates and propagation
      range among the two most verbose activities of today's malware.
      Worth noting is that specific malware functionality is typically
      related to malware size, i.e., data volume that the malware must
      transfer.  Depending on the implementation, malware can decide to
      transfer its entire body at propagation time or install a tiny
      foothold during propagation that subsequently loads the required
      modules.  The data pattern that monitoring devices can identify
      differs for these two alternatives: the self-carried malware will
      be visible in monitoring logs only once, when transferring a large
      amount of data.  The modular variant consists of several smaller
      data transfers.

   o  tbc...

   The remainder of this section presents prototypical case studies of
   existing malware variants, the mapping of their behavior to malware
   lifecycle model stages and how the lifecycle model can support in
   their detection.  Tables 1-4 of [GML] compare and discuss features
   and peculiarities of various malware variants in more detail.  Future
   versions of this draft are planned to structure and extend the
   malware communication aspects that these tables summarize, eventually
   building the base for a generic malware detection framework.

3.1.  Case study: Stuxnet

   Stuxnet [Stuxnet] is a computer worm that was reported for the first
   time in June 2010.  The effort associated with the design and
   implementation of Stuxnet was substantial, pointing to nation states

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   or intelligence services as authors.  This speculation is backed by
   Stuxnet's stealthy behavior and targeted attack against Siemens
   Simatic S7 Supervisory Control and Data Acquisition (SCADA)
   Industrial Control Systems (ICS), eventually aimed at causing
   physical damage.  The following subsections map the known Stuxnet
   behavioral and communication patterns to the generic Malware
   Lifecycle model stages and transitionsl.

3.1.1.  Access

   The initial Stuxnet access (patient zero) targets air-gapped systems.
   It uses the autostart functionality of Microsoft Windows 32 bit
   operating system variants on inserting a USB stick.  As soon as the
   first system has been infected, Stuxnet attempts to discover and
   access other computers within the same LAN.  Whereas the initial
   infection (USB drive autostart) can not be captured by the Lifecycle
   Model, the access to other computers within the LAN can be monitored
   within the network traffic.

3.1.2.  Infection

   Stuxnet exploits several zero-day vulnerabilities that were unknown
   by the time of its release and allowed for privilege escalation on
   several Microsoft Windows 32 bit operating system variants.  In
   addition, Stuxnet made use of two stolen certificates to sign its
   drivers.  The infection includes installation of dedicated RPC
   servers and -clients and peer-to-peer clients for communication with
   other infected Stuxnet instances within the same LAN, as well as
   infection of connected network shares.  Local infection and
   installation is not in the scope of the Lifecycle Model, whereas the
   infection of network shares may lead to unexpected network traffic
   and monitored network anomalies.

3.1.3.  Discovery

   Following an initial infection, Stuxnet scans the local network for
   potential, previously uninfected targets.  Stuxnet also uses specific
   domains to probe for Internet connectivity.  All of these network
   scan operations are typical and can be monitored and detected.

3.1.4.  Propagation

   Whenever Stuxnet identifies uninfected targets in the local network
   with Siemens Step7 software installed, it propagates and attempts to
   infect these PCs.  Otherwise it enters a dormant mode.

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3.1.5.  Control

   Stuxnet instances within the same network use peer-to-peer RPC calls
   and encryption to update each other.  This method allows one single
   USB drive infection to update distributed Stuxnet instances in air-
   gapped systems.  Whenever Internet access is available, Stuxnet
   contacts command and control servers using encrypted communication to
   receive updates, additional features, and instructions.  These update
   RPC calls and the traffic to command and control servers can be
   identified by network monitoring systems as anomalies.

3.1.6.  Trigger

   Stuxnet installation is conditioned by software (Siemens Step7)
   software to be installed on, and/or Siemens PLCs being connected to
   the Windows-based system.  The Lifecycle Model can not capture these
   triggers as they are proprietary to the malware and do not involve
   network communication.

3.1.7.  Attack

   Once installed on a system that controls a PLC, Stuxnet acts as a
   man-in-the-middle.  Faulty commands, aimed to cause physical damage,
   are sent to the PLC, and forged PLC response codes are forwarded by
   Stuxnet back to the controlling application to pretend correct
   operation.  Complex (cross-layer) monitoring systems, featuring
   sensors inside the PLC, could identify the mismatch between the
   commands sent by the controlling application and the commands
   received by the PLC.  Likewise, system log correlation with network
   traffic data could reveal anomalous behavior.

3.1.8.  Cleanup

   Stuxnet stores several encrypted copies of itself on infected
   systems.  Whereas cleanup on the host system should be feasible,
   Stuxnet can not delete the malicious code that has been sent to the
   PLC.  Therefore, Stuxnet will leave traces that may identify its

3.1.9.  Discussion: Stuxnet

   An analysis of Stuxnet reveals communication patterns that can be
   matched to specific stages of the Malware Lifecycle Model.  Depending
   on the specific network architecture and on the type of systems
   connected to the network under observation, these communications may
   appear more or less anomalous.  In Internet of Things (IoT) networks
   where automated machine-to-machine communications predominate, the
   type of communication originated by Stuxnet will be highly visible.

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   The more human-triggered network communications are present in the
   observed traffic, the more difficult the anomaly detection becomes,

   However, a word of warning is due: Stuxnet incorporates technology
   that was state-of-the-art more than ten years ago.  Evolutions of
   Stuxnet like Duqu and Duqu2, but also recent malware variants like
   Gauss, BlackEnergy3, AdWind or Locky show that multi-layer
   obfuscation and encryption will become the standard for advanced
   malware.  Moreover, malware like, e.g., Regin passively monitors the
   actual network traffic to select the least suspicious communication
   protocol as VPN tunnel for its command and control traffic.
   Therefore, network monitoring and subsequent anomaly detection
   systems will be challenged to identify anomalies in encrypted and
   obfuscated network traffic.

4.  Future work

   This draft aims at defining the basic framework that advanced anomaly
   detection methods will build upon.  Plans and ongoing work include
   the definition of metrics and methodologies to rate malware
   communications, protocols, and interfaces to applications.  As an
   example a malware's adopted scanning strategy is commonly related to
   its propagation speed.  On one hand, aggressive probing by a malware
   discovers a higher number of potential victims within a shorter time,
   increasing the malware's speed and likelihood of propagation.  The
   cost of this propagation speed is an increased scanning traffic that
   results in malware activity being detectable through network
   monitoring.  On the other hand, passive listening malware may spend
   long periods of time unobserved in a system, monitoring and learning
   its environment while waiting for activation through potentially
   hidden communication channels.  Discovery of such dormant persistent
   threats depends, therefore, on detection of highly sporadic, hidden
   activation signals in almost real-time.

5.  Acknowledgements

   Thanks to Kirsty P., Sage B., and Tanja Zseby for their comments that
   helped substantially in scoping, structuring and wording the initial
   version of this draft.

6.  IANA Considerations

   This memo includes no request to IANA.

   All drafts are required to have an IANA considerations section (see
   the update of RFC 2434 [I-D.narten-iana-considerations-rfc2434bis]
   for a guide).  If the draft does not require IANA to do anything, the
   section contains an explicit statement that this is the case (as

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   above).  If there are no requirements for IANA, the section will be
   removed during conversion into an RFC by the RFC Editor.

7.  Security Considerations

   All drafts are required to have a security considerations section.
   See RFC 3552 [RFC3552] for a guide.

8.  References

8.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,

   [RFC3552]  Rescorla, E. and B. Korver, "Guidelines for Writing RFC
              Text on Security Considerations", BCP 72, RFC 3552,
              DOI 10.17487/RFC3552, July 2003,

8.2.  Informative References

   [CBCS]     Rechtin, E., "Systems Architecting: Creating and Building
              Complex Systems", Prentice Hall ISBN-13: 978-0138803452,
              1991, 352 pages, 1991.

   [GML]      Eder-Neuhauser, P., Zseby, T., Fabini, J., and G. Vormayr,
              "Cyber Attack Models for Smart Grid Environments",
              Elsevier Sustainable Energy, Grids and Networks Volume 12,
              2017, pp 10-29, December 2017.

              Pre-published version available for download at

              McFadden, M., "Endpoint Taxonomy for CLESS", draft-
              mcfadden-smart-endpoint-taxonomy-for-cless-00 (work in
              progress), July 2019.

              Narten, T. and H. Alvestrand, "Guidelines for Writing an
              IANA Considerations Section in RFCs", draft-narten-iana-
              considerations-rfc2434bis-09 (work in progress), March

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   [Stuxnet]  Falliere, N., O Murchu, L., and E. Chien, "W32.Stuxnet
              Dossier", February 2011.


Author's Address

   Joachim Fabini
   TU Wien
   Gusshausstrasse 25/E389
   Vienna  1040

   Phone: +43 1 58801 38813
   Email: Joachim.Fabini@tuwien.ac.at

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