Deception in Information Security

coderman coderman at
Thu Jan 24 08:22:55 PST 2019

deception is becoming all the rage these days. done right, it provides a unique window into attacker intention and capability.

rather than the isolated, fabricated structures commonly used (honey-*) consider instrumenting your actual systems with traps for the nefarious:
- set browser strings showing out of date, vulnerable versions
- leave packages old, but replace with source built updates
- instrument applications with sanitizers and hardened allocators
- jail and container, to observe unexpected calls

exploits, like any other software, are fragile! slight changes in build and configuration can render even the most expensive and carefully constructed chain impotent.

some things will get through (think: logic bugs, rather than technical exploitation) so keep your most sensitive work on truly hardened systems with strong compartmentalization and attack surface reduction. (yes, Qubes is still better than a vanilla distro! it's amazing how much malware simply aborts when it finds itself in a virtualized environment. feature, not bug! :)


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Cyber Security DeceptionMohammed H. Almeshekah and Eugene H. Spafford
Abstract Our physical and digital worlds are converging at a rapid pace, putting
a lot of our valuable information in digital formats. Currently, most computer
systems’ predictable responses provide attackers with valuable information on how
to infiltrate them. In this chapter, we discuss how the use of deception can play a
prominent role in enhancing the security of current computer systems. We show
how deceptive techniques have been used in many successful computer breaches.
Phishing, social engineering, and drive-by-downloads are some prime examples.
We discuss why deception has only been used haphazardly in computer security.
Additionally, we discuss some of the unique advantages deception-based security
mechanisms bring to computer security. Finally, we present a framework where
deception can be planned and integrated into computer defenses.1 IntroductionMost data is digitized and stored in organizations’ servers, making them a valuable
target. Advanced persistent threats (APT), corporate espionage, and other forms of
attacks are continuously increasing. Companies reported 142 million unsuccessful
attacks in the first half of 2013, as reported by Fortinet [[1](]. In addition, a recent
Verizon Data Breach Investigation Report (DBIR) points out that currently deployed
protection mechanisms are not adequate to address current threats [[1](]. The report
states that 66 % of the breaches took months or years to discover, rising from 56 %
in 2012. Furthermore, 84% of these attacks only took hours or less to infiltrate
computer systems [[1](]. Moreover, the report states that only 5 % of these breaches
were detected using traditional intrusion detection systems (IDSs) while 69 % were
detected by external parties [[1](].
These numbers are only discussing attacks that were discovered. Because only
5 % of the attacks are discovered using traditional security tools, it is likely that theM.H. Almeshekah ( )
King Saud University, Riyadh, Saudi Arabia
e-mail: meshekah at
E.H. Spafford
Purdue University, West Lafayette, IN, USA
e-mail: spaf at
© Springer International Publishing Switzerland 2016
S. Jajodia et al. (eds.), Cyber Deception, DOI 10.1007/978-3-319-32699-3_2

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M.H. Almeshekah and E.H. Spaffordreality is significantly worse as there are unreported and undiscovered attacks. These
findings show that the status quo of organizations’ security posture is not enough to
address current threats.
Within computer systems, software and protocols have been written for decades
with an intent of providing useful feedback to every interaction. The original design
of these systems is structured to ease the process of error detection and correction by
informing the user about the exact reason why an interaction failed. This behavior
enhances the efforts of malfeasors by giving them information that helps them to
understand why their attack was not successful, refine their attacks and tools, and
then re-attack. As a result, these systems are helpful to attackers and guide them
throughout their attack. Meanwhile, targeted systems learn nothing about these
attempts, other than a panic in the security team. In fact, in many cases multiple
attempts that originate from the same entity are not successfully correlated.
Deception-based techniques provide significant advantages over traditional se-
curity controls. Currently, most security tools are responsive measures to attackers’
probes to previously known vulnerabilities. Whenever an attack surfaces, it is
hit hard with all preventative mechanisms at the defender’s disposal. Eventually,
persistent attackers find a vulnerability that leads to a successful infiltration by
evading the way tools detect probes or by finding new unknown vulnerabilities.
This security posture is partially driven by the assumption that “hacking-back” is
unethical, while there is a difference between the act of “attacking back” and the act
of deceiving attackers.
There is a fundamental difference in how deception-based mechanisms work in
contrast to traditional security controls. The latter usually focuses on attackers’
actions—detecting or preventing them—while the former focuses on attackers’
perceptions—manipulating them and therefore inducing adversaries to take action-
s/inactions in ways that are advantageous to targeted systems; traditional security
controls position themselves in response to attackers’ actions while deception-based
tools are positioned in prospect of such actions.1.1 DefinitionOne of the most widely accepted definitions of computer-security deception is the
one by Yuill [[2](]; Computer Deception is “Planned actions taken to mislead attackers
and to thereby cause them to take (or not take) specific actions that aid computer-
security defenses.” We adapt this definition and add “confusion” as one of goals
of using deceit (the expression of things that are not true) in computer system
protection. Therefore, the definition of defensive computer deception we will use
throughout this chapter is
Definition 1. Deception is “Planned actions taken to mislead and/or confuse
attackers and to thereby cause them to take (or not take) specific actions that aid
computer-security defenses.”

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Cyber Security Deception
272 A Brief HistoryThroughout history, deception has evolved to find its natural place in our societies
and eventually our technical systems. Deception and decoy-based mechanisms have
been used in security for more than two decades in mechanisms such as honeypots
and honeytokens. An early example of how deception was used to attribute and
study attackers can be seen in the work of Cheswick in his well-known paper “An
Evening with Berferd” [[3](]. He discusses how he interacted with an attacker in real
time providing him with fabricated responses. Two of the earliest documented uses
of deceptive techniques for computer security are in the work of Cliff Stoll in his
book “The Cuckoo’s Egg” [[4](] and the work of Spafford in his own lab [[5](]. The
Deception Toolkit (DTK),[1]( by Fred Cohen in 1997 was one of the first
publicly available tools to use deception for the purpose of computer defenses.
In late 1990s, “honeypots”—“a component that provides its value by being
attacked by an adversary” i.e. deceiving the attacker to interact with them—
have been used in computer security. In 2003, Spitzner published his book on
“Honeypots” discussing how they can be used to enhance computer defenses [[6](].
Following on the idea of honeypots, a proliferation of “honey-*” prefixed tools
have been proposed. Additionally, with the release of Tripwire, Kim and Spafford
suggested the use of planted files that should not be accessed by normal users, with
interesting names and/or locations and serving as bait that will trigger an alarm if
they are accessed by intruders [[7](].2.1 Honey-Based Tools2.1.1 Honeypots
Honeypots have been used in multiple security applications such as detecting and
stopping spam[2]( analyzing malware [[8](]. In addition, honeypots have been used
to secure databases [[9](]. They are starting to find their way into mobile environments
[[10](] where some interesting results have been reported [[11](].
Honeypots in the literature come in two different types: server honeypot and
client honeypot. The server honeypot is a computer system that contains no valuable
information and is designed to appear vulnerable for the goal of enticing attackers to
access them. Client honeypots are more active. These are vulnerable user agents that
troll many servers actively trying to get compromised [[12](]. When such incidents
happen, the client honeypots report the servers that are infecting users’ clients.
Honeypots have been used in computing in four main areas as we discuss in the
following paragraphs.1

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M.H. Almeshekah and E.H. SpaffordDetection
Honeypots provide an additional advantage over traditional detection mechanisms
such as Intrusion Detection Systems (IDS) and anomaly detection. First, they
generate less logging data as they are not intended to be used as part of normal
operations and thus any interaction with them is illicit. Second, the rate of false
positive is low as no one should interact with them for normal operations. Angnos-
takis et al. proposed an advanced honeypot-based detection architecture in the use
of shadow honeypots [[13](]. In their scheme they position Anomaly Detection Sensors
(ADSs) in front of the real system where a decision is made as whether to send the
request to a shadow machine or to the normal machine. The scheme attempts to
integrate honeypots with real systems by seamlessly diverting suspicious traffic to
the shadow system for further investigation. Finally, honeypots are also helpful in
detecting industry-wide attacks and outbreaks, e.g. the case of the Slammer worm
as discussed in [[14](].
Honeypots are used in prevention where they assist in slowing down the attackers
and/or deterring them. Sticky honeypots are one example of machines that utilize
unused IP address space and interact with attackers probing the network to slow
them down [[15](]. In addition, Cohen argues that by using his Deception ToolKit
(DTK) we can deter attackers confusing them and introducing risk on their side
[[16](]. However, we are not aware of any studies that investigated those claims.
Beyond the notion of enticement and traps used in honeypots, deception has been
studied from other perspectives. For example, Rowe et al. present a novel way of
using honeypots for deterrence [[17](]. They protect systems by making them look
like a honeypot and therefore deter attackers from accessing them. Their observation
stemmed from the developments of anti-honeypots techniques that employ advanced
methods to detect if the current system is a honeypot [[18](].
One of the advantages of using honeypots is that they are totally independent
systems that can be disconnected and analyzed after a successful attack on them
without hindering the functionality of the production systems. This simplifies the
task of forensic analysts as they can preserve the attacked state of the system and
extensively analyze what went wrong.

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Cyber Security Deception
Honeypots are heavily used in analyzing and researching new families of malware.
The honeynet project[3]( an “international non-profit security research organization,
dedicated to investigating the latest attacks and developing open source security
tools to improve Internet security.” For example, the HoneyComb system uses
honeypots to create unique attack signatures [[19](]. Other more specific tools, such
as dionaea,[4]( designed to capture a copy of computer malware for further
study. Furthermore, honeypots help in inferring and understanding some widespread
attacks such as Distributed Denial of Service (DDoS) [[20](].
2.1.2 Other Honey Prefixed Tools
The prefix “honey-*” has been used to refer to a wide range of techniques that
incorporate the act of deceit in them. The basic idea behind the use of the prefix
word “honey” in these techniques is that they need to entice attackers to interact
with them, i.e. fall for the bait—the “honey.” When such an interaction occurs the
value of these methods is realized.
The term honeytokens has been proposed by Spitzner [[21](] to refer to honeypots
but at a smaller granularity. Stoll used a number of files with enticing names and
distributed them in the targeted computer systems, acting as a beaconing mechanism
when they are accessed, to track down Markus Hess [[4](]. Yuill et al. coined the term
honeyfiles to refer to these files [[22](]. HoneyGen was also used to refer to tools that
are used to generate honeytokens [[23](].
Most recently, a scheme named Honeywords was proposed by Jules and Rivest
to confuse attackers when they crack a stolen hashed password file [[24](] by hiding
the real password among a list of “fake” ones. Their scheme augmenting password
databases with an additional .N
1/ fake credentials [[24](]. If the DB is stolen and
cracked, attackers are faced with N different passwords to choose from where only
one of them is the correct one. However, if they use any of the fake ones the system
triggers an alarm alerting system administrators that the DB has been cracked.2.2 Limitations of Isolated Use of DeceptionHoneypot-based tools are a valuable technique used for the detection, prevention,
and response to cyber attacks as we discuss in this chapter. Nevertheless, those
techniques suffer from the following major[.](

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M.H. Almeshekah and E.H. Spafford• As the prefix honey-* indicates, for such techniques to become useful, the
adversary needs to interact with them. Attackers and malware are increasingly
becoming sophisticated and their ability to avoid honeypots is increasing [[25](].
• Assuming we manage to lure the attacker into our honeypot, we need to be able
to continuously deceive them that they are in the real system. Chen et al. study
such a challenge and show that some malware, such as polymorphic malware,
not only detects honeypots, but also changes its behavior to deceive the honeypot
itself [[25](]. In this situation, attackers are in a position where they have the ability
to conduct counter-deception activities by behaving in a manner that is different
than how would they do in a real environment.
• To learn about attackers’ objectives and attribute them, we need them to interact
with the honeypot systems. However, with a high-interaction honeypot there is
a risk that attackers might exploit the honeypot itself and use it as a pivot point
to compromise other, more sensitive, parts of the organization’s internal systems.
Of course, with correct separation and DMZs we can alleviate the damage, but
many organizations consider the risk intolerable and simply avoid using such
• As honeypots are totally “fake systems” many tools currently exist to identify
whether the current system is a honeypot or not [[18]([,]( [25](]. This fundamental
limitation is intrinsic in their design.3 Deception as a Security TechniqueAchieving security cannot be done with single, silver-bullet solutions; instead, good
security involves a collection of mechanisms that work together to balance the cost
of securing our systems with the possible damage caused by security compromises,
and drive the success rate of attackers to the lowest possible level. In Fig. [1](, we
present a taxonomy of protection mechanisms commonly used in systems. The
diagram shows four major categories of protection mechanisms and illustrates how
they intersect achieving multiple goals.
The rationale behind having these intersecting categories is that a single layer of
security is not adequate to protect organizations so multi-level security controls are
needed [[26](]. In this model, the first goal is to deny unauthorized access and isolate
our information systems from untrusted agents. However, if adversaries succeed
in penetrating these security controls, we should have degradation and obfuscation
mechanisms in place that slow the lateral movement of attackers in penetrating our
internal systems. At the same time, this makes the extraction of information from
penetrated systems more challenging.
Even if we slow the attackers down and obfuscate our information, advanced
adversaries may explore our systems undetected. This motivates the need for a
third level of security controls that involves using means of deceit and negative
information. These techniques are designed to lead attackers astray and augment
our systems with decoys to detect stealthy adversaries. Furthermore, this deceitful
information will waste the time of the attackers and/or add risk during their

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Cyber Security Deception
Fig. 1 Taxonomy of information protection mechanismsinfiltration. The final group of mechanisms in our taxonomy is designed to attribute
attackers and give us the ability to have counter-operations. Booby-trapped software
is one example of counter-operations that can be employed.
Securing a system is an economic activity and organizations have to strike the
right balance between cost and benefits. Our taxonomy provides a holistic overview
of security controls, with an understanding of the goals of each group and how can
they interact with each other. This empowers decision makers on what and which
security controls they should deploy.

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M.H. Almeshekah and E.H. SpaffordDespite all the efforts organizations have in place, attackers might infiltrate
information systems, and operate without being detected or slowed. In addition,
persistent adversaries might infiltrat the system and passively observe for a while
to avoid being detected and/or slowed when moving on to their targets. As a result,
a deceptive layer of defense is needed to augment our systems with negative and
deceiving information to lead attackers astray. We may also significantly enhance
organizational intrusion detection capabilities by deploying detection methods using
multiple, additional facets.
Deception techniques are an integral part of human nature that is used around us
all the time. As an example of a deception widely used in sports: teams attempt to
deceive the other team into believing they are following a particular plan so as to
influence their course of action. Use of cosmetics may also be viewed as a form of
mild deception. We use white lies in conversation to hide mild lapses in etiquette. In
cybersecurity, deception and decoy-based mechanisms haven been used in security
for more than two decades in technologies such as honeypots and honeytokens.
When attackers infiltrate the system and successfully overcome traditional
detection and degradation mechanisms we would like to have the ability to not
only obfuscate our data, but also lead the attackers astray by deceiving them
and drawing their attention to other data that are false or intestinally misleading.
Furthermore, exhausting the attacker and causing frustration is also a successful
defensive outcome. This can be achieved by planting fake keys and/or using schemes
such as endless files [[5](]. These files look small on the organization servers but when
downloaded to be exfiltrated will exhaust the adversaries’ bandwidth and raise some
alarms. Moreover, with carefully designed deceiving information we can even cause
damage at the adversaries’ servers. A traditional, successful, deception technique
can be learned from the well-known story of Farewell Dossier during the cold war
where the CIA provided modified items to a Soviet spy ring. When the Soviets used
these designs thinking they are legitimate, it resulted in a major disaster affecting a
trans-Siberian pipeline.
When we inject false information we cause some confusion for the adversaries
even if they have already obtained some sensitive information; the injection of
negative information can degrade and/or devalue the correct information obtained
by adversaries. Heckman and his team, from Lockheed Martin, conducted an
experiment between a red and a blue team using some deception techniques, where
they found some interesting results [[27](]. Even after the red team successfully
attacked and infiltrate the blue system and obtained sensitive information, the blue
team injected some false information in their system that led the red team to devalue
the information they had obtained, believing that the new values were correct.
Another relationship can be observed between the last group of protection tech-
niques, namely attribution, and deception techniques. Deception-based mechanisms
are an effective way to lure attackers to expose themselves and their objectives when
we detect them accessing things and conducting unusual activities. Other tools, such
as anomaly-based IDS, have similar goals, but the advantage deception-based tools
have is that there is a clear line between normal user activities and abnormal ones.
This is because legitimate users are clearly not supposed to access this information.

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Cyber Security Deception
33This difference significantly enhances the effectiveness of deception-based security
controls and reduces the number of false-positives, as well as the size of the system’s
log file.3.1 Advantages of Using Deception in Computer DefensesReginald Jones, the British scientific military intelligence scholar, concisely articu-
lated the relationship between security and deception. He referred to security as a
“negative activity, in that you are trying to stop the flow of clues to an opponent” and
it needs its other counterpart, namely deception, to have a competitive advantage in
a conflict [[28](]. He refers to deception as the “positive counterpart to security” that
provides false clues to be fed to opponents.
By intelligently using deceptive techniques, system defenders can mislead
and/or confuse attackers, thus enhancing their defensive capabilities over time.
By exploiting attackers’ unquestioned trust of computer system responses, system
defenders can gain an edge and position themselves a step ahead of compromise
attempts. In general, deception-based security defenses bring the following unique
advantages to computer systems [[29](]
1. Increases the entropy of leaked information about targeted systems during
compromise attempts.
When a computer system is targeted, the focus is usually only on protecting
and defending it. With deception, extra defensive measures can be taken by
feeding attackers false information that will, in addition to defending the targeted
system, cause intruders to make wrong actions/inactions and draw incorrect
conclusions. With the increased spread of APT attacks and government/corporate
espionage threats such techniques can be effective.
When we inject false information we cause some confusion for the adversaries
even if they have already obtained some sensitive information; the injection
of negative information can degrade and devalue the correct information ob-
tained by adversaries. Heckman and her team, developed a tool, referred to as
“Blackjack,” that dynamically copies an internal state of a production server—
after removing sensitive information and injecting deceit—and then directs
adversaries to that instance [[27](]. Even after the red team successfully attacked
and infiltrated the blue systems and obtained sensitive information, the blue team
injected some false information in their system that led the red team to devalue
the information they had obtained, believing that the new values were correct.

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M.H. Almeshekah and E.H. Spafford2. Increases the information obtained from compromise attempts.
Many security controls are designed to create a boundary around computer
systems automatically stopping any illicit access attempts. This is becoming
increasingly challenging as such boundaries are increasingly blurring, partly as
a result of recent trends such as “consumerization”[5]([[30](]. Moreover, because
of the low cost on the adversaries’ side, and the existence of many automated
exploitation tools, attackers can continuously probe computer systems until
they find a vulnerability to infiltrate undetected. During this process, systems’
defenders learn nothing about the intruders’ targets. Ironically, this makes the
task of defending a computer system harder after every unsuccessful attack.
We conjecture that incorporating deception-based techniques can enhance our
understanding of compromise attempts using the illicit probing activity as
opportunity to enhance our understanding of the threats and, therefore, better
protect our systems over time.
3. Give defenders an edge in the OODA loop.
The OODA loop (for Observe, Orient, Decide, and Act) is a cyclic process
model, proposed by John Boyd, by which an entity reacts to an event [[31](]. The
victory in any tactical conflict requires executing this loop in a manner that
is faster than the opponent. The act of defending a computer system against
persistent attacks can be viewed as an OODA loop race between the attacker and
the defender. The winner of this conflict is the entity that executes this loop faster.
One critical advantage of deception-based defenses is that they give defenders an
edge in such a race as they actively feed adversaries deceptive information that
affects their OODA loop, more specifically the “observe” and “orient” stages
of the loop. Furthermore, slowing the adversary’s process gives defenders more
time to decide and act. This is especially crucial in the situation of surprise, which
is a common theme in digital attacks.
4. Increases the risk of attacking computer systems from the adversaries’ side.
Many current security controls focus on preventing the actions associated
with illicit attempts to access computer systems. As a result, intruders are using
this accurate negative feedback as an indication that their attempts have been
detected. Subsequently, they withdraw and use other, more stealthy, methods of
infiltration. Incorporating deceit in the design of computer systems introduces a
new possibility that adversaries need to account for; namely that they have been
detected and currently deceived. This new possibility can deter attackers who are
not willing to take the risk of being deceived, and further analyzed. In addition,
such technique gives systems’ defenders the ability to use intruders’ infiltration
attempts to their advantage by actively feeding them false information.5This term is widely used to refer to enterprises’ employees bringing their own digital devises andusing them to access the companies’ resources.

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Cyber Security Deception
353.2 Deception in the Cyber Kill-ChainThe cyber kill-chain introduced by Lockheed Martin researchers advocates an
intelligence-driven security model [[32](]. The main premise behind this model is that
for attackers to be successful they need to go through all these steps in the chain in
sequence. Breaking the chain at any step will break the attack and the earlier that
we break it the better we prevent the attackers from attacking our systems.
The cyber kill-chain model is a good framework to demonstrate the effectiveness
of incorporating deception at multiple levels in the chain. With the same underlying
principle of the kill-chain—early detection of adversaries—we argue that the earlier
we detect adversaries, the better we are at deceiving them and learning more about
their methods and techniques. We postulate that full intelligence cannot be gathered
without using some means of deception techniques.
Also, the better we know our enemies the better we can defend against them.
By using means of deception we can continuously learn about attackers at different
levels of the kill-chain and enhance our capabilities of detecting them and reducing
their abilities to attack us. This negative correlation is an interesting relationship
between our ability to detect attackers and their ability to probe our resources.
There is a consensus that we would like to be at least one step ahead of adver-
saries when they attack our systems. We argue that by intelligently incorporating
deception methods in our security models we can start achieving that. This is
because the further we enhance our abilities to detect adversaries the further ahead
of them we position ourselves. If we take an example of external network probing,
if we simply detect an attack and identify a set of IP address and domain names as
“bad,” we do not achieve much: these can be easily changed and adversaries will
become more careful not to raise an alarm the next time they probe our systems.
However, if we go one more step to attribute them by factors that are more difficult
to change it can cause greater difficulty for future attacks. For example, if we are able
to deceive attackers in manners that allow us to gather more information that allows
us to distinguish them based on fixed artifacts (such as distinctive protocol headers,
known tools and/or behavior and traits) we have a better position for defense. The
attackers will now have a less clear idea of how we are able to detect them, and
when they know, it should be more difficult for them to change these attributes.
The deployment of the cyber kill-chain was seen as fruitful for Lockheed when
they were able to detect an intruder who successfully logged into their system using
the SecurID attack [[33](]. We adopt this model with slight modification to better
reflect our additions.
Many deception techniques, such as honeypots, work in isolation and inde-
pendently of other parts of current information systems. This design decision has
been partly driven by the security risks associated with honeypots. We argue that
intelligently augmenting our systems with interacting deception-based techniques
can significantly enhance our security and gives us the ability to achieve deception
in depth. If we examine Table [1](, we can see that we can apply deception at every
stage of the cyber kill-chain, allowing us to break the chain and possibly attribute

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M.H. Almeshekah and E.H. Spafford
Table 1 Mapping deception to the kill-chain model
Cyber kill-chain phase
Artificial ports, fake sites
Weaponization and delivery
Create artificial bouncing back, sticky honeypots
Exploitation and installation
Create artificial exploitation response
Command and control (operation)
Lateral movement and persistence
HoneyAccounts, honeyFiles
Staging and exfiltration
Honeytokens, endless files, fake keysattackers. At the reconnaissance stage we can lure adversaries by creating a site
and have honey-activities that mimic a real-world organization. As an example,
an organization can subscribe with a number of cloud service providers and have
honey activities in place while monitoring any activities that signal external interest.
Another example is to address the problem of spear-phishing by creating a number
of fake persons and disseminating their information into the Internet while at
the same monitoring their contact details to detect any probing activities; some
commercial security firms currently do this.3.3 Deception and ObscurityDeception always involves two basic steps, hiding the real and showing the false.
This, at first glance, contradicts the widely believed misinterpretation of Kerckhoff’s
principle; “no security through obscurity.” A more correct English translation of
Kerckhoff’s principle is the one provided by Petitcolas in [[34](];The system must not require secrecy and can be stolen by the enemy without causing
trouble.The misinterpretation leads some security practitioners to believe that any
“obscurity” is ineffective, while this is not the case. Hiding a system from an
attacker or having a secret password does increase the work factor for the attacker—
until the deception is detected and defeated. So long as the security does not
materially depend on the obscurity, the addition of misdirection and deceit provides
an advantage. It is therefore valuable for a designer to include such mechanisms in
a comprehensive defense, with the knowledge that such mechanisms should not be
viewed as primary defenses.
In any system design there are three levels of viewing a system’s behavior and
responses to service requests [[29](]:
• Truthful. In such systems, the processes will always respond to any input with
full “honesty.” In other words, the system’s responses are always “trusted” and
accurately represent the internal state of the machine. For example, when the user

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Cyber Security Deception
37asks for a particular network port, a truthful system responds with either a real
port number or denies the request giving the specific reason of such denial.
• Naively Deceptive. In such systems, the processes attempt to deceive the
interacting user by crafting an artificial response. However, if the user knows
the deceptive behavior, e.g. by analyzing the previous deceptive response used
by the system, the deception act becomes useless and will only alert the user
that the system is trying to deceive her. For example, the system can designate
a specific port that is used for deceptive purposes. When the attacker asks for
a port, without carrying the appropriate permissions, this deceptive port is sent
• Intelligently Deceptive. In this case, the systems “deceptive behavior” is indistin-
guishable from the normal behavior even if the user has previously interacted
with the system. For example, an intelligently-deceptive system responds to
unauthorized port listening requests the same as a normal allowed request. How-
ever, extra actions are taken to monitor the port, alert the system administrators,
and/or sandbox the listening process to limit the damage if the process downloads
malicious content.3.4 Offensive DeceptionOffensively, many current, common attacks use deceptive techniques as a corner-
stone of their success. For example, phishing attacks often use two-level deceptive
techniques; they deceive users into clicking on links that appear to be coming from
legitimate sources, which take them to the second level of deception where they will
be presented with legitimate-looking websites luring them to give their credentials.
The “Nigerian 419” scams are another example of how users are deceived into
providing sensitive information with the hope of receiving a fortune later.
In many of these cases, attackers focus on deceiving users as they are usually
the most vulnerable component. Kevin Mitnick showed a number of examples in
his book, “The Art of Deception” [[35](], of how he used social engineering, i.e.,
deceptive skills to gain access to many computer systems. Trojan horses, which are
more than 30 years old, are a prime example of how deception has been used to
infiltrate systems.
Phishing, Cross-site Scripting (XSS) [[36](], and Cross-site Request Forgery
(XSRF) [[37](] are some examples of using deception. Despite more than a decade of
research by both the academic and private sectors, these problems are causing more
damage every year. XSS and XSRF have remained on the OWASP’s top ten list since
the first time they were added in 2007 [[38](]. The effectiveness of offensive deception
techniques should motivate security researchers to think of positive applications for
deception in security defenses.

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M.H. Almeshekah and E.H. Spafford4 A Framework to Integrate Deception in Computer
DefensesWe presented a framework that can be used to plan and integrate deception in
computer security defenses [[39](]. Many computer defenses that use deception were
ad-hoc attempts to incorporate deceptive elements in their design. We show how our
framework can be used to incorporate deception in many parts of a computer system
and discuss how we can use such techniques effectively. A successful deception
should present plausible alternative(s) to the truth and these should be designed to
exploit specific adversaries’ biases, as we will discuss later.
The framework discussed in this chapter is based on the general deception
model discussed by Bell and Whaley in [[40](]. There are three general phases of any
deceptive component; namely planning, implementing and integrating, and finally
monitoring and evaluating. In the following sections we discuss each one of those
phases in more detail. The framework is depicted in Fig. [3]( The Role of BiasesIn cognitive psychology a bias refers toAn inclination to judge others or interpret situations based on a personal and oftentimes
unreasonable point of view [[41](]Biases are a cornerstone component to the success of any deception-based
mechanism. The target of the deception needs to be presented with a plausible
“deceit” to successfully deceive and/or confuse him. If the target perceives this
deceit to be non-plausible she is more inclined to reject it instead of believing it,
or at least raise her suspicions about the possibility of currently being deceived.
A successful deception should exploit a bias in the attackers’ perception and provide
them with one or more plausible alternative information other than the truth.
Thompson et al. discuss four major groups of biases any analysts need to be
aware of: personal biases, cultural biases, organizational biases, and cognitive biases
[[42](]. It can be seen in Fig. [2]( the more specific the bias being exploited in a
deceptive security tool is, the less such a tool can be generalized, For example,
exploiting a number of personal biases, specific to an attacker, might not be
easily generalized to other adversaries who attack your system. However, the more
specific the choice of bias enhances the effectiveness of the deceptive component.
This is true partly because cognitive biases are well-known and adversaries might
intentionally guard themselves with an additional layer of explicit reasoning to
minimize their effects in manipulating their perceptions. In the following paragraphs
we discuss each one of these classes of biases.

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Fig. 2 Deception target
biases4.1.1 Personal Biases
Personal biases are those biases that originate from either first-hand experiences
or personal traits, as discussed by Jervis in [[43](]. These biases can be helpful
in designing deceptive components/operation; however, they are (1) harder to
obtain as they require specific knowledge of potential attackers and (2) they make
deceptive components less applicable to a wider range of attackers while becoming
more powerful against specific attackers. Personal biases have been exploited in
traditional deception operations in war, such as exploiting the arrogance of Hitler’s
administration in World War II as part of Operation Fortitude [[41](].
4.1.2 Cultural Biases
Hofstede refers to cultural biases as the “software of the mind” [[44](]. They represent
the mental and cognitive ways of thinking, perception, and action by humans
belonging to these cultures. In a study conducted by Guss and Dorner, they found
that cultures influenced the subjects’ perception, strategy development and decision
choices, even though all those subjects were presented with the same data [[45](].
Hofstede discusses six main dimensions of cultures and assigns quantitative values
to those dimensions for each culture in his website ( Also,
he associates different behavior that correlates with his measurements. Theses
dimensions are:
1. Power Distance Index (PDI)—PDI is a measure of the expectation and accep-
tance that “power is distributed unequally.” Hofstede found that cultures with
high PDI tend to have a sense of loyalty, show of strength, and preference to
in-group-person. This feature can be exploited by a deception planner focusing
on the attacker’s sense of pride to reveal himself, knowing that the attack is
originating from a high PDI culture with a show-of-strength property.
2. Individualism versus Collectivism (IVC)—A collectivist society values the
“betterment of a group” at the expense of the individual. Hofstede found that
most cultures are collectivist, i.e. with low IVC index.
3. Masculine versus Feminine (MVF)—A masculine culture is a culture where
“emotional gender roles are clearly distinct.” For example, an attacker coming

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M.H. Almeshekah and E.H. Spaffordfrom a masculine culture is more likely to discredit information and warnings
written by or addressed to a female. In this case, this bias can be exploited to
influence attackers’ behaviors.
4. Uncertainty Avoidance Cultures (UAI)—This measures the cultural response
to the unknown or the unexpected. High UAI means that this culture has a fairly
structured response to uncertainty making the attackers’ anticipation of deception
and confusion a much easier task.
5. Long-Term Orientation Versus Short-Term Orientation (LTO vs. STO)—
STO cultures usually seek immediate gratification. For example, the defender
may sacrifice information of lesser importance to deceive an attacker into
thinking that such information is of importance, in support of an over-arching
goal of protecting the most important information.
6. Indulgence versus Restraint (IVR)—This dimension characterizes cultures on
their norms of how they choose activities for leisure time and happiness.
Wirtz and Godson summarize the importance of accounting for cultures while
designing deception in the following quote; “To be successful the deceiver must
recognize the target’s perceptual context to know what (false) pictures of the world
will appear plausible” [[46](].
4.1.3 Organizational Biases
Organizational biases are of importance when designing deception for an target
within a heavily structured environment [[41](]. In such organizations there are many
keepers who have the job of analyzing information and deciding what is to be passed
to higher levels of analysts. This is one example of how organizational biases can
be used. These biases can be exploited causing important information to be marked
as less important while causing deceit to be passed to higher levels. One example of
organizational biases is uneven distribution of information led to uneven perception
and failure to anticipate the Pearl Harbor attack in 1941 by the United States [[41](].
4.1.4 Cognitive Biases
Cognitive biases are common among all humans across all cultures, personali-
ties, and organizations. They represent the “innate ways human beings perceive,
recall, and process information” [[41](]. These biases have long been studied by
many researchers around the world in many disciplines (particularly in cognitive
psychology); they are of importance to deception design as well as computing.
Tversky and Kahneman proposed three general heuristics our minds seem to
use to reduce a complex task to a simpler judgment decision—especially under
conditions of uncertainty—thus leading to some predictable biases [[47](]. These
are: representativeness, availability, and anchoring and adjustment. They defined
the representativeness heuristic as a “heuristic to evaluate the probability of an

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Cyber Security Deception
41event by the degree to which it is (i) similar in essential properties to its parent
population; and (ii) reflects the salient features of the process by which it is
generated” [[47](]. The availability heuristic is another bias that assess the likelihood
of an uncertain event by the ease with which someone can bring it to mind. Finally,
the anchoring/adjustment heuristic is a bias that causes us to make estimations closer
to the initial values we have been provided with than is otherwise warranted.
Solman presented a discussion of two reasoning systems postulated to be
common in humans: associative (system 1) and rule-based (system 2) [[48](]. System 1
is usually automatic and heuristic-based, and is usually governed by habits. System
2 is usually more logical with rules and principles. Both systems are theorized to
work simultaneously in the human brain; deception targets System 1 to achieve
more desirable reactions.
In 1994, Tversky and Koehler argued that people do not subjectively attach
probability judgments to events; instead they attach probabilities to the description
of these events [[49](]. That is, two different descriptions of the same event often lead
people to assign different probabilities to their likelihood. Moreover, the authors
postulate that the more explicit and detailed the description of the event is, the
higher the probability people assign to it. In addition, they found that unpacking the
description of the event into several disjoint components increases the probability
people attach to it. Their work provides an explanation for the errors often found
in probability assessments associated with the “conjunction fallacy” [[50](]. Tversky
and Kahneman found that people usually would give a higher probability to the
conjunction of two events, e.g. P(X and Y), than a single event, e.g. P(X) or P(Y).
They showed that humans are usually more inclined to believe a detailed story with
explicit details over a short compact one.4.2 Planning DeceptionThere are six essential steps to planning a successful deception-based defensive
component. The first, and often neglected, step is specifying exactly the strategic
goals the defender wants to achieve. Simply augmenting a computer system with
honey-like components, such as honeypots and honeyfiles, gives us a false sense
that we are using deception to lie to adversaries. It is essential to detail exactly
what are the goals of using any deception-based mechanisms. As an example, it
is significantly different to set up a honeypot for the purpose of simply capturing
malware than having a honeypot to closely monitor APT-like attacks.
After specifying the strategic goals of the deception process, we need to
specify—in the second step of the framework—how the target (attacker) should
react to the deception. This determination is critical to the long-term success of
any deceptive process. For example the work of Zhao and Mannan in [[51](] deceive
attackers launching online guessing attacks into believing that they have found a
correct username and password. The strategic goal of this deception process is to
direct an attacker to a “fake” account thus wasting their resources and monitoring

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M.H. Almeshekah and E.H. Spaffordtheir activities to learn about their objectives. It is crucial to analyze how the
target should react after the successful “fake” login. The obvious reaction is that
the attacker would continue to laterally move in the target system, attempting
further compromise. However, an alternative response is that the attacker ceases
the guessing attack and reports to its command and control that a successful
username/password pair has been found. In consideration of the second alternative
we might need to maintain the username/password pair of the fake account and keep
that account information consistent for future targeting.
Moreover, part of this second step is to specify how we desire an attacker to react
such that we may try to influence his perception and thus lead him to the desired
reaction. Continuing with the example in the previous paragraph, if we want the
attacker to login again so we have more time to monitor and setup a fake account,
we might cause an artificial network disconnection that will cause the target to login
4.2.1 Adversaries’ Biases
Deception-based defenses are useful tools that have been shown to be effective in
many human conflicts. Their effectiveness relies on the fact that they are designed to
exploit specific biases in how people think, making them appear to be plausible but
false alternatives to the hidden truth, as discussed above. These mechanisms give
defenders the ability to learn more about their attackers, reduce indirect information
leakages in their systems, and provide an advantage with regard to their defenses.
Step 3 of planning deception is to understand the attackers’ biases. As discussed
earlier, biases are a cornerstone component to the success of any deception-based
mechanisms. The deceiver needs to present a plausible deceit to successfully
deceive and/or confuse an adversary. If attackers decide that such information is
not plausible they are more inclined to reject it, or at least raise their suspicions
about the possibility of currently being deceived. When the defender determines
the strategic goal of the deception and the desired reactions by the target, he needs
to investigate the attacker’s biases to decide how best to influence the attacker’s
perception to achieve the desired reactions.
One example of using biases in developing some deceptive computer defenses
is using the “confirmation bias” to lead adversaries astray and waste their time and
resources. Confirmation bias is defined as “the seeking or interpreting of evidence
in ways that are partial to existing beliefs, expectations, or a hypothesis in hand”
[[52](]. A computer defender can use this bias in responding to a known adversarial
probing of the system’s perimeter. Traditional security defenses are intended to
detect and prevent such activity, by simply dropping such requests or actively
responding with an explicit denial. Taking this a step further by exploiting some
pre-existing expectation, i.e. the confirmation bias, we might provide a response
that the system is being taken down for some regular maintenance or as a result of
some unexpected failure. With such a response, the defender manages to prevent
illicit activity, provide a pause to consider next steps for the defender, and perhaps
waste the adversary’s time as they wait or investigate other alternatives to continue
their attacks.

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Cyber Security Deception
43Cultural biases play an important role in designing deceptive responses, as
discussed in Sect. [4.1.2]( For example, some studies found relationships between
the type of computer attacks and the culture/country from which the attack
originated [[53](].
In computing, the conjunction fallacy bias, discussed in Sect. [4.1.4](, can be
exploited by presenting the deception story as a conjunction of multiple detailed
components. For example, if deceivers want to misinform an attacker probing their
system by creating an artificial network failure, instead of simply blocking these
attempts, it is better to give a longer story. A message that says “Sorry the network
is down for some scheduled network maintenance. Please come back in three hours”
is more plausible than simply saying “The network is down” and thus more likely
to be believed.
4.2.2 Creating the Deception Story
After analyzing attackers’ biases the deceiver needs to decide exactly what compo-
nents to simulate/dissimulate; namely step 4 of the framework in Fig. [3](
In Fig. [4]( provide an overview of the different system components where
deception can be applied, exploiting the attacker’s biases to achieve the desired
reaction. Overall, deceit can be injected into the functionality and/or state of our
systems. We give a discussion of each one of these categories below and present
some examples.
System’s Decisions
We can apply deception to the different decisions any computer system makes.
As an example, Zhao and Mannan work in [[51](] apply deception at the system’s
authentication decision where they deceive adversaries by giving them access to
“fake” accounts in the cases of online guessing attacks. Another system’s decision
we can use concerns firewalls. Traditionally, we add firewall rules that prevent
specific IP addresses from interacting with our systems after detecting that they
are sources of some attacks. We consider this another form of data leakage in
accordance with the discussion of Zhao and Mannan in [[51](]. Therefore, we can
augment firewalls by applying deception to their decisions by presenting adversaries
with plausible responses other than simply denying access.
System’s Software and Services
Reconnaissance is the first stage of any attack on any computing system, as
identified in the kill-chain model [[32](]. Providing fake systems and services has been
the main focus of honeypot-based mechanisms. Honeypots discussed earlier in this
chapter are intended to provide attackers with a number of fake systems running

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M.H. Almeshekah and E.H. Spafford
Fig. 3 Framework to incorporate deception in computer security defensesfake services. Moreover, we can use deception to mask the identities of our current
existing software/services. The work of Murphy et al. in [[54](] recommended the use
of operating system obfuscation tools for Air Force computer defenses.
System’s Internal and Public Data
A honeyfile, discussed above, is an example of injecting deceit into the system’s
internal data. It can be applied to the raw data in computer systems, e.g., files and
directories, or to the administrative data that are used to make decisions and/or

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Fig. 4 Computer systems components where deception can be integrated withmonitor the system’s activities. An example applying deception to the administrative
data can be seen in the honeywords proposal [[24](]. Deceit can also be injected into
the public data about our systems. Wang et al. made the case of disseminating public
data about some “fake” personnel for the purpose of catching attacks such as spear
phishing [[55](]. Cliff Stoll did this during the story of his book [[4](]. In addition, we
note that this category also includes offline stored data such as back-ups that can be
used as a focus of deception.
System’s Activity
Different activities within a system are considered as one source of information
leakage. For example, traffic flow analysis has long been studied as a means for
attackers to deduce information [[56](]. Additionally, a system’s activity has been
used as a means of distinguishing between a “fake” and a real system [[25](]. We can
intelligently inject some data about activities into our system to influence attackers’
perception and, therefore, their reactions.
System’s Weaknesses
Adversaries probe computer systems trying to discover and then exploit any
weakness (vulnerability). Often, these adversaries come prepared with a list of
possible vulnerabilities and then try to use them until they discover something that
works. Traditional security mechanisms aid adversaries by quickly and promptly
responding back to any attempt to exploit fixed, i.e. patched, vulnerabilities with
a denial response. This response leaks information that these vulnerabilities are
known and fixed. When we inject deceit into this aspect of our systems we can
misinform adversaries by confusing them—by not giving them a definitive answer
whether the exploit has succeeded—or by deceiving them by making it appear as if
the vulnerability has been exploited.

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M.H. Almeshekah and E.H. SpaffordSystem’s Damage Assessment
This relates to the previous component; however, the focus here is to make the
attacker perceive that the damage caused is more or less than the real damage.
We may want the adversary to believe that he has caused more damage than what
has happened so as to either stop the attack or cause the attacker to become less
aggressive. This is especially important in the context of the OODA loop discussed
earlier in this chapter. We might want the adversary to believe that he has caused less
damage if we want to learn more about the attacker by prompting a more aggressive
System’s Performance
Influencing the attacker’s perception of system’s performance may put the deceiver
at an advantageous position. This has been seen in the use of sticky honeypots
and tarpits discussed at the beginning of this chapter that are intended to slow the
adversary’s probing activity. Also, tarpits have been used to throttle the spread of
network malware. In a related fashion, Somayaji et al. proposed a method to deal
with intrusions by slowing the operating system response to a series of anomalous
system calls [[57](].
System’s Configurations
Knowledge of the configuration of the defender’s systems and networks is often of
great importance to the success of the adversary’s attack. In the lateral movement
phase of the kill-chain adversarial model, attackers need to know how and where
to move to act on their targets. In the red-teaming experiment by Cohen and Koike
they deceived adversaries to attack the targeted system in a particular sequence from
a networking perspective [[58](].
After deciding which components to simulate/dissimulate, we can apply one of
Bell and Whaley’s techniques discussed in [[29](]. We give an example of how each
one of these techniques can be used in the following paragraphs.
• Using Masking—This has been used offensively where attackers hide potentially
damaging scripts in the background of the page by matching the text color with
the background color. When we apply hiding to software and services, we can
hide the fact that we are running some specific services when we detect a probing
activity. For example, when we receive an SSH connection request from a known
bad IP address we can mask our SSHd demon and respond as if the service is not
working or as if it is encountering an error.
• Using Repackaging—In several cases it might be easier to “repackage” data as
something else. In computing, repackaging has long been used to attack computer
users. The infamous cross-site scripting (XSS) attack uses this technique where

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47an attacker masks a dangerous post as harmless to steal the user’s cookies when
they view such post. Another example can be seen in the cross-site request
forgery (XSRF) attacks where an adversary deceives a user into visiting some
innocuous looking web pages that silently instruct the user’s browser to engage
in some unwanted activities. In addition, repackaging techniques are used by
botnet Trojans that repackage themselves as anti-virus software to deceive users
into installing them so an attacker can take control of their machines. From the
defensive standpoint, a repackaging act can be seen in HoneyFiles, discussed
above, that repackage themselves as normal files while acting internally as silent
alarms to system administrators when accessed.
• Using Dazzling—This is considered to be the weakest form of dissimulation,
where we confuse the targeted objects with others. An example of using dazzling
can be seen in the “honeywords” proposal [[24](]. The scheme confuses each user’s
hashed password with an extra .N
1/ hashes of other, similar, passwords
dazzling an attacker who obtains the credentials database.
• Using Mimicking—In computing, phishing attacks are a traditional example of
an unwanted deceiving login page mimicking a real website login. An attacker
takes advantage of users by deceiving them into giving up their credentials by
appearing as the real site. From a defensive perspective, we can apply mimicking
to software and services by making our system mimic the responses of a different
system, e.g., respond as if we are running a version of Windows XP while we
are running Windows 7. This will waste attackers’ resources in trying to exploit
our Windows 7 machine thinking it is Windows XP, as well as increase the
opportunity for discovery. This is seen in the work of Murphy et al. in operating
system obfuscation [[54](].
• Using Inventing—Mimicking requires the results to look like something else;
when this is not easy to achieve invention can be used instead. This technique
has seen the most research in the application of deception to computer security
defenses. Honeypots are one prominent example of inventing a number of nodes
in an organizations with the goal of deceiving an attacker that they are real
• Using Decoying—This technique is used to attract adversaries’ attention away
from the most valuable parts of a computer system. Honeypots are used, in some
cases, to deceive attackers by showing that these systems are more vulnerable
than other parts of the organization and therefore capture attackers’ attention.
This can be seen in the work of Carroll and Grosu [[59](].
After deciding which deceptive technique to use we need to analyze the patterns
attackers perceive and then apply one or more of those techniques to achieve the
desired reactions.
Deceit is an active manipulation of reality. We argue that reality can be manip-
ulated in one of three general ways, as depicted in Fig. [5]( We can manufacture
reality, alter reality, and/or hide reality. This can be applied to any one of the
components we discussed above.

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Fig. 5 Creating deceit. (a) Manipulation of reality. (b) Deception can be applied to the nature,
existence and/or value of dataIn addition, reality manipulation is not only to be applied to the existence of
the data in our systems—it can be applied to two other features of the data. As
represented in Fig. [5](, we can manipulate the reality with respect to the existence of
data, nature of the data, and/or value of the data. The existence of the data can be
manipulated not only for the present but also when the data has been created. This
can be achieved for example with the manipulation of time stamps. With regard to
the nature of the data, we can manipulate the size of the data, such as in the example
of endless files, when and why the data has been created. The value of the data can
also be manipulated. For example, log files are usually considered important data
that adversaries try to delete to cover their tracks. Making a file appear as a log file
will increase its value from the adversary’s perspective.
At this step, it is crucial to specify exactly when the deception process should
be activated. It is usually important that legitimate users’ activity should not be
hindered by the deceptive components. Optimally, the deception should only be
activated in the case of malicious interactions. However, we recognize that this may
not always be possible as the lines between legitimate and malicious activities might
be blurry. We argue that there are many defensive measures that can apply some
deceptive techniques in place of the traditional denial-based defenses that can make
these tradeoffs.
4.2.3 Feedback Channels and Risks
Deception-based defenses are not a single one-time defensive measure, as is the case
with many advanced computer defenses. It is essential to monitor these defenses,
and more importantly measure the impact they have on attackers’ perceptions
and actions. This is step 5 in the deception framework. We recognize that if an
attacker detects that he is being deceived, he can use this to his advantage to make
a counter-deception reaction. To successfully monitor such activities we need to
clearly identity the deception channels that can and should be used to monitor and
measure any adversary’s perceptions and actions.

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49In the sixth and final step before implementation and integration, we need to
consider that deception may introduce some new risks for which organizations need
to account. For example, the fact that adversaries can launch a counter-deception
operation is a new risk that needs to be analyzed. In addition, an analysis needs to
done on the effects of deception on normal users’ activities. The defender needs
to accurately identify potential risks associated with the use of such deceptive
components and ensure that residual risks are accepted and well identified.4.3 Implementing and Integrating DeceptionMany deception-based mechanisms are implemented as a separate disjoint com-
ponent from real production systems, as in the honeypot example. With the
advancement of many detection techniques used by adversaries and malware,
attackers can detect whether they are in real system or a “fake” system [[25](], and then
change behavior accordingly, as we discussed earlier in this chapter. A successful
deception operation needs to be integrated with the real operation. The honeywords
proposal [[24](] is an example of this tight integration as there is no obvious way to
distinguish between a real and a “fake” password.4.4 Monitoring and Evaluating the Use of DeceptionIdentifying and monitoring the feedback channels is critical to the success of
any deception operation/component. Hesketh discussed three general categories of
signals that can be used to know whether a deception was successful or not [[60](]:
1. The target acts in the wrong time and/or place.
2. The target acts in a way that is wasteful of his resources.
3. The target delays acting or stop acting at all.
Defenders need to monitor all the feedback channels identified in step 5 of the
framework. We note that there are usually three general outputs from the use of
any deceptive components. The adversary might (1) believe it, where the defender
usually sees one of the three signs of a successful deception highlighted above,
(2) suspect it or (3) disbelieve it. When an attacker suspects that a deceptive
component is being used, we should make the decision whether to increase the level
of deception or stop the deceptive component to avoid exposure. Often deception
can be enhanced by presenting more (and perhaps, true) information that makes
the deception story more plausible. This can be included as a feedback loop in
the framework. This observation should be analyzed by the defender to review his
analysis of the attacker’s biases, (i.e., step 3), and the methodology used to create
the deceit (i.e., step 4). Furthermore, the deceiver might employ multiple levels of
deception based on the interaction with the attacker during the attack.

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M.H. Almeshekah and E.H. SpaffordWhen an attacker disbelieves the presented deceit we need to have an active
monitoring and a detailed plan of action. This should be part the sixth step
of planning in our framework where risks are assessed. In addition, during our
discussions with security practitioners many have indicated that some attackers
often act aggressively when they realize that they have been deceived. This can
be one of the signals that is used during the monitoring stage to measure attackers’
reaction of the deceptive component. In addition, this behavior can be used as one
of the biases to be exploited by other deceptive mechanisms that may focus on
deceiving the attacker about the system’s damage assessment.Acknowledgements The material in the chapter is derived from [[29](]. Portions of this work
were supported by National Science Foundation Grant EAGER-1548114, by Northrop Grumman
Corporation (NGCRC), and by sponsors of the Center for Education and Research in Information
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