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Cyber Security Deception

Mohammed 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 Introduction

Most 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 the

M.H. Almeshekah ( )

King Saud University, Riyadh, Saudi Arabia

e-mail: meshekah@ksu.edu.sa

E.H. Spafford

Purdue University, West Lafayette, IN, USA

e-mail: spaf@purdue.edu

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. Spafford

reality 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 Definition

One 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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2 A Brief History

Throughout 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 developed 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 Tools

2.1.1 Honeypots

Honeypots have been used in multiple security applications such as detecting and

stopping spam2 and 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.

1http://www.all.net/dtk/.

2http://www.projecthoneypot.org.

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M.H. Almeshekah and E.H. Spafford

Detection

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].

Prevention

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].

Response

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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Research

Honeypots are heavily used in analyzing and researching new families of malware.

The honeynet project3 is 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 are 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 Deception

Honeypot-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 limitations:

3www.honeynet.org.

4http://dionaea.carnivore.it/.

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• 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

tools.

• 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 Technique

Achieving 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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Fig. 1 Taxonomy of information protection mechanisms

infiltration. 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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Despite 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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This 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 Defenses

Reginald 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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2. 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 and

using them to access the companies’ resources.

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3.2 Deception in the Cyber Kill-Chain

The 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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Table 1 Mapping deception to the kill-chain model

Cyber kill-chain phase

Deception

Reconnaissance

Artificial ports, fake sites

Weaponization and delivery

Create artificial bouncing back, sticky honeypots

Exploitation and installation

Create artificial exploitation response

Command and control (operation)

Honeypot

Lateral movement and persistence

HoneyAccounts, honeyFiles

Staging and exfiltration

Honeytokens, endless files, fake keys

attackers. 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 Obscurity

Deception 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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asks 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

back.

• 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 Deception

Offensively, 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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4 A Framework to Integrate Deception in Computer

Defenses

We 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.

4.1 The Role of Biases

In cognitive psychology a bias refers to

An 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 that 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

biases

4.1.1 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 (geerte-hofstede.com). 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. Spafford

from 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

event 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 Deception

There 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. Spafford

their 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

again.

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

Cultural 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 we 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 defenses

fake 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 with

monitor 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. Spafford

System’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

attack.

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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Cyber Security Deception

an 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

systems.

• 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. 5a. 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 data

In 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. 5b, 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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Cyber Security Deception

In 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 Deception

Many 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 Deception

Identifying 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. Spafford

When 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

Assurance and Security (CERIAS).

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