Now that cyberspace is a warfare domain like air, land, sea,
and space, military leadership is thinking about how defense and offense can be
applied in a virtual environment. Even though cyberspace is evolving and consistently
changing with the mass influx of internet-connected devices coming online daily,
there are no Department of Defense (DOD) or commercial virtual wargame simulations
used to cover basic cyberspace operations for planning purposes. One of the biggest
challenges in Defensive Cyberspace Operations (DCO) is analysts' ability to understand,
interpret, and act on the massive amounts of data that we use on our networks. The
purpose of this research and experiment is to bridge that gap.
During the course of a few months, we, the authors, experimented
on modeling cyberwar tactics as a wargame to prepare leadership and operators to
succeed in cyberspace operations. Our ability to customize and propose realistic
scenarios to Soar Technologies is an important factor in wargaming. Not every person
will have the same skill level or base knowledge due to their role in the organization.
With the emergence of cyber warfare, the concept of deception needs to be revisited.
The deception was never really considered in cyberwar planning, and the knowledge
gained from a wargame is indicative of the possibilities of how a conflict might
evolve and the causes of the outcome. Using deception within cyberspace provides
an additional level of protection to defenders. It enhances the defender's rate
of success while limiting the attacker's ability to disrupt activities.
Cyberwar games are proven to be useful for a broad range of
purposes. Cyberspace wargames are helpful for planning purposes because they simulate
a military operation between opposing forces using procedures, data, and rules to
simulate actual conditions. The benefit of a wargame is it allows military leaders
and operators to go through realistic virtual simulations without real-world penalties.
Educational institutions and corporations are also conducting wargames due to their
benefits. These games allow corporations to test their processes, train employees,
or identify unforeseen vulnerabilities. There are unexpected scenarios in wargames,
just as in actual warfare. Seeing and experiencing the unthinkable in a wargame
physically and mentally prepares an agent, student, or player to respond efficiently
in real-life. Wargames give agents, operators, and students hands-on practical experience
either as a cyber defender or attacker.
The simulated experiment in this research involves many runs
with random choices in each run. The simulations within the Soar Technologies developed
tool offers a randomization level for agents to get a different outcome each time
the simulation is played. This stochastic testing is useful for developing both
offensive and defensive tactics. They can be viewed as tools and benefit a dynamic
environment by simulating players or agents to explore concepts better and make
decisions. Our expectation is the simulation of cyberspace threat scenarios will
strengthen existing cybersecurity programs and provide an engaging and practical
application of cyberspace operations for cyberspace doctrine, theory, and planning.
Wargames are executed with established rules within a structured
framework and have been a role-playing method to anticipate, plan, understand, and
prepare for war. Wargames support learning objectives; they teach and reinforce
training of specific tasks or maneuvers. Emulating planned changes in architecture
and strategy while exposing them to a cyberattack in the wargaming environment is
beneficial. An organization can simulate the impacts and effects of modifications
under consideration before dedicating time and effort to implementing them in their
environment. These measures grant an opportunity to identify threats incurred with
new capabilities and approaches rather than learning them after deployment in the
real world. The DOD, corporations, and educational institutions use wargames to
simulate problem-solving, creative thinking, and decision-making when applied to
real-world situations. These elements are what the attacker and defender agents
in the simulated commercial tool seek to accomplish.
As a team of two for this experiment, our preliminary process
was to create the cyber wargame foundation with as much detail about the expansion
plan for the simulated commercial tool. The analysis, design, development, implementation,
and evaluation (ADDIE) model was used as a guideline during the thesis proposal
stage. We set the framework with initial mechanics and dynamics for the simulated
commercial tool, understanding that not all elements within the framework will remain
the same during the experiment's research and initial design phase. Chapter II
references previous experiments on cyberspace deception, honey pots, game theory,
artificial intelligence, and attack modeling. Following this, in collaboration with
Soar Technologies, Chapter III describes our experiments' methodology, including
tested scenarios. The attacker and defenders' actions will be measured from offensive
strategies, defensive strategies, situational awareness, and time stamps for each
scenario. Chapter IV breaks down the results from the proposed scenarios. Chapter
V discusses considerations and recommendations for future work.
Successful network defense is becoming harder to accomplish
within today's cyberspace because of the increasing sophistication of attacks, the
number of new vulnerabilities discovered, and the number of devices requiring Internet
connectivity. Network defense must be increasingly automated. One useful form of
automated response could be implementing cyberspace deception within a network.
Automated deception can allow defenders to conceal true-value targets while encouraging
attackers to go after low-value targets (Gartzke & Lindsay, 2015). It provides
the defenders more time to successfully identify attacks and figure out a way to
fight future attacks. This method allows a strategic advantage for defenders over
their attackers (Park & Kim, 2019).
Penetration testing or “red teaming” can test implemented
network deceptions. It can show the network and the system's weaknesses, and it
can identify vulnerable or exploitable systems that require remediation (Randhawa
et al., 2018). This type of testing is completed by an organization different from
the targeted network owner to provide an unbiased test. An example software to support
this test is Trogdor (Randhawa et al., 2018), an automated system that uses a model
and critical-node analysis. It compiles a visual picture of the defenders and vulnerable
resources within the network. Trogdor could also provide feedback to the defender
about the network's vulnerabilities on both the real network devices and the deceptive
ones. This software makes deceptive elements appear more realistic.
Traditional deception taxonomy practices are still applicable
to cyberspace (Rowe and Rothstein, 2004). Some taxonomies are most effective when
referring to cyber defense and offense. Using object-based offensive deception is
common and useful for the attacker (Rowe & Rrushi, 2016). Some of the examples
of this are messages, attachments to messages, and the data. Spear-phishing is one
of the most common and effective methods of using messages as a form of deception
that attackers use.
Along with messages, there can also be attachments for these
messages. That is an efficient way for an attacker to conceal malicious software
by hiding it within an attachment that appeals to the user. The final example is
the data itself that is used. Data itself can be overlooked but can be malicious
on its own (Rowe and Rrushi, 2016). Accomplished using malformed packets to compromise
the target's device; this can also be effective when compromising software.
Effective defensive deception examples are using camouflage
and fakes within the network. Camouflage is mainly used for offensive purposes,
but it has great value on the defensive side. An intelligent way to use camouflage
is hiding honeypots within the defender's network (Rowe and Rrushi, 2016). This
action is something that attackers wish to avoid so, being able to effectively hide
a defender's honeypot can give the defender an edge.
Using fake documents or devices is an effective way for the
defender to protect information and throw attackers off the trail. One way to use
this is by planting fake user information in files to bait the attackers, providing
alerts to defenders showing them potential malicious activity occurring on the network
(Rowe and Rrushi, 2016).
Setting up fake network devices provides a similar effect
as the fake files, except that network devices are being accessed. This setup could
give the defender more time to react to an attacker. Even though both offensive
and defensive cyberspace deception methods are similar to each other, they are different
enough to affect each side positively.
Attackers use cyber deception as a tool to mask reconnaissance
and infiltrate networks while remaining hidden from defenders. Cyber deception strategies
for the defenders include misinformation and disinformation to sabotage the early
stage of attack reconnaissance. A project used game theory to reason about a cyberattack
scenario where a defender used lightweight decoys to hide and defend real hosts
(Major et al., 2019). The defender and attacker played a game with resources consisting
of real and decoy systems, possible actions for each player, and a method for defining
and evaluating individual player strategies. This work included multiple game trees
and an explicit representation of each player's knowledge of the game structure
and payoffs.
Other previous work tested a deceptive response framework
that categorizes the types of response used against intruders and displays how intrusion
deception has its place on the framework (Goh, 2007). Another thesis put together
tools and technologies such as Snort, VMWARE, and honeypots in a testbed open to
attacks from the Internet (Chong and Koh, 2018). The results showed that attackers
had interesting reactions to deceptions. Another article proposed a deep-learning
method to conduct automated cyber deceptive defenses (Matthew, 2020).
Deceptive responses can be incorporated into network defenses
at the application layer by adapting to Web traffic. One project did a controlled
human-subject cyber deception experiment to examine the value of human interactions
and teamwork in red team cognitive and behavioral testing on a large scale (Bruggen
et al., 2019). This research concluded that red team members' cyber and psychological
deception might be more effective on an operational network; these deceptive efforts
provide confusion through complexities and anomalies (Bruggen et al., 2019).
Decoys deployed within a network can potentially entrap the
attackers and divert their attacks from the assets that most impact the organization.
A decoy is something that is extended on a network to mislead an attacker. A decoy
in cyberspace is a decoy that exists within the network to trap the attacker. One
project introduced a new hybrid decoy architecture that involved moving decoys (Sun
et al., 2019). In this configuration, front-end decoys would constrain the attackers
and forward the malicious commands to decoy servers, providing the attackers with
plausible appearances from their attacks. This project showed that such decoys require
very little overhead.
Fake documents can also provide deception in cyberspace. They
can be disinformation or misleading but correct information. Mixing real documents
with these fake ones can provide defenders an early warning of potential data exfiltration.
One method to generate believable fake text documents uses a genetic algorithm to
develop the plausible text. Experiments showed that the algorithm successfully created
fakes that appear to be legitimate documents. Another idea is to confuse people
trying to steal code by generating code that looks similar but has no function (Park
and Stolfo, 2012).
Honeypots receive attacks and deceive attackers into thinking
that they are accessing a real system while the honeypot is studying the attack
pattern. Honeypots can detect new attacks and previously unknown vulnerabilities
based on an attacker's behavior. One project implemented deception techniques such
as fake files, defensive camouflage, delays, and false excuses in a Web honeypot
built with SNARE and TANNER software and an SSH honeypot built with Cowrie software
(Chong and Koh, 2018). The results showed that most attackers performed only vulnerability
scanning and fingerprinting of honeypots. They did not respond to customized deception
and were primarily non-interactive.
The effectiveness of two free honeypot tools in real networks
was analyzed in an experiment by varying their location, virtualization, and the
effects of adding more deception to them (Rowe et al., 2015). The experiment tested
and evaluated a Web honeypot tool, Glastopf, and an SSH honeypot tool Kippo. The
results showed that including deception in the Web honeypot generated more interest
by attackers in additional linked Web pages and interactive features. For comparison,
the experiment examined log files of a legitimate website www.cmand.org.
Game theory provides tools for a framework of deception and
allows the attacker and defender to simulate strategies. Game theory is the study
where choices produce outcomes using agents to affect multiple results (Ross, 2019).
In a game model, defenders use security methods to secure the network, and attackers
try to get around those defenses. Both defender and attacker actions can be assigned
costs. Costs are often site-specific because each organization has its unique information
assets, security policies, and risk factors. A measure of how well the attacker
won the game defines the difference between the total cost of the attack and the
defense's total cost.
Deception games are examples of imperfect information stochastic
games that have been examined in the literature. These game models are beneficial
for both attackers and defenders because they help provide real-life situations
in a simulated environment that can help provide information on how both sides can
perform better and is where minimax game theory comes into play. This game theory
is used to minimize the worst-case potential loss by having the agents consider
the opponent's response to their strategy and select the best method that gives
the payoff as enormous as possible (Strategies of play, n.d.).
Modeling attacks enables simulation and testing of them. One
project-built attack models using deep learning with numerical simulations to verify
accuracy (Najada, 2018). Another study generated an agent taxonomy using topological
data analysis; it caused an agent taxonomy by analyzing simulation outputs (Swarup,
2019). The Malicious Activity Simulation Tool (MAST) is a scalable, flexible, and
interoperable architecture for training specialists in Cyber Security (Swiatocha,
2018).
Attack modeling needs to include methods to allow movement
throughout the network to establish footholds for malicious activities (“lateral
movement”) (Bai et al., 2019). This activity leaves footprints on hosts as well
as the network. The defender can detect these movements and implement countermeasures.
Defenders can make decoys look like legitimate network assets to confuse the attacker
(Amin et al., 2020). Previous indicators of compromise can help network defenders
identify and prevent this activity. One way is by finding the shortest paths by
biased random walks to identify lateral movement (Wilkens et al., 2019). Cyber deception
in honeypots can help detect lateral movement within networks since anyone accessing
them is suspicious.
Artificial Intelligence (AI) is considered the intelligence
displayed by machines in how they behave like humans, such as speech recognition,
decision-making, visual perception, and translation between languages. Machines
use it for problem-solving or learning. Artificial Intelligence (AI) technologies
can enhance security by defending against intrusions at various network layers.
AI can enhance the network's security by collecting data from logs, records, and
alerts and use this information to correlate events on the network (Using artificial
intelligence in cybersecurity, 2020). Being able to link the events occurring efficiently
and effectively on a network is challenging to accomplish. While SIEM, security
information, and event management devices can provide a place for defenders to correlate
events. AI, however, does this seamlessly and will not miss any correlations. Additionally,
AI learns from previous events and actions, enhancing the overall security the more
it works.
An example of AI enhancing security is a program that can
defend itself from various network attacks, intrusion detection with several multitasking
processes (Anitha et al., 2016). A software model was developed to represent, capture,
and learn to recognize attacks. AI technologies can process large amounts of data
to identify patterns in the data; AI methods can be used in various cyber defense
phases (Trifonov, 2020). Another project analyzed cyber-physical systems (CPS) with
AI concepts, compared them to CPS modeling approaches, and suggested AI based on
reinforcement learning. Also, a framework was created to analyze AI-based cyberattacks
for smart grids (Kaloudi, 2020).
Machine learning is a subarea of AI that trains machines to
learn, usually by identifying patterns. Cyber threats advance yearly in sophistication
and automation, making automated learning about them desirable. If conventional
security measures rely on the manual analysis of attack development and security
incidents, timely protection from these threats cannot be provided, making systems
unprotected for extended periods (Rieck, 2011). Machine learning could provide much
faster protection.
Reinforcement learning is a field within machine learning
and can be used in many ways. Two iterative reinforcement learning algorithms for
the attacker and defender allowed them to confront one another (Zhu, 2014). To maximize
the notion of cumulative reward, Intelligent agents take actions in an environment.
A project used reinforcement learning to provide an optimal defense policy in a
more accurate and timelier way (Feng, 2017). Reinforcement learning was proven to
cope with increasingly complex cybersecurity problems (Nguyen, 2020).
We collaborated with Soar Technology Inc. (SoarTech) to use
their proprietary tool, Cyberspace Course of Action Tool (CCAT). CCAT simulates
deception within a computer network and permits the testing of security measures.
In particular, it can test the impact that deception has within a network. This
provides actionable insights and helps support the cyberspace decision-making process.
In our experiments with CCAT, we proposed three realistic scenarios. We used actions
developed from previous experiments (Green, 2020). The deception effectiveness was
then measured using a set of metrics. The metrics utilized were based on a cost
and reward value discussed later in this chapter.
The CCAT is a proprietary tool developed by SoarTech and was
designed as a cyber wargame between attackers and defenders. There are various actions
that both the attacker and defender can choose depending on the specific situation.
The scenarios are designed with network assets and nodes to simulate a real-life
network. Empirical game-theoretic analysis (EGTA) was used with reinforcement learning
to train agent behavior based on runs. During training, the agent becomes less likely
to choose an action that has been seen to make it more costly to achieve its objectives
and more likely to choose an action that has been seen to make it less costly to
achieve its objectives.
The network map can be seen in Figure 1 and is a simulated
local-area network with segmentation and security. This is the network map that
was used for our experiments. It has three simulated local-area subnetworks (VLANs).
These contain simulated servers, workstations, a firewall, and other network devices
commonly found within military networks. Also, front-end servers support Web, mail,
and applications that access the backend servers for those specific tasks. Two simulated
Program of Record (POR) servers are within the server VLAN, two domain controllers,
two Web database servers, two file servers, and one router with the application
and mail backend servers. Figure 2 is the defender map that shows the flow of response
actions based on the attack experienced, and figures 3 through 5 are the attack
map that shows the flow an attacker can take depending on the specific attack chosen
(Green, 2020).
Figure 1.
Network Map: Source: Green (2020).
Figure 2.
Defender Map. Source: Green (2020).
Figure 3.
Attack Map (Part 1). Source: Green (2020).
Figure 4.
Attack Map (Part 2). Source: Green (2020).
Figure 5.
Attack Map (Part 5). Source: Green (2020).
The first scenario tested included a delayed effect, which
tested the attacker's perceptiveness. An example of such an effect is a user clicking
on a phishing link after the attacker sends a phishing email. This scenario's subsequent
attacker's actions could be to exfiltrate data or destroy the server. The defender
could use actions to disable the links and attachments that were sent. The attack
map is represented in figures 6 through 8.
Figure 6.
Attack Map for Proposed Scenario 1 (Part 1)
Figure 7.
Attack Map for Proposed Scenario 1 (Part 2)
Figure 8.
Attack Map for Proposed Scenario 1 (Part 3)
The second scenario set up an attack using a distributed denial-of-service
(DDoS) attack as a distraction, otherwise known as a smokescreen. The attack performs
a denial-of-service attack on an isolated part of the network to distract the defenders
from an exploit attack against either the mail server or application server or perform
a web crawl against the webserver. These are the attacks that will give the attacker
access to the network. As shown in figures 9 through 11, the attacker can choose
which server to attack when the DDoS is performed; then, they have several options
for subsequent attacks to gain access to the network and perform other tasks. The
defender's options are to blacklist the IP address, do denial-of-service mitigations,
and install tools to detect further and prevent additional such attacks.
Figure 9.
Attack Map for Proposed Scenario 2 (Part 1)
Figure 10.
Attack Map for Proposed Scenario 2 (Part 2)
Figure 11.
Attack Map for Proposed Scenario 2 (Part 3)
The third scenario involved an attacker spoofing a network
IP address by creating IP packets with that address (figures 12–14). The attacker
used a trusted IP address that did not require the usual degree of authentication.
Spoofed IP addresses can be made more challenging to recognize if each packet appears
to come from a different address. One method is for the attacker to scan the network
to identify the network's addresses, then construct an address that matches an internal
network subdomain. The defender needs to detect and block all inbound traffic from
the attacker's IP. This could succeed or fail based on the probability that the
defender chooses the correct IP to block, given the evidence generated by the attacker.
Figure 12.
Attack Map for Proposed Scenario 3 (Part 1)
Figure 13.
Attack Map for Proposed Scenario 3 (Part 2)
Figure 14.
Attack Map for Proposed Scenario 3 (Part 3)
There are various costs associated with each selected action
and benefits that come with a specific action. Each action by the attacker has a
cost subtracted from the overall score at the end of the game. Similarly, the defender
also has a cost based on the defender action that they choose. Cost, reward, and
the associated, linked actions are shown in Tables 1–5. These costs and reward points
were used from a previous experiment (Green, 2020). They were set while consulting
SoarTech to identify values that would bring realism to the game within realistic
scenarios.
Table 1.
Cost Attacker. Adapted from Green (2020).
Table 2.
Cost Defender. Adapted from Green (2020).
Table 3.
Attacker Decoy Scenario Actions. Adapted from Green (2020).
Table 4.
Defender Decoy Scenario Actions. Adapted from Green (2020).
Table 5.
Defender Decoy Scenario Actions. Adapted from Green (2020).
Figures 3 through 5 show the attacker's flow based on the
actions chosen. Attackers start the game with a set of preparation actions by selecting
“Detecting Decoy,” which means that the attacker determines a decoy's existence
within the environment, or “Maintain IP Address,” which allows the attacker to continue
forward with the attack. The follow-on action is “Map Network.” This simulates a
network scan on the defender network, resulting in open ports on the servers located
within the DMZ. Following the “Map Network” completion, the attacker can select
a specific network attack based on the security conditions discovered from the
“Map Network” phase. These actions include spear-phishing attacks, exploiting public-facing
applications through servers, and a web crawl (collects an inventory) of the web
server through the open port. Upon selection and completion of this selection, the
attacker can discover local networks, users, processes, search local data, or determine
decoys. This leads the attacker to gain vital information from the network, such
as usernames, personally identifiable information (PII), or network connections.
The action was taken here relies on the result of the attack, with the two final
possibilities being “Exfil Data” or “Server Destroyed.” The costs and rewards for
these actions will be tallied, and a total score will be presented.
Like the attackers, the defenders also have a set of actions,
Figure 2, that can be chosen from and accomplished to effectively grade via the
cost/reward via Tables 4–6. The defender's overall goals are to identify the actions
being taken against their network and prevent the attacker from accomplishing their
primary objective. At the beginning of the game, the defender can select six various
options. These include file monitoring, process monitoring, “Audit Logon Events,”
“Audit HIPS Logs,” or “Check Firewall Alerts.” These actions provide the defender
the ability to determine if anything malicious has occurred or is occurring on the
network. Once the defender has determined an attack has occurred, they are given
the ability to perform follow-on actions to stop the current attack from progressing
and stop the exfiltration of data or server being destroyed. Within the defender
actions, there are options to create decoy servers, decoy POR, change file names,
and change hostnames. Adding in the ability for deception can significantly increase
the attacker's costs, helping the defender win the game.
Specifically, for the denial-of-service scenario, the defender's
reward for detecting and mitigating the first attack is minimal since the attacker's
primary goal is to gain access using a different technique than the DDoS. The main
benefit is in preventing the attacker from gaining critical-system access. The longer
the denial of service occurs, the easier it is to figure how to block it, so there
will be a small reward if the defender does not block the attack. However, there
is a competing negative cost to the defender of suffering denial of service; an
alternative cost-benefit accounting is that the attacker accrues points for each
timestamp that a node has a DDoS. These new defender DDoS Scenario cost table can
be seen in Table 6 and the new costs and rewards can be seen in Table 7. The defender
could get a few points each time a DDoS attack has not been blocked, added to their
overall cost. The costs and benefits for the attacker are different if they are
a military adversary since their primary goal can be sabotage and espionage. The
defender's goal against denial of service should be to end excess network connections.
Table 6.
Proposed Defender DDoS Scenario Cost Table
Table 7.
Proposed Actions for Attacker and Defender
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