eXfiltration Advanced Persistent Threats (XAPTs) increasingly account for incidents concerned with critical information exfiltration from High Valued Targets (HVT's) by terrorists, cyber criminals or enemy states. Existing Cyber Defence frameworks and data fusion models do not adequately address (i) the multi-stage nature of XAPTs and (ii) the uncertainty and conflicting information associated with XAPTs. A new data fusion theory, called the Markov Multi-phase Transferable Belief Model (MM-TBM) is developed, for tracking and predicting XAPTs. MM-TBM expands the attack kill-chain model to attack trees and introduces a novel approach for combining various sources of cyber evidence, which takes into account the multi-phased nature of XAPTs and the characteristics of the cyberspace. As a data fusion theory, MM-TBM constitutes a novel approach for performing hypothesis assessment and evidence combination across phases, by means of a new combination rule, called the Multi-phase Combination Rule with conflict Reset (MCR2). This is the first combination rule in the field of data fusion that formalises a new method for combining evidence from multiple, causally connected hypotheses spaces and eliminating the bias from preceding phases of the kill-chain. Moreover, this is the first time a data fusion theory utilises the conflict mass m(Ø) for identifying paradoxes. In addition, a diagnostic formula for managing missing pieces of evidence within attack trees is presented. MM-TBM is designed, developed and evaluated using a Design Science Research approach within two iterations. Evaluation is conducted in a relevant computer network environment using scenario-based testing. The experimental design has been reviewed and approved by Cyber Security Subject Matter Experts from MoD’s Defence Science Technology Laboratory and Airbus Group. The experimental results validate the novel capabilities introduced by the new MM-TBM theory to Cyber Defence in the presence of information clutter, conflict and congestion. Furthermore, the results underpin the importance of selecting an optimal sampling policy to effectively track and predict XAPTs. This PhD bridges the gaps in the body of knowledge concerned with multi-phase fusion under uncertainty and Cyber SA against XAPTs. MM-TBM is a novel mathematical fusion theory for managing applications that existing fusion models do not address. This research has demonstrated MM-TBM enables the successful Tracking and Prediction of XAPTs to deliver an enhanced Cyber SA capability.

<p>Software vulnerabilities in programs and malware deployments have been increasing almost every year since we started measuring them. Information about how to program securely, how malware shall be avoided and technological countermeasures for this are more available than ever. Still, the trend seems to favor the attacker. This thesis tries to visualize the effects of a selection of technological countermeasures that have been proposed by researchers. These countermeasures: non-executable memory, address randomization, system call interception and file integrity monitoring are described along with the attacks they are designed to defend against. The coverage of each countermeasure is then visualized with the help of attack trees. Attack trees are normally used for describing how systems can be attacked but here they instead serve the purpose of showing where in an attack a countermeasure takes effect. Using attack trees for this highlights a couple of important aspects of a security mechanism, such as how early in an attack it is effective and which variants of an attack it potentially defends against. This is done by the use of what we call defensive codes that describe how a defense mechanism counters a sub-goal in an attack. Unfortunately the whole process is not well formalized and depends on many uncertain factors.</p>

Software vulnerabilities in programs and malware deployments have been increasing almost every year since we started measuring them. Information about how to program securely, how malware shall be avoided and technological countermeasures for this are more available than ever. Still, the trend seems to favor the attacker. This thesis tries to visualize the effects of a selection of technological countermeasures that have been proposed by researchers. These countermeasures: non-executable memory, address randomization, system call interception and file integrity monitoring are described along with the attacks they are designed to defend against. The coverage of each countermeasure is then visualized with the help of attack trees. Attack trees are normally used for describing how systems can be attacked but here they instead serve the purpose of showing where in an attack a countermeasure takes effect. Using attack trees for this highlights a couple of important aspects of a security mechanism, such as how early in an attack it is effective and which variants of an attack it potentially defends against. This is done by the use of what we call defensive codes that describe how a defense mechanism counters a sub-goal in an attack. Unfortunately the whole process is not well formalized and depends on many uncertain factors.

Security threats to cyber-physical systems are targeting institutions and infrastructure around the world, and the frequency and severity of attacks are on the rise. Healthcare manufacturing, financial services, education, government, and transportation are among the industries that are the most lucrative targets for adversaries. Hacking is not just about companies, organizations, or banks; it also includes critical infrastructure. Wireless Sensors Networks, Vehicle-to-everything communication (V2X), Dynamic Message Signs (DMS), and Traffic Signal Controllers are among major Intelligent Transportation Systems (ITS) infrastructure that has already been attacked or remain vulnerable to hacking. ITS has been deployed with a focus on increasing efficiency and safety in the face of dramatic increases in travel demand. Although many studies have been performed and many security primitives have been proposed, there are significant concerns about flawless performance in a dynamic environment. A holistic security approach, in which all infrastructure performs within the satisfactory level of security remains undiscovered. Previously, hacking of road infrastructure was a rare event, however, in recent years, field devices such as DMS are hacked with higher frequency. The primary reason that transportation assets are vulnerable to cyber-attacks is due to their location. A more dramatic scenario occurs when hackers attempt to convey tampered instructions to the public. Analyzing traveler behavior in response to the hacked messages sign on the basis of empirical data is a vital step toward operating a secure and reliable transportation system. There may be room for improvement by policymakers and program managers when considering critical infrastructure vulnerabilities. With cybersecurity issues escalating every day, road users' safety has been neglected. This dissertation overcomes these challenges and contributes to the nascent but growing literature of Intelligent Transportation System (ITS) security impact-oriented risk assessment in threefold. • First, I employ a risk-based approach to conduct a threat assessment. This threat assessment performs a qualitative vulnerability-oriented threat analysis. The objective is to scrutinize safety, security, reliability, and operation issues that are prompted by a compromised Dynamic Message Signs (DMS). • Second, I examine the impact of drivers' attitudes and behaviors on compliance, route diversion behavior, and speed change behavior, under a compromised DMS. We aim to assess the determinants that are likely to contribute to drivers' compliance with forged information. To this extent, this dissertation evaluates drivers' behavior under different unauthentic messages to assess in-depth the impact of an adversarial attack on the transportation network. • Third, I evaluate distracted driving under different scenarios to assess the in-depth impact of an adversarial attack on the transportation network. To this extent, this dissertation examines factors that are contributing to the manual, visual, and cognitive distractions when drivers encountering fabricated advisory information at a compromised DMS. The results of this dissertation support the original hypothesis and indicate that with respect to the forged information drivers tend to (1) change their planned route, (2) become involved in distracting activities, and (3) change their choice speed at the presence of a compromised DMS. The main findings of this dissertation are outlined below: 1. The DMS security vulnerabilities and predisposing conditions allow adversaries to compromise ITS functionality. The risk-based approach of this study delivers the impact-likelihood matrix, which maps the adverse impacts of the threat events onto a meaningful, visual, matrix. DMS hacking adverse impacts can be categorized mainly as high-risk and medium-risk clusters. The safety, operational (i.e., monetary losses) and behavioral impacts are associated with a high-risk cluster. While the security, reliability, efficiency, and operational (i.e., congestion) impacts are associated with the medium-risk cluster. 2. Tech friendly drivers are more likely to change their route under a compromised DMS. At the same time, while they are acquiring new information, they need to lowering their speed to respond to the higher information load. Under realistic-fabricated information, about 65% of the subjects would depart from their current route. The results indicate that females and subjects with a higher driving experience are more likely to change their route. In addition, those subjects who are more sensitive to the DMS's traffic-related messages and those who use DMS under congested traffic condition are more likely to divert. Interestingly, individuals with lower education level, Asians, those who live in urban areas, and those with trouble finding their direction in new routes are less likely to pick another route rather the one they planned for. 3. Regardless of the DMS hacking scenarios, drivers would engage in at least one of the distractive activities. Among the distractive activities, cognitive distraction has the highest impact on the distracted driving likelihood. Meaning, there is a high chance that drivers think of something other than driving, look at surrounding traffic and scenery, or talk to other passengers regarding the forged information they saw on the DMS. Drivers who rely and trust in technology, and those who check traffic condition before starting their trips tend to become distracted. In addition, the result identified that at the presence of bogus information, drivers tend to slow down or stop in order to react to the DMS. That is, they would either (1) become involved in activities through the means of their phone, (2) they would mind wander, look around, and talk to a passenger about the sign, and (3) search for extra information by means of their vehicle's radio or internet. 4. Females, black individuals, subjects with a disability, older, and those with high trust in DMS are less likely to ignore the fabricated messages. In contrary, white, those who drive long hours, and those who see driving as a tedious task are more likely to ignore the bogus messages. Drivers who comply with traffic regulations and have a good driving record are likely to slow down under the tampered messages. Furthermore, female drivers and those who live in rural areas are more likely to slow down under fabricated advisory information. Furthermore, this dissertation identifies that planning for alternative route and involvement in distractive activities cause speed variation behaviors under the compromised DMS. This dissertation is the first to investigate the adverse impact of a compromised DMS on the road users and operators. I attempt to address the current gap in the literature by assessing and evaluating the impact of ITS security vulnerabilities. Broader impacts of this study include (1) to systematically raising awareness among policy-makers and engineers, (2) motivating further simulations and real-world experiments to investigate this matter further, (3) to systematically assessing the adverse impact of a security breach on transportation reliability and safety, and drivers' behavior, and (4) providing insights for system operators and decision-makers to prioritize the risk of a compromised DMS. Additionally, the outcome can be integrated with the nationwide connected vehicle and V2X implementations and security design. / Doctor of Philosophy / Security threats are targeting institutions and infrastructure around the world, and the frequency and severity of security attacks are on the rise. Healthcare manufacturing, financial services, education, government, and transportation are among the industries that are the most lucrative targets for adversaries. Hacking is not just about companies, organizations, or banks; it also includes critical infrastructure. Intelligent Transportation Systems have been deployed with a focus on increasing efficiency and safety in the face of dramatic increases in traffic volume. Although many studies have been performed and many security primitives have been proposed, there are significant concerns about flawless performance in a dynamic environment. A holistic security approach, in which all infrastructure performs within the satisfactory level of security remains undiscovered. Previously, hacking of road infrastructure was a rare event, however, in recent years, field devices, such as dynamic message signs, are hacked with higher frequency. The primary reason that transportation assets are vulnerable to cyber-attacks is that of their location in public. A more dramatic scenario occurs when hackers attempt to convey tampered instructions to the public. Analyzing traveler behavior in response to the hacked messages sign on the basis of empirical data is a vital step toward operating a secure and reliable transportation system. This study is the first to investigate the adversarial impact of a compromised message sign on the road users and operators. I attempt to address the current gap in the literature by assessing and evaluating the impact of ITS security vulnerabilities.