In this dissertation research, we design and analyze resilient cyber-physical systems (CPSs) under high network dynamics, adversarial attacks, and various uncertainties. We focus on three key system attributes to build resilient CPSs by developing a suite of the autonomous cyber defense mechanisms. First, we consider network adaptability to achieve the resilience of a CPS. Network adaptability represents the network ability to maintain its security and connectivity level when faced with incoming attacks. We address this by network topology adaptation. Network topology adaptation can contribute to quickly identifying and updating the network topology to confuse attacks by changing attack paths. We leverage deep reinforcement learning (DRL) to develop CPSs using network topology adaptation. Second, we consider the fault-tolerance of a CPS as another attribute to ensure system resilience. We aim to build a resilient CPS under severe resource constraints, adversarial attacks, and various uncertainties. We chose a solar sensor-based smart farm as one example of the CPS applications and develop a resource-aware monitoring system for the smart farms. We leverage DRL and uncertainty quantification using a belief theory, called Subjective Logic, to optimize critical tradeoffs between system performance and security under the contested CPS environments. Lastly, we study system resilience in terms of system recoverability. The system recoverability refers to the system's ability to recover from performance degradation or failure. In this task, we mainly focus on developing an automated intrusion response system (IRS) for CPSs. We aim to design the IRS with effective and efficient responses by reducing a false alarm rate and defense cost, respectively. Specifically, We build a lightweight IRS for an in-vehicle controller area network (CAN) bus system operating with DRL-based autonomous driving. / Doctor of Philosophy / In this dissertation research, we design and analyze resilient cyber-physical systems (CPSs) under high network dynamics, adversarial attacks, and various uncertainties. We focus on three key system attributes to build resilient CPSs by developing a suite of the autonomous cyber defense mechanisms. First, we consider network adaptability to achieve the resilience of a CPS. Network adaptability represents the network ability to maintain its security and connectivity level when faced with incoming attacks. We address this by network topology adaptation. Network topology adaptation can contribute to quickly identifying and updating the network topology to confuse attacks by changing attack paths. We leverage deep reinforcement learning (DRL) to develop CPSs using network topology adaptation. Second, we consider the fault-tolerance of a CPS as another attribute to ensure system resilience. We aim to build a resilient CPS under severe resource constraints, adversarial attacks, and various uncertainties. We chose a solar sensor-based smart farm as one example of the CPS applications and develop a resource-aware monitoring system for the smart farms. We leverage DRL and uncertainty quantification using a belief theory, called Subjective Logic, to optimize critical tradeoffs between system performance and security under the contested CPS environments. Lastly, we study system resilience in terms of system recoverability. The system recoverability refers to the system's ability to recover from performance degradation or failure. In this task, we mainly focus on developing an automated intrusion response system (IRS) for CPSs. We aim to design the IRS with effective and efficient responses by reducing a false alarm rate and defense cost, respectively. Specifically, We build a lightweight IRS for an in-vehicle controller area network (CAN) bus system operating with DRL-based autonomous driving.

The introduction of communication technology into the electric power grid has made the grid more reliable. Power system operators gain visibility over the power system and are able to resolve operational issues remotely via Supervisory Control And Data Acquisition (SCADA) technology. This reduces outage periods. Nonetheless, the remote-control capability has rendered the power grid vulnerable to cyberattacks. In December 2015, over 200,000 people in Ukraine became victims of the first publicly reported cyberattack on the power grid. Consequently, cyber-physical security research for the power system as a critical infrastructure is in critical need. Research on cybersecurity for power grids has produced a diverse literature; the multi-faceted nature of the grid makes it vulnerable to different types of cyberattacks, such as direct power grid, supply chain and ransom attacks. The attacks may also target different levels of grid operation, such as the transmission system, distribution system, microgrids, and generation. As these levels are characterized by varying operational constraints, the literature may be categorized not only according to the type of attack it targets, but also according to the level of power system operation under consideration. It is noteworthy that cybersecurity research for the transmission system dominates the literature, although the distribution system is noted to have a larger attack surface. For the distribution system, a notable attack type is the so-called direct switching attack, in which an attacker aims to disrupt power supply by compromising switching devices that connect equipment such as generators, and power grid lines. To maximize the damage, this attack tends to be coordinated as the attacker optimally selects the nodes and switches to attack. This decision-making process is often a bi- or tri-level optimization problem which models the interaction between the attacker and the power system defender. It is necessary to detect attacks and establish coordination/correlation among them. Determining coordination is a necessary step to predict the targets of an attack before attack completion, and aids in the mitigation strategy that ensues. While the literature has addressed the direct switching attack on the distribution system in different ways, there are also shortcomings. These include: (i) techniques to establish coordination among attacks are centralized, making them prone to single-point failures; (ii) techniques to establish coordination among attacks leverage only power system models, ignoring the influence of communication network vulnerabilities and load criticality in the decisions of the attacker; (iii) attacker-defender optimization models assume specific knowledge of the attacker resources and constraints by the defender, a strong unrealistic assumption that reduces their usability; (iv) and, mitigation strategies tend to be static and one-sided, being implemented only at the physical level, or at the communication network level. In light of this, this dissertation culminates in major contributions concerning real-time decentralized correlation of detected direct switching attacks and hybrid mitigation for electric power distribution systems. Concerning this, four novel contributions are presented: (i) a framework for decentralized correlation of attacks and mitigation; (ii) an attacker-defender optimization model that accounts for power system laws, load criticality, and cyber vulnerabilities in the decision-making process of the attacker; (iii) a real-time learning-based mechanism for determining correlation among detected attacks and predicting attack targets, and which does not assume knowledge of the attacker's resources and constraints by the power system defender; (iv) a hybrid mitigation strategy optimized in real-time based on information learned from detected attacks, and which combines both physical level and communication network level mitigation. Since the execution of intrusion detection systems and mechanisms such as the ones proposed in this dissertation may deter attackers from directly attacking the power grid, attackers may perform a supply chain cyberattack to yield the same results. Although, supply chain cyberattacks have been acknowledged as potentially far-reaching, and compliance directives put forward for this, the detection of supply chain cyberattacks is in a nascent stage. Consequently, this dissertation also proposes a novel method for detecting supply chain cyberattacks. To the best of the knowledge of the author, this work is the first preliminary work on supply chain cyberattack detection. / Doctor of Philosophy / The electric power grid is the network that transports electricity from generation to consumers, such as homes and factories. The power grid today is highly remote-monitored and controlled. Should there be a fault on the grid, the human operator, often remotely located, may only need to resolve it by sending a control signal to telemetry points, called nodes, via a communication network. This significantly reduces outage periods and improves the reliability of the grid. Nonetheless, the high level connectivity also exposes the grid to cyberattacks. The cyber connectivity between the power grid and the human operator, like all communication networks, is vulnerable to cyberattacks that may allow attackers to gain control of the power grid. If and when successful, wide-spread and extended outages, equipment damage, etc. may ensue. Indeed, in December 2015, over 200,000 people in Ukraine became victims to the first publicly reported cyberattack on a power grid. As a critical infrastructure, cybersecurity for the power grid is, therefore, in critical need. Research on cybersecurity for power grids has produced a diverse literature; the multi-faceted nature of the grid makes it vulnerable to different types of cyberattacks, such as direct power grid, supply chain and ransom attacks. Notable is the so-called direct switching attack, in which an attacker aims to compromise the power grid communication network in order to toggle switches that connect equipment such as generators, and power grid lines. The aim is to disrupt electricity service. To maximize the damage, this attack tends to be coordinated; the attacker optimally selects several grid elements to attack. Thus, it is necessary to both detect attacks and establish coordination among them. Determining coordination is a necessary step to predict the targets of an attack before attack completion. This aids the power grid owner to intercept and mitigate attacks. While the literature has addressed the direct switching attack in different ways, there are also shortcomings. Three outstanding ones are: (i) techniques to determine coordination among attacks and predict attack targets are centralized, making them prone to single-point failures; (ii) techniques to establish coordination among attacks leverage only power system physical laws, ignoring the influence of communication network vulnerabilities in the decisions of the attacker; (iii) and, studies on the interaction between the attacker and the defender (i.e., power grid owner) assume specific knowledge of the attacker resources and constraints by the defender, a strong unrealistic assumption that reduces their usability. This research project addresses several of the shortcomings in the literature, particularly the aforementioned. The work focuses on the electric distribution system, which is the power grid that connects directly to consumers. Indeed, this choice is ideal, as the distribution system has a larger attack surface than other parts of the grid and is characterized by computing devices with more constrained computational capability. Thus, adaptability to simple computing devices is a priority. The contributions of this dissertation provide leverage to the power grid owner to intercept and mitigate attacks in a resilient manner. The original contributions of the work are: (i) a novel realistic model that shows the decision making process of the attacker and their interactions with the defender; (ii) a novel decentralized mechanism for predicting the targets of coordinated cyberattacks on the electric distribution grid in real-time and which is guided by the attack model, (iii) and a novel hybrid optimized mitigation strategy that provides security to the power grid at both the communication network level and the physical power grid level. Since the power grid is constructed with smart equipment from various vendors, attackers may launch effective attacks by compromising the devices deployed in the power grid through a compromised supply chain. By nature, such an attack is evasive to traditional intrusion detection systems and algorithms such as the aforementioned. Therefore, this work also provides a new method to defend the grid against supply chain attacks, resulting in a mechanism for its detection in a critical power system communication device.

The recent emergence and rapid advancement of Cloud Computing (CC) infrastructure and services have made outsourcing Information Technology (IT) and digital services to Cloud Providers (CPs) attractive. Cloud offerings enable reduction in IT resources (hardware, software, services, support, and staffing), and provide flexibility and agility in resource allocation, data and resource delivery, fault-tolerance, and scalability. However, the current standards and guidelines adopted by many CPs are tailored to address functionality (such as availability, speed, and utilization) and design requirements (such as integration), rather than protection against cyber-attacks and associated security issues. In order to achieve sustainable trust for cloud services with minimal risks and impact on cloud customers, appropriate cloud information security models are required. The research described in this dissertation details the processes adopted for the development and implementation of an integrated information security cloud based approach to cloud service models. This involves detailed investigation into the inherent information security deficiencies identified in the existing cloud service models, service agreements, and compliance issues. The research conducted was a multidisciplinary in nature, with detailed investigations on factors such as people, technology, security, privacy, and compliance involved in cloud risk assessment to ensure all aspects are addressed in holistic and well-structured models. The primary research objectives for this dissertation are investigated through a series of scientific papers centered on these key research disciplines. The assessment of information security, privacy, and compliance implementations in a cloud environment is described in Chapters two, three, four, and five. Paper 1 (CCIPS: A Cooperative Intrusion Detection and Prevention Framework for Cloud Services) outlines a framework for detecting and preventing known and zero-day threats targeting cloud computing networks. This framework forms the basis for implementing enhanced threat detection and prevention via behavioral and anomaly data analysis. Paper 2 (A Trusted CCIPS Framework) extends the work of cooperative intrusion detection and prevention to enable trusted delivery of cloud services. The trusted CCIPS model details and justifies the multi-layer approach to enhance the performance and efficiency of detecting and preventing cloud threats. Paper 3 (SOCaaS: Security Operations Center as a Service for Cloud Computing Environments) describes the need for a trusted third party to perform real-time monitoring of cloud services to ensure compliance with security requirements by suggesting a security operations center system architecture. Paper 4 (SecSLA: A Proactive and Secure Service Level Agreement Framework for Cloud Services) identifies the necessary cloud security and privacy controls that need to be addressed in the contractual agreements, i.e. service level agreements (SLAs), between CPs and their customers. Papers five, six, seven, and eight (Chapters 6 – 9) focus on addressing and reducing the risk issues resulting from poor assessment to the adoption of cloud services and the factors that influence such as migration. The investigation of cloud-specific information security risk management and migration readiness frameworks, detailed in Paper 5 (An Effective Risk Management Framework for Cloud Computing Services) and Paper 6 (Information Security, Privacy, and Compliance Readiness Model) was achieved through extensive consideration of all possible factors obtained from different studies. An analysis of the results indicates that several key factors, including risk tolerance, can significantly influence the migration decision to cloud technology. An additional issue found during this research in assessing the readiness of an organization to move to the cloud is the necessity to ensure that the cloud service provider is actually with information security, privacy, and compliance (ISPC) requirements. This investigation is extended in Paper 7 (A Practical Life Cycle Approach for Cloud based Information Security) to include the six phases of creating proactive cloud information security systems beginning with initial design, through the development, implementation, operations and maintenance. The inherent difficulty in identifying ISPC compliant cloud technology is resolved by employing a tracking method, namely the eligibility and verification system presented in Paper 8 (Cloud Services Information Security and Privacy Eligibility and Verification System). Finally, Paper 9 (A Case Study of Migration to a Compliant Cloud Technology) describes the actual implementation of the proposed frameworks and models to help the decision making process faced by the Saudi financial agency in migrating their IT services to the cloud. Together these models and frameworks suggest that the threats and risks associated with cloud services are continuously changing and more importantly, increasing in complexity and sophistication. They contribute to making stronger cloud based information security, privacy, and compliance technological frameworks. The outcomes obtained significantly contribute to best practices in ensuring information security controls are addressed, monitoring, enforced, and compliant with relevant regulations. / Graduate / 0984 / 0790 / fahd333@gmail.com

Information technology systems are essential for most businesses as they facilitate the handling and sharing of data and the execution of tasks. Due to connectivity to the internet and other internal networks, these systems are susceptible to cyberattacks. Computer worms are one of the most significant threats to computer systems because of their fast self-propagation to multiple systems and malicious payloads. Modern worms employ obfuscation techniques to avoid detection using patterns from previous attacks. Although the best defense is to eliminate (patch) the software vulnerabilities being exploited by computer worms, this requires a substantial amount of time to create, test, and deploy the patches. Worm containment techniques are used to reduce or stop the spread of worm infections to allow time for software patches to be developed and deployed. In this dissertation, a novel blockchain-based collaborative intrusion prevention system model is introduced. This model is designed to proactively contain zero-day and obfuscated computer worms. In this model, containment is achieved by creating and distributing signatures for the exploited vulnerabilities. Blockchain technology is employed to provide liveness, maintain an immutable record of vulnerability-based signatures to update peers, accomplish trust in confirming the occurrence of a malicious event and the corresponding signature, and allow a decentralized defensive environment. A consensus algorithm based on the Practical Byzantine Fault Tolerance (PBFT) algorithm is employed in the model. The TLA+ formal method is utilized to check the correctness, liveness, and safety properties of the model as well as to assert that it has no behavioral errors. A blockchain-based automatic worm containment system is implemented. A synthetic worm is created to exploit a network-deployed vulnerable program. This is used to evaluate the effectiveness of the containment system. It is shown that the system can contain the worm and has good performance. The system can contain 100 worm attacks a second by generating and distributing the corresponding vulnerability-based signatures. The system latency to contain these attacks is less than 10 ms. In addition, the system has low resource requirements with respect to memory, CPU, and network traffic. / Graduate

This diploma thesis is focused on the detection and protection against Slow DoS and DDoS attacks using computer network traffic analysis. The reader is introduced to the basic issues of this specific category of sophisticated attacks, and the characteristics of several specific attacks are clarified. There is also a set of methods for detecting and protecting against these attacks. The proposed methods are used to implement custom intrusion prevention system that is deployed on the border filtering server of computer network in order to protect Web servers against attacks from the Internet. Then created system is tested in the laboratory network. Presented results of the testing show that the system is able to detect attacks Slow GET, Slow POST, Slow Read and Apache Range Header and then protect Web servers from affecting provided services.