Cyber Defense Remediation in Energy Delivery Systems

The integration of Information Technology (IT) and Operational Technology (OT) in Cyber-Physical Systems (CPS) has resulted in increased efficiency and facilitated real-time information acquisition, processing, and decision making. However, the increase in automation technology and the use of the internet for connecting, remote controlling, and supervising systems and facilities has also increased the likelihood of cybersecurity threats that can impact safety of humans and property. There is a need to assess cybersecurity risks in the power grid, nuclear plants, chemical factories, etc. to gain insight into the likelihood of safety hazards. Quantitative cybersecurity risk assessment will lead to informed cyber defense remediation and will ensure the presence of a mitigation plan to prevent safety hazards. In this dissertation, using Energy Delivery Systems (EDS) as a use case to contextualize a CPS, we address key research challenges in managing cyber risk for cyber defense remediation. First, we developed a platform for modeling and analyzing the effect of cyber threats and random system faults on EDS\u27s safety that could lead to catastrophic damages. We developed a data-driven attack graph and fault graph-based model to characterize the exploitability and impact of threats in EDS. We created an operational impact assessment to quantify the damages. Finally, we developed a strategic response decision capability that presents optimal mitigation actions and policies that balance the tradeoff between operational resilience (tactical risk) and strategic risk. Next, we addressed the challenge of management of tactical risk based on a prioritized cyber defense remediation plan. A prioritized cyber defense remediation plan is critical for effective risk management in EDS. Due to EDS\u27s complexity in terms of the heterogeneous nature of blending IT and OT and Industrial Control System (ICS), scale, and critical processes tasks, prioritized remediation should be applied gradually to protect critical assets. We proposed a methodology for prioritizing cyber risk remediation plans by detecting and evaluating critical EDS nodes\u27 paths. We conducted evaluation of critical nodes characteristics based on nodes\u27 architectural positions, measure of centrality based on nodes\u27 connectivity and frequency of network traffic, as well as the controlled amount of electrical power. The model also examines the relationship between cost models of budget allocation for removing vulnerabilities on critical nodes and their impact on gradual readiness. The proposed cost models were empirically validated in an existing network ICS test-bed computing nodes criticality. Two cost models were examined, and although varied, we concluded the lack of correlation between types of cost models to most damageable attack path and critical nodes readiness. Finally, we proposed a time-varying dynamical model for the cyber defense remediation in EDS. We utilize the stochastic evolutionary game model to simulate the dynamic adversary of cyber-attack-defense. We leveraged the Logit Quantal Response Dynamics (LQRD) model to quantify real-world players\u27 cognitive differences. We proposed the optimal decision making approach by calculating the stable evolutionary equilibrium and balancing defense costs and benefits. Case studies on EDS indicate that the proposed method can help the defender predict possible attack action, select the related optimal defense strategy over time, and gain the maximum defense payoffs. We also leveraged software-defined networking (SDN) in EDS for dynamical cyber defense remediation. We presented an approach to aid the selection security controls dynamically in an SDN-enabled EDS and achieve tradeoffs between providing security and Quality of Service (QoS). We modeled the security costs based on end-to-end packet delay and throughput. We proposed a non-dominated sorting based multi-objective optimization framework which can be implemented within an SDN controller to address the joint problem of optimizing between security and QoS parameters by alleviating time complexity at O(MN2). The M is the number of objective functions, and N is the population for each generation, respectively. We presented simulation results that illustrate how data availability and data integrity can be achieved while maintaining QoS constraints

Design and Analysis of Anomaly Detection and Mitigation Schemes for Distributed Denial of Service Attacks in Software Defined Network. An Investigation into the Security Vulnerabilities of Software Defined Network and the Design of Efficient Detection and Mitigation Techniques for DDoS Attack using Machine Learning Techniques

Software Defined Networks (SDN) has created great potential and hope to overcome the need for secure, reliable and well managed next generation networks to drive effective service delivery on the go and meet the demand for high data rate and seamless connectivity expected by users. Thus, it is a network technology that is set to enhance our day-to-day activities. As network usage and reliance on computer technology are increasing and popular, users with bad intentions exploit the inherent weakness of this technology to render targeted services unavailable to legitimate users. Among the security weaknesses of SDN is Distributed Denial of Service (DDoS) attacks. Even though DDoS attack strategy is known, the number of successful DDoS attacks launched has seen an increment at an alarming rate over the last decade. Existing detection mechanisms depend on signatures of known attacks which has not been successful in detecting unknown or different shades of DDoS attacks. Therefore, a novel detection mechanism that relies on deviation from confidence interval obtained from the normal distribution of throughput polled without attack from the server. Furthermore, sensitivity analysis to determine which of the network metrics (jitter, throughput and response time) is more sensitive to attack by introducing white Gaussian noise and evaluating the local sensitivity using feed-forward artificial neural network is evaluated. All metrics are sensitive in detecting DDoS attacks. However, jitter appears to be the most sensitive to attack. As a result, the developed framework provides an avenue to make the SDN technology more robust and secure to DDoS attacks

Design and Analysis of Anomaly Detection and Mitigation Schemes for Distributed Denial of Service Attacks in Software Defined Network. An Investigation into the Security Vulnerabilities of Software Defined Network and the Design of Efficient Detection and Mitigation Techniques for DDoS Attack using Machine Learning Techniques

Software Defined Networks (SDN) has created great potential and hope to overcome the need for secure, reliable and well managed next generation networks to drive effective service delivery on the go and meet the demand for high data rate and seamless connectivity expected by users. Thus, it is a network technology that is set to enhance our day-to-day activities. As network usage and reliance on computer technology are increasing and popular, users with bad intentions exploit the inherent weakness of this technology to render targeted services unavailable to legitimate users. Among the security weaknesses of SDN is Distributed Denial of Service (DDoS) attacks. Even though DDoS attack strategy is known, the number of successful DDoS attacks launched has seen an increment at an alarming rate over the last decade. Existing detection mechanisms depend on signatures of known attacks which has not been successful in detecting unknown or different shades of DDoS attacks. Therefore, a novel detection mechanism that relies on deviation from confidence interval obtained from the normal distribution of throughput polled without attack from the server. Furthermore, sensitivity analysis to determine which of the network metrics (jitter, throughput and response time) is more sensitive to attack by introducing white Gaussian noise and evaluating the local sensitivity using feed-forward artificial neural network is evaluated. All metrics are sensitive in detecting DDoS attacks. However, jitter appears to be the most sensitive to attack. As a result, the developed framework provides an avenue to make the SDN technology more robust and secure to DDoS attacks