Industrial Control Systems (ICSs) are used to monitor and control critical infrastructure such as electricity and water. ICS were originally stand-alone systems, but are now widely being connected to corporate national IT networks, making remote monitoring and more timely control possible. While this connectivity has brought multiple benefits to ICS, such as cost reductions and an increase in redundancy and flexibility, ICS were not designed for open connectivity and therefore are more prone to security threats, creating a greater requirement for adequate security engineering approaches. The culture gap between developers and security experts is one of the main challenges of ICS security engineering. Control system developers play an important role in building secure systems; however, they lack security training and support throughout the development process. Security training, which is an essential activity in the defence-indepth strategy for ICS security, has been addressed, but has not been given sufficient attention in academia. Security support is a key means by which to tackle this challenge via assisting developers in ICS security by design. This thesis proposes a novel framework, the Industrial Control System Security Engineering Support (ICS-SES), which aims to help developers in designing secure control systems by enabling them to reuse secure design patterns and improve their security knowledge. ICS-SES adapts pattern-based approach to guide developers in security engineering, and an automated planning technique to provide adaptive on-the-job security training tailored to personal needs. The usability of ICS-SES has been evaluated using an empirical study in terms of its effectiveness in assisting the design of secure control systems and improving developers’ security knowledge. The results show that ICS-SES can efficiently help control system designers to mitigate security vulnerabilities and improve their security knowledge, reducing the difficulties associated with the security engineering process, and the results have been found to be statically significant. In summary, ICS-SES provides a unified method of supporting an ICS security by design approach. It fosters a development environment where engineers can improve their security knowledge while working in a control system production line.

<p dir="ltr">In the contemporary digital era, Advanced Persistent Threat (APT) attacks are evolving, becoming increasingly sophisticated, and now perilously targeting critical cyber-physical systems, notably Industrial Control Systems (ICS). The intersection of digital and physical realms in these systems enables APT attacks on ICSs to potentially inflict physical damage, disrupt critical infrastructure, and jeopardize human safety, thereby posing severe consequences for our interconnected world. Provenance tracing techniques are essential for investigating these attacks, yet existing APT attack forensics approaches grapple with scalability and maintainability issues. These approaches often hinge on system- or application-level logging, incurring high space and run-time overheads and potentially encountering difficulties in accessing source code. Their dependency on heuristics and manual rules necessitates perpetual updates by domain-knowledge experts to counteract newly developed attacks. Additionally, while there have been efforts to verify the safety of Programming Logic Controller (PLC) code as adversaries increasingly target industrial environments, these works either exclusively consider PLC program code without connecting to the underlying physical process or only address time-related physical safety issues neglecting other vital physical features.</p><p dir="ltr">This dissertation introduces two novel frameworks, ATLAS and ARCHPLC, to address the aforementioned challenges, offering a synergistic approach to fortifying cybersecurity in the face of evolving APT and ICS threats. ATLAS, an effective and efficient multi-host attack investigation framework, constructs end-to-end APT attack stories from audit logs by combining causality analysis, Natural Language Processing (NLP), and machine learning. Identifying key attack patterns, ATLAS proficiently analyzes and pinpoints attack events, minimizing alert fatigue for cyber analysts. During evaluations involving ten real-world APT attacks executed in a realistic virtual environment, ATLAS demonstrated an ability to recover attack steps and construct attack stories with an average precision of 91.06%, a recall of 97.29%, and an F1-score of 93.76%, providing a robust framework for understanding and mitigating cyber threats.</p><p dir="ltr">Concurrently, ARCHPLC, an advanced approach for enhancing ICS security, combines static analysis of PLC code and data mining from ICS data traces to derive accurate invariants, providing a comprehensive understanding of ICS behavior. ARCHPLC employs physical causality graph analysis techniques to identify cause-effect relationships among plant components (e.g., sensors and actuators), enabling efficient and quantitative discovery of physical causality invariants. Supporting patching and run-time monitoring modes, ARCHPLC inserts derived invariants into PLC code using program synthesis in patching mode and inserts invariants into a dedicated monitoring program for continuous safety checks in run-time monitoring mode. ARCHPLC adeptly detects and mitigates run-time anomalies, providing exceptional protection against cyber-physical attacks with minimal overhead. In evaluations against 11 cyber-physical attacks on a Fischertechnik manufacturing plant and a chemical plant simulator, ARCHPLC protected the plants without any false positives or negatives, with an average run-time overhead of 14.31% in patching mode and 0.4% in run-time monitoring mode.</p><p dir="ltr">In summary, this dissertation provides invaluable solutions that equip cybersecurity professionals to enhance APT attack investigation, enabling them to identify and comprehend complex attacks with heightened accuracy. Moreover, these solutions significantly bolster the safety and security of ICS infrastructure, effectively protecting critical systems and strengthening defenses against cyber-physical attacks, thereby contributing substantially to the field of cybersecurity.</p>