B Webb

Braden K Webb, Sumit Purohit, Rounak Meyur

The integration of the Internet of Things (IoT) into Cyber-Physical Systems (CPSs) has expanded their cyber-attack surface, introducing new and sophisticated threats with potential to exploit emerging vulnerabilities. Assessing the risks of CPSs is increasingly difficult due to incomplete and outdated cybersecurity knowledge. This highlights the urgent need for better-informed risk assessments and mitigation strategies. While previous efforts have relied on rule-based natural language processing (NLP) tools to map vulnerabilities, weaknesses, and attack patterns, recent advancements in Large Language Models (LLMs) present a unique opportunity to enhance cyber-attack knowledge completion through improved reasoning, inference, and summarization capabilities. We apply embedding models to encapsulate information on attack patterns and adversarial techniques, generating mappings between them using vector embeddings. Additionally, we propose a Retrieval-Augmented Generation (RAG)-based approach that leverages pre-trained models to create structured mappings between different taxonomies of threat patterns. Further, we use a small hand-labeled dataset to compare the proposed RAG-based approach to a baseline standard binary classification model. Thus, the proposed approach provides a comprehensive framework to address the challenge of cyber-attack knowledge graph completion.

Rounak Meyur, Sumit Purohit, Braden K. Webb

The abundance of cyber-physical components in modern day power grid with their diverse hardware and software vulnerabilities has made it difficult to protect them from advanced persistent threats (APTs). An attack graph depicting the propagation of potential cyber-attack sequences from the initial access point to the end objective is vital to identify critical weaknesses of any cyber-physical system. A cyber security personnel can accordingly plan preventive mitigation measures for the identified weaknesses addressing the cyber-attack sequences. However, limitations on available cybersecurity budget restrict the choice of mitigation measures. We address this aspect through our framework, which solves the following problem: given potential cyber-attack sequences for a cyber-physical component in the power grid, find the optimal manner to allocate an available budget to implement necessary preventive mitigation measures. We formulate the problem as a mixed integer linear program (MILP) to identify the optimal budget partition and set of mitigation measures which minimize the vulnerability of cyber-physical components to potential attack sequences. We assume that the allocation of budget affects the efficacy of the mitigation measures. We show how altering the budget allocation for tasks such as asset management, cybersecurity infrastructure improvement, incident response planning and employee training affects the choice of the optimal set of preventive mitigation measures and modifies the associated cybersecurity risk. The proposed framework can be used by cyber policymakers and system owners to allocate optimal budgets for various tasks required to improve the overall security of a cyber-physical system.