Cyber-Forensic Investigations (CSE) PH.D seminar
"Principled Data-Driven Decision Support for Cyber-Forensic Investigations"
Abstract: In the wake of a cybersecurity incident, it is crucial to promptly discover how the threat actors breached security to assess the impact of the incident and to develop and deploy countermeasures that can protect against further attacks. To this end, defenders can launch a cyber-forensic investigation, which discovers the techniques that the threat actors used in the incident. A fundamental challenge in such an investigation is prioritizing the investigation of particular techniques since the investigation of each technique requires time and effort, but forensic analysts cannot know which ones were actually used before investigating them. To ensure prompt discovery, it is imperative to provide decision support that can help forensic analysts with this prioritization. A recent study demonstrated that data-driven decision support, based on a dataset of prior incidents, can provide state-of-the-art prioritization. However, this data-driven approach, called DISCLOSE, is based on a heuristic that utilizes only a subset of the available information and does not approximate optimal decisions. To improve upon this heuristic, authors introduce a principled approach for data-driven decision support for cyber-forensic investigations. They formulate the decision-support problem using a Markov decision process, whose states represent the states of a forensic investigation. To solve the decision problem, they propose a Monte Carlo tree search-based method, which relies on a k-NN regression over prior incidents to estimate state-transition probabilities. Authors evaluate the proposed approach on multiple versions of the MITRE ATT&CK dataset, which is a knowledge base of adversarial techniques and tactics based on real-world cyber incidents and demonstrate that their approach outperforms DISCLOSE in terms of techniques discovered per effort spent.
Bio: Dr. Soodeh Atefi is beginning her postdoctoral position at the Department of Computer Science and Engineering at the University of Louisville. She obtained both her M.S. and Ph.D. in computer science from the University of Houston, successfully defending her Ph.D. thesis in the summer of 2023. Her research interests revolve around applied artificial intelligence and machine learning in cyber security. Her work towards her research has been published in two prestigious computer science conferences: The Association for the Advancement of Artificial Intelligence (AAAI) and the International World Wide Web Conference (WWW).
Friday, February 23 at 3:00pm to 4:00pmVirtual Event