The transformative potential of deep learning in enhancing computer science solutions has not gone unnoticed in the fields of security and privacy. However, the sheer volume of related scientific literature and the significant gap between a lab context and real-world environments make it extremely challenging to assess the current progress in the area. In this talk, I will review underlying mechanisms and main principles behind deep learning when applied to offensive and defensive cybersecurity solutions. I will focus on two primary use cases: traffic analysis attacks on Tor and network-based intrusion detection systems, analyzing the expected benefits and potential pitfalls of using deep learning. This analysis effectively challenges the common perception of a purely end-to-end approach. To that end, the presentation emphasizes the importance of explainability and error analysis for validating and troubleshooting deep neural networks. This discussion is meant to equip cybersecurity researchers and practitioners to begin incorporating deep learning in their toolbox while maintaining a critical and holistic perspective.
Vera Rimmer is a post-doctoral researcher at the DistriNet lab in KU Leuven, Belgium, where she has recently completed her PhD under the supervision of Prof. Wouter Joosen and Dr. Davy Preuveneers. She studies cybersecurity and privacy-enhancing technologies; data analytics in cybersecurity and privacy; applied machine learning and deep learning; privacy and trustworthiness of applied data-driven AI. Her published research revolves around exploring deep learning as a threat against anonymous communication, and various aspects of AI-enabled network intrusion detection and authentication.