10 citations · 10 across the 2 of their papers we have counts for
4 papers · 1 filter
RX-ADS: Interpretable Anomaly Detection using Adversarial ML for Electric Vehicle CAN data
Chathurika S. Wickramasinghe, Daniel L. Marino, Harindra S. Mavikumbure +5
Recent year has brought considerable advancements in Electric Vehicles (EVs) and associated infrastructures/communications. Intrusion Detection Systems (IDS) are widely deployed fo…
Self-Supervised and Interpretable Anomaly Detection using Network Transformers
Daniel L. Marino, Chathurika S. Wickramasinghe, Craig Rieger +1
Monitoring traffic in computer networks is one of the core approaches for defending critical infrastructure against cyber attacks. Machine Learning (ML) and Deep Neural Networks (D…
Physics Enhanced Data-Driven Models with Variational Gaussian Processes
Daniel L. Marino, Milos Manic
Centuries of development in natural sciences and mathematical modeling provide valuable domain expert knowledge that has yet to be explored for the development of machine learning…
An Adversarial Approach for Explainable AI in Intrusion Detection Systems
Daniel L. Marino, Chathurika S. Wickramasinghe, Milos Manic
Despite the growing popularity of modern machine learning techniques (e.g. Deep Neural Networks) in cyber-security applications, most of these models are perceived as a black-box f…