8 citations · 20 across the 21 of their papers we have counts for
7 papers · 1 filter
Exposing Surveillance Detection Routes via Reinforcement Learning, Attack Graphs, and Cyber Terrain
Lanxiao Huang, Tyler Cody, Christopher Redino +8
Reinforcement learning (RL) operating on attack graphs leveraging cyber terrain principles are used to develop reward and state associated with determination of surveillance detect…
Zero Day Threat Detection Using Metric Learning Autoencoders
Dhruv Nandakumar, Robert Schiller, Christopher Redino +7
The proliferation of zero-day threats (ZDTs) to companies' networks has been immensely costly and requires novel methods to scan traffic for malicious behavior at massive scale. Th…
Anomaly Detection via Federated Learning
Marc Vucovich, Amogh Tarcar, Penjo Rebelo +11
Machine learning has helped advance the field of anomaly detection by incorporating classifiers and autoencoders to decipher between normal and anomalous behavior. Additionally, fe…
Lateral Movement Detection Using User Behavioral Analysis
Deepak Kushwaha, Dhruv Nandakumar, Akshay Kakkar +9
Lateral Movement refers to methods by which threat actors gain initial access to a network and then progressively move through said network collecting key data about assets until t…
Do-AIQ: A Design-of-Experiment Approach to Quality Evaluation of AI Mislabel Detection Algorithm
J. Lian, K. Choi, B. Veeramani +4
The quality of Artificial Intelligence (AI) algorithms is of significant importance for confidently adopting algorithms in various applications such as cybersecurity, healthcare, a…
Zero Day Threat Detection Using Graph and Flow Based Security Telemetry
Christopher Redino, Dhruv Nandakumar, Robert Schiller +6
Zero Day Threats (ZDT) are novel methods used by malicious actors to attack and exploit information technology (IT) networks or infrastructure. In the past few years, the number of…
Discovering Exfiltration Paths Using Reinforcement Learning with Attack Graphs
Tyler Cody, Abdul Rahman, Christopher Redino +7
Reinforcement learning (RL), in conjunction with attack graphs and cyber terrain, are used to develop reward and state associated with determination of optimal paths for exfiltrati…