most citedDiscovering Command and Control Channels Using Reinforcement Learning

8 citations · 10 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2024

Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning

Sayyed Farid Ahamed, Soumya Banerjee, Sandip Roy +7

Over the last few years, federated learning (FL) has emerged as a prominent method in machine learning, emphasizing privacy preservation by allowing multiple clients to collaborati…

cs.CR20241 cited

Leveraging Reinforcement Learning in Red Teaming for Advanced Ransomware Attack Simulations

Cheng Wang, Christopher Redino, Ryan Clark +8

Ransomware presents a significant and increasing threat to individuals and organizations by encrypting their systems and not releasing them until a large fee has been extracted. To…

cs.CR20248 cited

Discovering Command and Control Channels Using Reinforcement Learning

Cheng Wang, Akshay Kakkar, Christopher Redino +7

Command and control (C2) paths for issuing commands to malware are sometimes the only indicators of its existence within networks. Identifying potential C2 channels is often a manu…

cs.CR20231 cited

Enhancing Exfiltration Path Analysis Using Reinforcement Learning

Riddam Rishu, Akshay Kakkar, Cheng Wang +7

Building on previous work using reinforcement learning (RL) focused on identification of exfiltration paths, this work expands the methodology to include protocol and payload consi…

cs.CR2023

Cross-temporal Detection of Novel Ransomware Campaigns: A Multi-Modal Alert Approach

Sathvik Murli, Dhruv Nandakumar, Prabhat Kumar Kushwaha +6

We present a novel approach to identify ransomware campaigns derived from attack timelines representations within victim networks. Malicious activity profiles developed from multip…