8 citations · 20 across the 21 of their papers we have counts for
5 papers · 1 filter
Privacy Drift: Evolving Privacy Concerns in Incremental Learning
Sayyed Farid Ahamed, Soumya Banerjee, Sandip Roy +6
In the evolving landscape of machine learning (ML), Federated Learning (FL) presents a paradigm shift towards decentralized model training while preserving user data privacy. This…
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…
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…
Discovering Command and Control (C2) Channels on Tor and Public Networks Using Reinforcement Learning
Cheng Wang, Christopher Redino, Abdul Rahman +5
Command and control (C2) channels are an essential component of many types of cyber attacks, as they enable attackers to remotely control their malware-infected machines and execut…
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…