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20212024
most citedDiscovering Command and Control Channels Using Reinforcement Learning

8 citations · 11 across the 5 of their papers we have counts for

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5 papers · 1 filter

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.CR2024

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…

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.CR2021

Crown Jewels Analysis using Reinforcement Learning with Attack Graphs

Rohit Gangupantulu, Tyler Cody, Abdul Rahman +3

Cyber attacks pose existential threats to nations and enterprises. Current practice favors piece-wise analysis using threat-models in the stead of rigorous cyber terrain analysis a…