8 citations · 20 across the 15 of their papers we have counts for
12 papers · 1 filter
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…
FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks
Marc Vucovich, Devin Quinn, Kevin Choi +3
Federated learning has created a decentralized method to train a machine learning model without needing direct access to client data. The main goal of a federated learning architec…
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…
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…
Foundational Models for Malware Embeddings Using Spatio-Temporal Parallel Convolutional Networks
Dhruv Nandakumar, Devin Quinn, Elijah Soba +6
In today's interconnected digital landscape, the proliferation of malware poses a significant threat to the security and stability of computer networks and systems worldwide. As th…
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…
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…
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…
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…