5 citations · 11 across the 12 of their papers we have counts for
12 papers
Retrieval Augmented Anomaly Detection (RAAD): Nimble Model Adjustment Without Retraining
Sam Pastoriza, Iman Yousfi, Christopher Redino +4
We propose a novel mechanism for real-time (human-in-the-loop) feedback focused on false positive reduction to enhance anomaly detection models. It was designed for the lightweight…
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…
MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigation with Federated Learning
Soumya Banerjee, Sandip Roy, Sayyed Farid Ahamed +7
The membership inference attack (MIA) is a popular paradigm for compromising the privacy of a machine learning (ML) model. MIA exploits the natural inclination of ML models to over…
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…
A Novel Approach To User Agent String Parsing For Vulnerability Analysis Using Mutli-Headed Attention
Dhruv Nandakumar, Sathvik Murli, Ankur Khosla +6
The increasing reliance on the internet has led to the proliferation of a diverse set of web-browsers and operating systems (OSs) capable of browsing the web. User agent strings (U…
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…
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…
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…