8 citations · 20 across the 21 of their papers we have counts for
6 papers · 1 filter
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…
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…