2 citations · 4 across the 10 of their papers we have counts for
4 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…
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…