4 citations · 5 across the 5 of their papers we have counts for
5 papers
Self-Supervised Representation Learning for Adversarial Attack Detection
Yi Li, Plamen Angelov, Neeraj Suri
Supervised learning-based adversarial attack detection methods rely on a large number of labeled data and suffer significant performance degradation when applying the trained model…
UNICAD: A Unified Approach for Attack Detection, Noise Reduction and Novel Class Identification
Alvaro Lopez Pellicer, Kittipos Giatgong, Yi Li +2
As the use of Deep Neural Networks (DNNs) becomes pervasive, their vulnerability to adversarial attacks and limitations in handling unseen classes poses significant challenges. The…
Model Leeching: An Extraction Attack Targeting LLMs
Lewis Birch, William Hackett, Stefan Trawicki +2
Model Leeching is a novel extraction attack targeting Large Language Models (LLMs), capable of distilling task-specific knowledge from a target LLM into a reduced parameter model.…
Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph
Yang Lu, Zhengxin Yu, Neeraj Suri
Establishing how a set of learners can provide privacy-preserving federated learning in a fully decentralized (peer-to-peer, no coordinator) manner is an open problem. We propose t…
ThreatPro: Multi-Layer Threat Analysis in the Cloud
Salman Manzoor, Antonios Gouglidis, Matthew Bradbury +1
Many effective Threat Analysis (TA) techniques exist that focus on analyzing threats to targeted assets (e.g., components, services). These techniques consider static interconnecti…