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20222026
most citedRES-HD: Resilient Intelligent Fault Diagnosis Against Adversarial Attacks Using Hyper-Dimensional Computing

4 citations · 6 across the 22 of their papers we have counts for

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Showing 2025Show all

14 papers · 1 filter

cs.CR2025

EAGER: Edge-Aligned LLM Defense for Robust, Efficient, and Accurate Cybersecurity Question Answering

Onat Gungor, Roshan Sood, Jiasheng Zhou +1

Large Language Models (LLMs) are highly effective for cybersecurity question answering (QA) but are difficult to deploy on edge devices due to their size. Quantization reduces memo…

cs.CR2025

AQUA-LLM: Evaluating Accuracy, Quantization, and Adversarial Robustness Trade-offs in LLMs for Cybersecurity Question Answering

Onat Gungor, Roshan Sood, Harold Wang +1

Large Language Models (LLMs) have recently demonstrated strong potential for cybersecurity question answering (QA), supporting decision-making in real-time threat detection and res…

cs.CR2025

LIGHT-HIDS: A Lightweight and Effective Machine Learning-Based Framework for Robust Host Intrusion Detection

Onat Gungor, Ishaan Kale, Jiasheng Zhou +1

The expansion of edge computing has increased the attack surface, creating an urgent need for robust, real-time machine learning (ML)-based host intrusion detection systems (HIDS)…

cs.CR2025

SAGE: Sample-Aware Guarding Engine for Robust Intrusion Detection Against Adversarial Attacks

Jing Chen, Onat Gungor, Zhengli Shang +1

The rapid proliferation of the Internet of Things (IoT) continues to expose critical security vulnerabilities, necessitating the development of efficient and robust intrusion detec…

cs.CR2025

ReLATE+: Unified Framework for Adversarial Attack Detection, Classification, and Resilient Model Selection in Time-Series Classification

Cagla Ipek Kocal, Onat Gungor, Tajana Rosing +1

Minimizing computational overhead in time-series classification, particularly in deep learning models, presents a significant challenge due to the high complexity of model architec…

cs.CR2025

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection

Elvin Li, Onat Gungor, Zhengli Shang +1

The Internet of Things (IoT), with its high degree of interconnectivity and limited computational resources, is particularly vulnerable to a wide range of cyber threats. Intrusion…