2 citations · 2 across the 28 of their papers we have counts for
12 papers · 1 filter
CyberMaskQA: A Privacy-Aware Benchmark for Evaluating Large Language Models in Cybersecurity Question Answering
Matilda Gaddi, Jin Noh, Onat Gungor +1
Large language models (LLMs) are increasingly applied to cybersecurity question answering (QA) for critical tasks such as incident response and vulnerability analysis. However, rea…
CAN-QA: A Question-Answering Benchmark for Reasoning over In-Vehicle CAN Traffic
Jing Chen, Abhijay Deevi, Onat Gungor +1
The Controller Area Network (CAN) is a safety-critical in-vehicle communication protocol that lacks built-in security mechanisms, making intrusion detection essential. Existing app…
ACORN-IDS: Adaptive Continual Novelty Detection for Intrusion Detection Systems
Sean Fuhrman, Onat Gungor, Tajana Rosing
Intrusion Detection Systems (IDS) must maintain reliable detection performance under rapidly evolving benign traffic patterns and the continual emergence of cyberattacks, including…
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…
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…
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)…
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…
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…
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…
DYNAMITE: Dynamic Defense Selection for Enhancing Machine Learning-based Intrusion Detection Against Adversarial Attacks
Jing Chen, Onat Gungor, Zhengli Shang +2
The rapid proliferation of the Internet of Things (IoT) has introduced substantial security vulnerabilities, highlighting the need for robust Intrusion Detection Systems (IDS). Mac…
CND-IDS: Continual Novelty Detection for Intrusion Detection Systems
Sean Fuhrman, Onat Gungor, Tajana Rosing
Intrusion detection systems (IDS) play a crucial role in IoT and network security by monitoring system data and alerting to suspicious activities. Machine learning (ML) has emerged…
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection
Elvin Li, Zhengli Shang, Onat Gungor +1
The proliferation of IoT devices has significantly increased network vulnerabilities, creating an urgent need for effective Intrusion Detection Systems (IDS). Machine Learning-base…