4 citations · 6 across the 22 of their papers we have counts for
8 papers · 1 filter
RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection
Quanling Zhao, Jiaying Yang, Ye Tian +5
Unsupervised tabular anomaly detection requires methods that are accurate, robust across heterogeneous datasets, and computationally efficient. Classical statistical detectors are…
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…
FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis
Woojin Lee, Pranav Mekkoth, Ye Tian +2
The widespread adoption of camera-equipped mobile devices and wearables has enabled convenient capture of meal images, making food recognition a key component for real time dietary…
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…
INTARG: Informed Real-Time Adversarial Attack Generation for Time-Series Regression
Gamze Kirman Tokgoz, Onat Gungor, Tajana Rosing +1
Time-series forecasting aims to predict future values by modeling temporal dependencies in historical observations. It is a critical component of many real-world systems, where acc…
KLDrive: Fine-Grained 3D Scene Reasoning for Autonomous Driving based on Knowledge Graph
Ye Tian, Jingyi Zhang, Zihao Wang +4
Autonomous driving requires reliable reasoning over fine-grained 3D scene facts. Fine-grained question answering over multi-modal driving observations provides a natural way to eva…
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…
QMC: Efficient SLM Edge Inference via Outlier-Aware Quantization and Emergent Memories Co-Design
Nilesh Prasad Pandey, Jangseon Park, Onat Gungor +2
Deploying Small Language Models (SLMs) on edge platforms is critical for real-time, privacy-sensitive generative AI, yet constrained by memory, latency, and energy budgets. Quantiz…