Onat Gungor
3 indexed papers
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The paper proposes INTARG, an informed and selective adversarial attack framework for time-series forecasting that significantly increases prediction error by targeting only the most vulnerable time steps.
The paper introduces CAN-QA, a novel question-answering benchmark that reformulates CAN traffic analysis from a classification task to a reasoning task, demonstrating that current LLMs struggle with complex temporal and behavioral reasoning over vehicle network data.
The paper introduces CYBERMASKQA, a novel privacy-aware benchmark designed to evaluate Large Language Models' ability to perform accurate cybersecurity question answering while simultaneously preserving sensitive information.
Papers
CyberMaskQA: A Privacy-Aware Benchmark for Evaluating Large Language Models in Cybersecurity Question Answering
The paper introduces CYBERMASKQA, a novel privacy-aware benchmark designed to evaluate Large Language Models' ability to perform accurate cybersecurity question answering while simultaneously preservi…