These supervised learning systems are able to learn to detect and identify known threats but are unable to react to unknown threats. To this end, we have ...
Therefore, in this paper, we propose a novel artificial intelligence based constraint learning technique to help their detection. The approach creates an ...
Oct 11, 2024 · This dissertation aims to develop an automated end-to-end threat hunting model, harnessing the power of Large Language Models (LLMs) to enhance threat ...
Apr 18, 2025 · To address these issues, this study introduces an Explainable and Lightweight AI (ELAI) framework designed for real-time cyber threat detection ...
Aug 8, 2024 · This paper examines the feasibility of employing LLMs as a Network Intrusion Detection System (NIDS), despite their high computational requirements.
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This section provides an up-to-date, comprehensive survey of recent approaches that address insider threat detection: (i) machine learning (ML) and deep ...
This study aims to propose an improved trustworthy insider threat detection method that ensures two of the trustworthy learning requirements: Privacy and ...
May 22, 2025 · ... threat intelligence in Security Operations Centers (SOCs) has accelerated threat detection and response but introduced new challenges related to ...
May 2, 2025 · To address these issues, this study introduces an Explainable and Lightweight AI (ELAI) framework designed for real-time cyber threat detection ...
Jul 9, 2025 · Incorporating explainable AI techniques can provide greater transparency in threat detection, enabling security professionals to comprehend ...