Published: Dec. 19, 2024
Language: Английский
Published: Dec. 19, 2024
Language: Английский
Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(11)
Published: Sept. 18, 2024
Abstract In recent years, Advanced Persistent Threat (APT) attacks on network systems have increased through sophisticated fraud tactics. Traditional Intrusion Detection Systems (IDSs) suffer from low detection accuracy, high false-positive rates, and difficulty identifying unknown such as remote-to-local (R2L) user-to-root (U2R) attacks. This paper addresses these challenges by providing a foundational discussion of APTs the limitations existing methods. It then pivots to explore novel integration deep learning techniques Explainable Artificial Intelligence (XAI) improve APT detection. aims fill gaps in current research thorough analysis how XAI methods, Shapley Additive Explanations (SHAP) Local Interpretable Model-agnostic (LIME), can make black-box models more transparent interpretable. The objective is demonstrate necessity explainability propose solutions that enhance trustworthiness effectiveness models. offers critical approaches, highlights their strengths limitations, identifies open issues require further research. also suggests future directions combat evolving threats, paving way for effective reliable cybersecurity solutions. Overall, this emphasizes importance enhancing performance systems.
Language: Английский
Citations
5Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110161 - 110161
Published: Feb. 21, 2025
Language: Английский
Citations
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Published: March 1, 2025
Language: Английский
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Published: Dec. 7, 2024
Language: Английский
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0Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 122, P. 109988 - 109988
Published: Dec. 14, 2024
Language: Английский
Citations
0Published: Dec. 19, 2024
Language: Английский
Citations
0