Maria Mpitsi
Faculty of Mathematics and Natural Sciences
South-West University – Blagoevgrad (Bulgaria)
https://doi.org/10.53656/math2024-6-7-art
Abstract. This paper offers a critical review of the applications of AI in cybersecurity, focusing on the recent trends of automation in threat detection, enhancement of response strategies, and prediction of vulnerabilities.
The methodology is based on a thorough analysis of empirical studies up to 2021 as per the efficiency of AI malware detection, insider threat identification, and mitigation of zero-day vulnerabilities. In particular, machine learning- and deep learning-based methodologies of artificial intelligence ensure clear advantages over conventional models concerning the precision in detection and reduction of false positives. However, challenges persist regarding explainability, scalability, and ethical concerns around data bias and quality. Finally, this paper concludes by pointing out some areas of future research with regard to needing XAI techniques and methods related to bias reduction to establish better trust in the efficacy of AI-driven cybersecurity frameworks.
Keywords: artificial intelligence, cybersecurity, machine learning, explainable AI, anomaly detection
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