Authors : Almash Saifi; Mukul Sharma; Mragesh Pratap Singh

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/ye4vbzsb

DOI : https://doi.org/10.38124/ijisrt/25may2116

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The development of intelligent threat detection systems is required due to the increasing complexity and frequency of cyber attacks. With the use of sophisticated anomaly detection and behavioural analysis, artificial intelligence (AI) has become a crucial element in improving network security. With an emphasis on AI techniques applicable to network behavior analysis, current machine learning algorithms for anomaly detection, theoretical risk evaluation of institutional network threats, and best practices for deploying AI-driven detection systems in real-world networks, this paper provides an extensive review of recent literature on AI-driven threat detection. Additionally, by incorporating knowledge from recent studies and business procedures, we offer a conceptual architecture for an AI-based threat detection system.

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