Enhancing Threat Detection and Mitigation Strategies through Machine Learning and Artificial Intelligence in Cybersecurity and Network Security
Abstract
Cybersecurity and network security have become critical areas of focus due to the increasing complexity and volume of cyber threats. Traditional security systems often struggle to keep pace with the evolving nature of these threats. This paper explores how machine learning (ML) and artificial intelligence (AI) can enhance threat detection and mitigation strategies. AI and ML enable real-time threat identification, predictive analysis, and automated response mechanisms, improving overall security posture. The paper provides a comprehensive literature review of existing research before 2024, highlighting various ML and AI-based security models. It further discusses the challenges and limitations of implementing AI-driven security measures and presents potential future research directions. Graphs, tables, and flowcharts are included to demonstrate the impact and effectiveness of AI and ML in cybersecurity.
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