Vol. 1, Issue 1, Part A (2024)

The growing sophistication and frequency of cyber threats, fueled by increasing digitalization and interconnected systems, have exposed the limitations of traditional cybersecurity measures. Conventional signature-based threat detection often falls short in identifying novel or evolving attacks. To address these shortcomings, Artificial Intelligence (AI) and Machine Learning (ML) offer powerful solutions capable of automating anomaly detection and strengthening security defenses. These intelligent models are trained to recognize normal behavior and can swiftly flag deviations that may indicate cyber intrusions. This paper examines the applications of AI and ML in anomaly detection within cybersecurity, discussing the methodologies, benefits, challenges, and emerging trends. The integration of AI-driven anomaly detection enables organizations to identify threats proactively, minimize false positives, and enhance the protection of sensitive data and network infrastructures. Although concerns surrounding data requirements, algorithm transparency, and fairness remain, continuous innovations and responsible governance can advance the reliability and ethical deployment of these technologies for securing the digital domain.