AI-Driven Anomaly Detection for Advanced Threat Detection

Abstract : In the rapidly evolving digital landscape, cyber threats are becoming increasingly sophisticated, making traditional security measures inadequate. Advanced Threat Detection (ATD) leveraging Artificial Intelligence (AI)-driven anomaly detection systems offers a proactive approach to identifying and mitigating cyber threats in real time. This paper explores the integration of AI, particularly machine learning (ML) and deep learning (DL) techniques, in anomaly detection to enhance cybersecurity defenses. By analyzing vast amounts of network traffic, user behavior, and system logs, AI-driven models can identify deviations from normal patterns, enabling early threat detection and prevention. These systems excel in detecting zero-day attacks, insider threats, and advanced persistent threats (APTs), which often bypass conventional rule-based security mechanisms. Additionally, we discuss the challenges of AI-based anomaly detection, including false positives, model interpretability, and adversarial attacks. The findings emphasize the need for continuous learning and adaptive security frameworks to ensure robust cyber threat detection. The study concludes that AI-driven anomaly detection significantly enhances threat intelligence and response capabilities, making it a vital component of modern cybersecurity strategies.

Keyword: Advanced Threat Detection (ATD), Artificial Intelligence (AI) in Cybersecurity Machine Learning (ML) for Security, Deep Learning (DL) in Cyber Threat Detection, Anomaly Detection Systems, Intrusion Detection and Prevention Systems (IDS/IPS), Zero-Day Attacks, Advanced Persistent Threats (APTs).