AI Enhanced Cyber Security Methods for Anomaly Detection

As cyber threats become more sophisticated, there is a greater demand for enhanced anomaly detection technologies to strengthen cybersecurity defenses. This paper presents a comprehensive review of AI-enhanced cybersecurity methods for anomaly detection. These technologies use artificial intelligence (AI) and machine learning (ML) techniques to discover anomalous patterns and behaviors that may indicate possible security vulnerabilities. The paper investigates several methodologies, such as supervised and unsupervised learning, deep learning, and ensemble methods, and demonstrates their usefulness in detecting known and new abnormalities. Behavioral analysis, particularly User and Entity Behavior Analytics (UEBA), is critical for recognizing departures from established standards. Feature engineering techniques are examined for extracting relevant information from raw data, enhancing the discriminatory power of the models. The concept of continuous learning is emphasized, stressing the importance of adaptive models that evolve to counter emerging threats in real-time. Network traffic analysis, focusing on packet inspection and flow analysis, is discussed as a fundamental component of anomaly detection. Integration with threat intelligence feeds is explored to enrich data and improve the models’ capability to identify known threats. User and device profiling, along with endpoint detection and response (EDR), contribute to a multi-layered defense strategy.