The application of machine learning algorithms and techniques to cybersecurity problems, including threat detection, anomaly identification, and automated response capabilities.
Machine learning enhances insider threat detection through supervised learning for known threat patterns, unsupervised learning for anomaly detection, and reinforcement learning for adaptive response systems. Common algorithms include random forests for classification, neural networks for complex pattern recognition, and clustering algorithms for behavioral grouping. However, ML systems require careful tuning to avoid high false positive rates and can be vulnerable to adversarial attacks where insiders deliberately modify their behavior to evade detection.