Insider Threat

Insider threat detection aims to identify malicious activities by individuals with legitimate access to an organization's systems. Current research focuses on developing real-time, fine-grained detection methods using machine learning, particularly deep learning architectures like generative adversarial networks (GANs) and graph neural networks, often addressing data imbalance and privacy concerns through federated learning and unsupervised techniques. These advancements are crucial for improving cybersecurity by enabling earlier threat identification and more effective mitigation strategies, impacting both organizational security practices and the broader field of anomaly detection.