AI-Driven Detection of Advanced Persistent Threats in Enterprise Networks

Advanced Persistent Threats (APTs) can be regarded as one of the most complicated cyber threats that target enterprise networks. APTs involve the use of stealth technologies, persistence, and several stages of intrusion to stay undetected for long periods of time and to exfiltrate organization’s sensitive data. At the same time, traditional signature-based IDS and SIEM solutions are unable to detect zero-day exploits, lateral movement, and fileless malware utilized by modern APT groups. There has been made significant progress recently in development of AI-based techniques like Machine Learning (ML) and Deep Learning (DL) models aimed at detection of behavioral anomalies in cyber activity. This paper suggests an AI-based solution to detection of APTs in enterprise networks based on network traffic analysis, endpoint detection, user behavior analytics, and threat intelligence. The suggested system is based on sequence learning using Transformers, Graph Neural Networks (GNNs), Autoencoders, and XAI models. The experiments carried out on several benchmark cybersecurity datasets proved superiority of the suggested solution compared to the conventional ML approaches. The proposed framework guarantees high precision, recall, and F1 score and enables real-time threat detection.