AI-Driven: Detecting and Preventing Advanced Persistent Threats Cybersecurity

Abstract

Organizations must adopt improved cyber security methods that defend against cyber threats because Advanced Persistent Threats have exhibited rising sophistication in their operations. APT infiltrates organizations through extended targeted system intrusions to access secrets or break infrastructure while defying conventional sign-based security measures. The paper examines the operation of Artificial Intelligence technologies for APT detection and defense. The research develops an APT detection system in real time using machine learning and deep learning simultaneously for detecting anomalous activity and predictive modeling. The detection accuracy of AI systems increases substantially due to neural networks that show better results than normal traditional models. Standard cyber security infrastructure and false alarm management present main barriers to the deployment of this artificial intelligence system. The study focuses on Advanced Persistent Threats together with Artificial Intelligence and its linked techniques such as Anomaly Detection, Intrusion Detection Systems, and Real-time Response and Machine Learning and its subset Deep Learning.