AI-Powered Anomaly Detection in Cybersecurity: Leveraging Deep Learning for Intrusion Prevention

The progression of cyber-attacks is forcing traditional Intrusion Detection Systems (IDS) that rely on signature detection systems to quickly become obsolete because such systems are incapable of identifying novel and adaptive threats like zero-day and advanced persistent threats (APTs). Basically, they are limited because they rely on predefined attack patterns and cannot identify new or evolving types of attacks. A deep learning model to improve the overall defense against cybersecurity threats is proposed with an AI powered framework for anomaly detection. The proposed framework enhances real time detection and prevention of intrusions by integrating autoencoders, graph neural networks (GNNs) with long short-term memory (LSTM) networks. Autoencoders help in discovering new anomalies in traffic patterns, neural networks GNNs help in understanding the intricate interrelations and LSTMs help in understanding attacks over time. This study implements XAI anomaly detection methods and combines them with SHAP and LIME for greater transparency and trust over the model. Additionally, unsupervised learning is employed for pattern recognition to guarantee that no pre-assigned data is required. This work significantly advances the domain of AI-based automated anomaly detection and offers a scalable, adaptable, and explainable approach to the problem of cybersecurity. As described in the paper, the model has far-reaching consequences for safeguarding vital infrastructures in the domains of health care, banking, and governmental administration by providing improved protection from various sophisticated cyber threats.