AI-Powered Cybersecurity: A Technical Review of Intrusion Detection Models, Tools, and Metrics
Wael Maged Badawy · 2025
As cyber threats grow in complexity and scale, traditional Intrusion Detection Systems (IDS) are proving increasingly inadequate. This paper presents a technical review of the integration of Artificial Intelligence (AI) into IDS, focusing on models, tools, and evaluation metrics drawn from existing literature. Various machine learning (ML) and deep learning (DL) approaches-such as Support Vector Machines (SVM), Random Forests (RF), Convolutional Neural Networks (CNN), and Generative Adversarial Networks (GANs)-are analyzed in terms of accuracy, false positive rates, scalability, and computational efficiency. Widely adopted IDS tools including Snort, Suricata, and Zeek are examined for AI integration capabilities. Challenges such as dataset bias, adversarial attacks, and deployment constraints are discussed. Special attention is given to emerging directions like explainable AI (XAI), federated learning, and hybrid IDS. While the study is literature-based, it offers critical analysis, taxonomy, and deployment guidelines to aid practitioners in selecting appropriate AI models for cloud, IoT, or enterprise scenarios. The paper concludes with a synthesis of open research challenges and limitations to guide future work.