Advancing Cybersecurity with AI for Intrusion Detection Systems: A Review and Analysis of Tools, Techniques, and Challenges

Abstract

The state of cybersecurity can be improved with the help of AI-based Intrusion Detection Systems (IDS) because they enable the recognition of sophisticated threats in a dynamic network environment in an intelligent, dynamic, and real-time manner. The paper goes even deeper into the design of IDS, such as host-based, network-based, and hybrid, followed by the conventional detection methods, such as signature-based, anomaly-based, and specification-based. The paper continues to discuss the more advanced AI practices, noting ML and DL models such as SVM, Random Forest, CNN, and LSTM, which enhance the identification of known and zero-day attacks. The report continues by examining some of the enabling technologies, such as cloud-based IDS, Big data streaming platforms such as Kafka and Spark, AI-based security applications, and ML frameworks. Applications, such as real-time monitoring, multi-tenant security and DevSecOps integration, are explained alongside problematic issues, which are noisy data, computational overhead, encrypted traffic, adversarial attacks and limited real-world implementation. Overall, this study emphasises the need for AI-based IDS that are scalable, efficient, and strong enough to tackle all the new cyber threats.