Artificial Intelligence Based Intrusion Detection Systems

Breakthroughs in deep learning (DL) and machine learning (ML) have revolutionized cybersecurity by remarkably improving detection performances and achieving Turing completeness to rapidly changing attack scenario. These developments have made AI-based intrusion detection systems (IDS) one of the most important technologies for networked and Internet of Things (IoT) security. This work presents an in-depth overview of state-of-the-art IDS approaches such as Bi-Directional Long Short-Term Memory (Bi-LSTM) networks, Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs) and ensembles. Particular attention is given to dealing with the specific challenges that arise with integration of IoT devices, whose safety is often in jeopardy due to sophisticated cyber-attacks, as well as with the heterogeneity of modern network structures. Analysis reveals the advantages and disadvantages of each method and demonstrates that the Bi-LSTM, and GANs methods perform better in dynamic, complex data streams, while ensemble methods enhance the robustness and threat detection performance of the statistical classifier. Using these sophisticated AI approaches, IDS can be improved to provide security enhancements, delivering crucial information for securing IoT ecosystem and addressing the threats posed by complex network architectures.