A Critical Review of Artificial Intelligence Based Approaches in Intrusion Detection: A Comprehensive Analysis

Abstract

Intrusion detection (ID) is critical in securing computer networks against various malicious attacks. Recent advancements in machine learning (ML), deep learning (DL), federated learning (FL), and explainable artificial intelligence (XAI) have drawn significant attention as potential approaches for ID. DL-based approaches have shown impressive performance in ID by automatically learning relevant features from data but require significant labelled data and computational resources to train complex models. ML-based approaches require fewer computational resources and labelled data, but their ability to generalize to unseen data is limited. FL is a relatively new approach that enables multiple entities to train a model collectively without exchanging their data, providing privacy and security benefits, making it an attractive option for ID. However, FL-based approaches require more communication resources and additional computation to aggregate models from different entities. XAI is critical for understanding how AI models make decisions, improving interpretability and transparency. While existing literature has explored the strengths and weaknesses of DL, ML, FL, and XAI-based approaches for ID, a significant gap exists in providing a comprehensive analysis of the specific use cases and scenarios where each approach is most suitable. This paper seeks to fill this void by delivering an in-depth review that not only highlights strengths and weaknesses but also offers guidance for selecting the appropriate approach based on the unique ID context and available resources. The selection of an appropriate approach depends on the specific use case, and this work provides insights into which method is best suited for various network sizes, data availability, privacy, and security concerns, thus aiding practitioners in making informed decisions for their ID needs.