Majlesi Journal of Electrical Engineering A review on recent approaches of Machine Learning, Deep Learning, and Explainable Artificial intelligence in Intrusion Detection Systems Volume 17 (2023) Issue 1, March 2023 2024 02 03 A review on recent approaches of Machine Learning, Deep Learning, and Explainable Artificial intelligence in Intrusion Detection Systems 10.30486/mjee.2023.1976657.1046 EN Seshu Bhavani Mallampati VIT-AP University, School of Computer Science and Engineering, Near Vijayawada, Andhra Pradesh, India Seetha Hari VIT-AP University, Center of Excellence, AI and Robotics, Near Vijayawada, Andhra Pradesh, India Journal Article 2024 02 03 In recent decades, network security has become increasingly crucial, and intrusion detection systems play a critical role in securing it. An intrusion Detection System (IDS) is a mechanism that protects the network from various possible intrusions by analyzing network traffic. It provides confidentiality and ensures the integrity and availability of data. Intrusion detection is a classification task that classifies network data into benign and attack by using various machine learning and deep learning models. It further develops a better potential solution for detecting intrusions across the network and mitigating the false alarm rate efficiently. This paper presents an overview of current machine learning (ML), deep learning (DL), and eXplainable Artificial intelligence (XAI) techniques. Our findings provide helpful advice to researchers who are thinking about integrating ML and DL models into network intrusion detection. At the conclusion of this work, we outline various open challenges.