Open Access

REVIEW

A Review of Advancements in Deep Learning Approaches for Intrusion Detection Systems

Akash Garg^*

Department of Computer Science and Engineering, Rajkiya Engineering College, Mainpuri, India

* Corresponding Author: Akash Garg. Email:

Journal on Artificial Intelligence 2026, 8, 273-298. https://doi.org/10.32604/jai.2026.079401

Received 21 January 2026; Accepted 17 March 2026; Issue published 12 May 2026

Abstract

As cyber threats continue to evolve in scale and sophistication, the need for intelligent and adaptive security mechanisms has become increasingly urgent. Intrusion Detection Systems (IDS) are critical components in safeguarding computer networks from malicious activities. This review paper presents a comprehensive analysis of recent advancements in deep learning-based IDS, examining various architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and generative adversarial networks (GANs). The study compares traditional intrusion detection techniques with modern deep learning approaches, highlighting their strengths, limitations, and suitability for real-world deployment. Special attention is given to hybrid models that integrate anomaly and misuse detection, as well as techniques that address challenges such as data imbalance, feature selection, and real-time detection. Benchmark datasets like KDD’99, NSL-KDD, and UNSW-NB15 are discussed in the context of evaluating IDS performance. The review concludes that deep learning offers significant promise in enhancing the accuracy, adaptability, and scalability of IDS, though challenges remain in terms of computational cost and interpretability. This paper serves as a resource for researchers and practitioners seeking to understand the current landscape of deep learning in network intrusion detection and identifies potential directions for future research.

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Keywords

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