AI-Driven: Network Intrusion Detection Systems

Abstract

Modern computer networks, particularly those sup- porting Internet of Things (IoT), cloud, and edge environments, are increasingly exposed to sophisticated cyber-attacks such as zero-day exploits and stealthy intrusion techniques. Traditional Intrusion Detection Systems (IDS) primarily rely on signature- based or rule-based mechanisms, which limits their effective- ness against evolving threats. Recent advancements in Artificial Intelligence (AI) and Deep Learning (DL) have enabled IDS to automatically learn complex patterns from network traffic, significantly improving detection capability. This review paper presents a comprehensive survey of AI-driven IDS frameworks, focusing on deep learning, self- supervised learning, graph-based models, and hybrid approaches. Particular attention is given to recent trends addressing key limitations of existing systems, including lack of robustness to adversarial attacks and limited explainability of model decisions. Techniques such as adversarial training and Explainable Artificial Intelligence (XAI) are reviewed for their role in improving system reliability and analyst trust. The paper highlights challenges such as dataset imbalance, adversarial vulnerability, and scalability issues, and discusses future research directions for developing adaptive, reliable, and transparent intrusion detection systems suitable for modern network environments.

Introduction

The growing complexity of modern digital networks—driven by cloud computing, IoT, and 5G—has increased both the volume of network traffic and the sophistication of cyberattacks. Intrusion Detection Systems (IDS) are essential for identifying malicious activities, but traditional signature- and rule-based IDS are limited to known threats and struggle with zero-day and evolving attacks.

To overcome these limitations, researchers have increasingly applied Artificial Intelligence (AI) and Machine Learning (ML). Early ML-based IDS models such as Decision Trees, Support Vector Machines, and Random Forests improved detection accuracy but depended heavily on manual feature engineering and faced challenges with high-dimensional data, class imbalance, and scalability. Deep learning approaches have significantly advanced IDS by enabling automatic feature extraction and learning complex patterns directly from raw network data.

Recent research highlights the evolution toward deep learning, self-supervised, and hybrid IDS architectures. Models combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) such as LSTMs effectively capture spatial and temporal traffic patterns. To address data imbalance and limited labeled data, self- and semi-supervised methods have been introduced. Security concerns related to adversarial attacks have led to the development of robust and adversarially trained IDS models, while lightweight architectures target resource-constrained IoT environments.

Conclusion

This review surveyed recent advancements in AI-driven Intrusion Detection Systems, focusing on deep learning, self-supervised learning, explainability, and adversarial robustness. Deep learning architectures such as CNNs, RNNs, Autoen- coders, and Graph Neural Networks have significantly en- hanced IDS performance by learning complex traffic repre- sentations. Despite notable progress, challenges remain in address- ing dataset imbalance, adversarial vulnerability, computational overhead, and privacy concerns. Future research should ex- plore federated and privacy-preserving IDS models, scalable architectures for IoT environments, and interpretable AI tech- niques for real-world deployment. Overall, AI-driven IDS represent a crucial step toward intelligent, adaptive, and trust- worthy cybersecurity defense systems.

References

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Copyright

Copyright © 2025 Amal Murali, Dharvish K. S., Fasil Shah T. S., Sreedil V. J., Mahshiya V. M.. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.