Deep Learning Techniques for Intrusion Detection Systems: A Comparative Study of Accuracy and Efficiency

Abstract

Intrusion detection systems (IDS) are vital for safeguarding large-scale networks from cyber threats. Traditional IDS approaches often struggle to balance accuracy, detection time, and resource efficiency, especially in complex environments. Recent advances in deep learning have shown promise in improving these metrics. This paper provides a comparative study of various deep learning techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and hybrid models. It assesses their performance in terms of detection accuracy, computational efficiency, and suitability for real-time applications. The findings suggest that while CNNs excel in processing large amounts of network traffic data, RNNs are better suited for temporal sequence analysis. Autoencoders, on the other hand, demonstrate efficiency in anomaly detection with minimal resource consumption. The paper concludes with insights into the practical implementation of these models and discusses future directions for enhancing IDS performance through deep learning.