Enhancing intrusion detection systems: Innovative deep learning approaches using CNN, RNN, DBN and autoencoders for robust network security

Abstract

The rapid increase in sophisticated cyber threats necessitates the evolution of Intrusion Detection Systems (IDS) to ensure robust network security. Traditional IDS often struggle with high false positive and negative rates, and fail to detect complex intrusion patterns. This study investigates the integration of deep learning algorithms with autoencoders to address these challenges, proposing a novel approach to IDS enhancement. Deep learning algorithms are implemented and optimized to improve detection accuracy and reduce false alerts. Additionally, autoencoders are utilized for feature extraction and dimensionality reduction, enhancing the IDS’s ability to identify subtle and intricate anomalies. The study evaluates the impact of autoencoder based preprocessing on the performance of deep learning models, hypothesizing that this integration significantly boosts detection rates and efficiency. A comprehensive comparative analysis is conducted, assessing the performance of traditional deep learning algorithms with and without autoencoder integration. Various performance metrics, including accuracy, precision, recall, F1 score, and computational efficiency, are employed to evaluate the models. The study explores the benefits and trade-offs of incorporating autoencoders, particularly in terms of detection accuracy, false positive/negative rates, and real-time applicability. This research provides valuable insights into the deployment of advanced IDS in real world scenarios, highlighting the most effective approaches for leveraging deep learning and autoencoder techniques. The findings aim to contribute significantly to the field of network security, offering enhanced reliability and robustness in intrusion detection.