Intrusion Detection System Using Deep Learning for Network Security

TL;DR

This paper evaluates various deep learning models like CNN, ANN, and LSTM for intrusion detection, demonstrating their effectiveness in classifying network traffic with high accuracy to improve network security.

Contribution

It provides an experimental comparison of deep learning architectures for IDS, highlighting their performance and trade-offs in real network traffic scenarios.

Findings

Best model achieved 96% accuracy

Deep learning models outperform traditional IDS methods

LSTM showed strong performance in sequence classification

Abstract

As the number of cyberattacks and their particualr nature escalate, the need for effective intrusion detection systems (IDS) has become indispensable for ensuring the security of contemporary networks. Adaptive and more sophisticated threats are often beyond the reach of traditional approaches to intrusion detection and access control. This paper proposes an experimental evaluation of IDS models based on deep learning techniques, focusing on the classification of network traffic into malicious and benign categories. We analyze and retrain an assortment of architectures, such as Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), and LSTM models. Each model was tested based on a real dataset simulated in a multi-faceted and everchanging network traffic environment. Among the tested models, the best achieved an accuracy of 96 percent, underscoring the potential of deep learning models in improving efficiency and rapid response in IDS systems. The goal of the research is to demonstrate the effectiveness of distinct architectures and their corresponding trade-offs to enhance framework development for adaptive IDS solutions and improve overall network security.