Network Intrusion Detection Utilizing Autoencoder Neural Networks

Abstract

In today's interconnected digital landscape, protecting computer networks from unauthorized access and cyber threats is critically important. Network Intrusion Detection Systems (NIDS) play a vital role in identifying and mitigating potential security breaches. This research paper examines the use of autoencoder neural networks, a subset of deep learning techniques, in the field of Network Intrusion Detection.Autoencoder neural networks are renowned for their ability to learn and represent data in a compressed, low-dimensional form. This study explores their potential to model network traffic patterns and identify anomalous activities. By training autoencoder networks on both normal and malicious network traffic data, we aim to develop effective intrusion detection models capable of distinguishing between benign and malicious network behavior.The paper provides a comprehensive analysis of the architecture and training methodologies of autoencoder neural networks for intrusion detection. It also investigates various data preprocessing techniques and feature engineering approaches to improve the model's performance. Additionally, the research assesses the robustness and scalability of autoencoder-based NIDS in real-world network environments.Ethical considerations in network intrusion detection, including privacy concerns and false positive rates, are also discussed. This approach ensures a balanced methodology that secures networks while respecting user privacy and minimizing disruptions. By compressing majority samples and increasing the minority sample count in challenging scenarios, the IDS achieves higher classification accuracy.