A Computer Network Security Intrusion Detection Algorithm Based on Deep Learning
Xuan Song, Meng Song, Xiaomei Li · 2024
The rapid advancement of network technology has led to a surge in network security concerns. Detecting various intrusion behaviors promptly and precisely has become a critical issue that demands urgent attention in network security. Traditional machine learning (ML) methods for intrusion detection, which rely on feature extraction and separation, often encounter challenges such as limited detection capabilities and high false positive rates when confronted with intricate and evolving network attacks. To address these challenges, this article introduces a deep learning (DL) based algorithm for computer network security intrusion detection. The aim is to enhance detection accuracy and minimize false alarms. This algorithm leverages the DL model to autonomously extract deep-level features from network traffic data and accurately recognize network intrusion behaviors through the construction of an efficient neural network architecture. Experimental results reveal that the proposed DL-based intrusion detection algorithm surpasses traditional ML detection methods in terms of accuracy and processing speed. This algorithm not only accurately identifies known attack behaviors but also effectively responds to unknown attack patterns, exhibiting remarkable generalization capabilities.