Enhanced Network Intrusion Detection Using Convolutional Neural Networks

Abstract

Network security in Local Area Networks depends greatly on network intrusion detection (LAN). Due to a lack of ongoing learning, traditional defences like firewalls cannot completely prevent LAN attacks. Convolutional neural networks' (CNN) capacity to extract features in the computer vision field has recently attracted a lot of attention. Network intrusion detection uses CNN's capability to automatically extract efficient complicated features to adapt to constantly changing surroundings. In this article, we emphasize on LAN network security. We suggest a method for LAN intrusion detection that is based on CNN. This method accurately detects network assaults with a 98.34% precision on the KDD99 dataset. The experimental findings demonstrate the excellent accuracy of malware detection of the suggested strategy based on CNN.