Intrusion Detection System Using Improved Convolution Neural Network

Abstract

In the ever-evolving landscape of cybersecurity, the rapid proliferation of network attacks—ranging from simple port scans to sophisticated Advanced Persistent identification of malicious activity within network traffic has become critical. Traditional intrusion detection systems (IDS) often rely on signature-based or shallow learning techniques, which can be ineffective against novel or obfuscated attacks. This research presents an improved Convolutional Neural Network (CNN)- based An Intrusion Detection System (IDS) is a security tool designed to monitor network or system activity for malicious events or policy violations. The core purpose of an IDS is to act as a digital watchdog, identifying potential threats and alerting administrators. detection accuracy while maintaining computational efficiency. The proposed model introduces architectural modifications to standard CNNs, including optimized kernel sizes, adaptive pooling strategies, and feature fusion techniques to better capture temporal and spatial patterns in network traffic data.