Development of an Intelligent IDS Web Application using CNN for Network Threat Detection

Abstract

Cybersecurity threats are becoming increasingly frequent and complex as organizations rely more on interconnected digital networks. Traditional security methods often struggle to detect advanced and unknown attacks, such as zero-day exploits. Intrusion Detection Systems (IDS) are essential for monitoring network activity and identifying malicious behavior. However, conventional signature-based IDS are limited because they rely on predefined attack patterns and cannot effectively detect new or evolving threats. This paper presents the development of an Intelligent IDS Web App that integrates Artificial Intelligence (AI) techniques to improve detection accuracy and enhance network security monitoring. The system uses machine learning to analyze network traffic patterns and identify abnormal behavior that may indicate cyberattacks. The development process follows a structured methodology, covering system design, model training, and web-based deployment. A dashboard interface was implemented to visualize detection results, allowing users to monitor network activity in real time. The results show that the system can effectively analyze network traffic and detect potential intrusions through an interactive and user-friendly environment. The proposed intelligent IDS supports proactive threat detection and contributes to improving cybersecurity awareness in modern networked environments.