A Hybrid Framework for Intrusion Detection and Attack Classification Using Network Security and Data Mining Techniques

Abstract

The swift growth of cloud platforms, network-based services, and IoT infrastructures has led to an increase in the frequency and sophistication of assaults. Due to their limited classification capabilities and reliance on signature-based methodologies, traditional intrusion detection systems frequently fall short in identifying new threats. This study suggests an integrated method for intrusion detection and attack classification utilising deep learning and intelligent machine learning approaches in order to address these issues. Network traffic data collection, preprocessing, feature extraction and selection, intrusion detection, and multi-class attack categorization are some of the steps that make up the suggested framework. To improve detection accuracy and categorise assaults into groups including Denial of Service (DoS), Distributed DoS (DDoS), Probe, Remote-to-Local (R2L), User-to-Root (U2R), and malware incursions, a hybrid deep learning model is used. In order to facilitate real-time response and mitigation, the system also includes an alert production mechanism. Comparing the suggested integrated model to traditional IDS techniques, experimental results on benchmark intrusion datasets show that it improves classification performance, lowers false positive rates, and increases accuracy. All things considered, the suggested framework offers a scalable and effective way to handle proactive cyber threat management and next-generation network security.