Leveraging Deep Learning Models for Intrusion Detection Systems for Secure Networks

Abstract

Real-time cyber threat identification and mitigation depend heavily on intrusion detection systems (IDS) for secure networks. Network systems utilize analysis to spot malicious events unauthorized access and system vulnerabilities in network traffic. Machine learning forms the foundation of these security implementations. Through improved network security measures, IDS protect business assets by ensuring both data availability and uncompromised security. After enhancing the effectiveness of intrusion detection, this study suggests a strong method based on DL. A CNN model is developed and evaluated against traditional models, including KNN, Autoencoders (AE), and DNN that are trained on the NSL-KDD dataset. CNN delivers remarkable performance when used in network threat detection with success metrics of 98.63% accuracy alongside 98.45% precision, 98.98% recall, and an F1-score of 98.72%, proving its efficiency for threat recognition. Visualization and comparative performance analysis further prove the model's effectiveness, paving the way for its possible use in safe network settings. The benefits of using DL frameworks to improve IDS systems are highlighted in this paper.