A Deep Learning Framework for Network Intrusion Detection Using hybrid CNN-BiGRU-Multi-Head Attention model

This paper describes a fresh IDS framework that utilizes CNN and BiGRU and Multi-Head Attention techniques to develop an improved deep learning approach for network protection from changing cyber dangers. The development of modern defense techniques surpasses standard signature-based and anomaly-based systems since their inability to detect previously unknown threats and their high false positive rates makes them insufficient to combat sophisticated ransomware and phishing events. A hybrid framework was proposed to fusion CNN spatial feature extraction with BiGRU temporal dependency analysis and Multi-Head Attention feature prioritization for building an effective adaptive IDS. The goal of this system is to reduce false alarm incidents along with enhancing traffic detection precision so that it can conduct traffic monitoring that targets today’s major cyber security infrastructure problems.This experiment is conducted on the NSL-KDD dataset,showing very promising results.