Deep Learning-Based Intrusion Detection System for Evolving Cyber Threats in High-Speed Networks

The rapid growth of computer networks, cloud computing, and Internet-based services has substantially increased the attack surface against cyber threats. The latest cyber attacks, such as zero-day attacks, advanced persistent threats (APTs), and polymorphic malware, are constantly evolving and are difficult to identify using traditional security solutions. Traditional intrusion detection systems (IDS), which are based on static rules and known attack signatures, are unable to cope with these evolving threats and tend to produce high false-positive rates, thereby reducing their efficiency in practical scenarios. This paper presents an intelligent deep learning-based intrusion detection system that can automatically identify complex and non-linear patterns from network traffic data. Using deep neural networks for feature extraction and classification, the proposed system improves the accuracy, scalability, and real-time performance of intrusion detection. Experimental results show that the proposed system outperforms traditional IDS techniques and can be effectively used in modern high-speed networks.