Intelligent Attack Detection Mechanism Design and Analysis in Network Security

In the evolving landscape of cyberspace, the sophistication and frequency of network attacks have increased significantly, necessitating advanced security solutions. This research presents the design and analysis of an intelligent attack detection mechanism leveraging machine learning, deep learning, and hybrid intrusion detection systems (IDS). The proposed framework integrates signature-based and anomaly-based detection techniques to identify known and zero-day threats with high accuracy. Feature engineering and dimensionality reduction methods are employed to optimize detection speed while minimizing false positives. Experimental evaluation is conducted using benchmark datasets such as NSLKDD, CICIDS2017, and UNSW-NB15, achieving superior detection rates compared to traditional IDS approaches. The results demonstrate that the hybrid model outperforms standalone methods in terms of accuracy, precision, recall, and F1-score, making it a promising solution for real-time network threat detection and prevention.