Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Cyber threat network security is of importance because the technology is progressively turning out to be important in the era of heightened digitalization. Improving cyber intrusion detection systems is crucial because cyberattacks on critical infrastructure are becoming more targeted and sophisticated. Using the NSL-KDD dataset for IDS is best accomplished with a CNN-LSTM model, according to the research. The workflow contains expensive preprocessing such as label encoding, feature selection, and z-score normalization, and 70 30 train-test splits. The spatial features of the traffic data are learnt with CNN and the sequential dependencies are performed by LSTM, which allows the robust classification of normal and attack traffic. The suggested model outperforms standard classifiers like SVM and LR experimentally, with results of 94.32% accuracy, 99.32% precision, 97.88% recall, and 98.60% F1-score. Based on these findings, CNN-LSTM hybrid is an intriguing method for enhancing detection accuracy while decreasing false positives. Deep learning-based intruder detection systems could help improve cybersecurity, but further research is needed to evaluate their performance on new data, address class imbalance, and enable real-time operation, as the study suggests.