Network Intrusion Detection System Using Optimized Feature Selection

Rapid internet growth leads to increased cyber threats, making cybersecurity crucial. Diverse attacks like DoS, U2R, R2L, Probe, and DNS Spoofing are concerning. Machine learning presents robust tools for intrusion detection, potentially replacing human analysts. This project employs machine learning algorithms on the NSL-KDD dataset to model intrusion detection. These algorithms—logistic regression, Naive Bayes, K-Nearest Neighbor, decision trees—aim to detect anomalies and standard patterns, refining classifiers for cyber assaults. Evaluation seeks to identify the most accurate classification and deep learning models. The project optimizes intrusion detection for network traffic data, leveraging diverse techniques. The outcome is an advanced intrusion detection system displaying superior precision compared to existing models, presenting a promising solution for safeguarding smart networks.