An Efficient NIDS Using Ensemble Techniques for Multinominal Classification

Ensuring robust network security is a critical priority in modern network administration, as systems are constantly exposed to both known vulnerabilities and emerging threats. Intrusion detection plays a vital role in safeguarding these systems by identifying malicious activities that compromise confidentiality, integrity, or availability. To address these challenges, this work presents the development of a Network Intrusion Detection System (NIDS) capable of accurately detecting and classifying multiple categories of cyberattacks, including Denial of Service (DoS), Probe, User to Root (U2R), and Remote to Local (R2L), while maintaining a low false alarm rate, even under high network traffic. The proposed system leverages ensemble learning and advanced data mining classification techniques to enhance detection accuracy and efficiency. Using the benchmark NSLKDD dataset, which includes attack-type labels and difficulty levels, the model is trained to recognize diverse attack patterns and determine their specific categories. Comprehensive parameter tuning further optimizes performance, enabling the NIDS to serve as a reliable and scalable security solution for real-world network environments.