Enhancing Cybersecurity: Innovative Hybrid Feature Selection for Intrusion Detection

Network security has evolved into a major issue that necessitates careful consideration in the present digital world. Intrusion Detection Systems (IDS) are critical in detecting and preventing network intrusions. In this work, we propose a feature selection and majority vote based intrusion detection method. Using the Majority Voting approach, an intrusion detection system with many models is created. The proposed method was tested using the NSL-KDD benchmark dataset. To improve the model's performance, we combined the BestFirst search strategy with the Correlation Feature Selection (CFS) technique. This strategy successfully reduced the number of available features from 41 to 12 while maintaining detection accuracy. The experiment findings reveal that the suggested model has an accuracy rate of 96.49%, indicating the method's worth in selecting the most relevant and instructive features for the classification operation. This research contributes significantly to the development of more efficient and effective intrusion detection systems by emphasizing the role of feature selection in improving classification model performance in detecting network security threats.