Proceedings of the 2nd International Conference on Environmental, Energy, and Earth Science, ICEEES 2023, 30 October 2023, Pekanbaru, Indonesia

Research Article

Enhancing Cybersecurity: Innovative Hybrid Feature Selection for Intrusion Detection

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  • @INPROCEEDINGS{10.4108/eai.30-10-2023.2343092,
        author={Guntoro  Guntoro and Lisnawita  Lisnawita and Loneli  Costaner},
        title={Enhancing Cybersecurity: Innovative Hybrid Feature Selection for Intrusion Detection},
        proceedings={Proceedings of the 2nd International Conference on Environmental, Energy, and Earth Science, ICEEES 2023, 30 October 2023, Pekanbaru, Indonesia},
        publisher={EAI},
        proceedings_a={ICEEES},
        year={2024},
        month={4},
        keywords={intrusion detection system correlation feature selection (cfs) bestfirst majority voting},
        doi={10.4108/eai.30-10-2023.2343092}
    }
    
  • Guntoro Guntoro
    Lisnawita Lisnawita
    Loneli Costaner
    Year: 2024
    Enhancing Cybersecurity: Innovative Hybrid Feature Selection for Intrusion Detection
    ICEEES
    EAI
    DOI: 10.4108/eai.30-10-2023.2343092
Guntoro Guntoro1,*, Lisnawita Lisnawita1, Loneli Costaner1
  • 1: Informatics of Engineering, Universitas Lancang Kuning, Pekanbaru, Indonesia
*Contact email: guntoro@unilak.ac.id

Abstract

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.