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Implementing attack detection system using filter-based feature selection methods for fog-enabled IoT networks

Author

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  • Pooja Chaudhary

    (National Institute of Technology)

  • Brij Gupta

    (Asia University
    Skyline University College
    King Abdulaziz University)

  • A. K. Singh

    (National Institute of Technology)

Abstract

Internet-of-Things (IoT) has become an enthralling attacking surface for attackers to explode multitude of cyber-attacks. Distributed Denial of Service (DDoS) attack has transpired as the most menacing attack in the IoT networks. In this article, we propose an attack detection system to identify anomalous activities in the fog-enabled IoT network. Initially, authors have investigated exhaustively on the performance of filter-based feature selection algorithms comprising ReliefF, Correlation Feature Selection (CFS), Information Gain (IG), and Minimum-Redundancy-Maximum-Relevancy (mRMR) and distinct categories classification algorithms upon the prepared dataset consisting of IoT network specific features. Performance of the tested classification algorithm is assessed using prominent evaluation measures. Moreover, response time of classifiers is calculated for centralized and fog-enabled IoT network infrastructure. The experimental outcomes unveil that, in terms of both accuracy and latency, J48 classifier outperforms all other tested classifier with mRMR feature selection algorithm.

Suggested Citation

  • Pooja Chaudhary & Brij Gupta & A. K. Singh, 2022. "Implementing attack detection system using filter-based feature selection methods for fog-enabled IoT networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 81(1), pages 23-39, September.
    • Handle: RePEc:spr:telsys:v:81:y:2022:i:1:d:10.1007_s11235-022-00927-w
    DOI: 10.1007/s11235-022-00927-w
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    References listed on IDEAS

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    1. Naveen John & Shatheesh Sam, 2021. "Provably Secure Data Sharing Approach for Personal Health Records in Cloud Storage Using Session Password, Data Access Key, and Circular Interpolation," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 17(4), pages 76-98, October.
    2. Waqas Haider & Gideon Creech & Yi Xie & Jiankun Hu, 2016. "Windows Based Data Sets for Evaluation of Robustness of Host Based Intrusion Detection Systems (IDS) to Zero-Day and Stealth Attacks," Future Internet, MDPI, vol. 8(3), pages 1-8, July.
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