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An Enhanced WOA and MI-Based Feature Selection Method for Attack Detection in Smart Meter Communication

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Proceedings of 4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications (ICMISC 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 873))

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Abstract

Smart meter is a customer locality component of modern electrical grid. It is an easy target for the cyber-attacks. Learning algorithm-based intrusion detection system has good performance in various applications. It is a notable mechanism that provides security by early detection of intrusions from the meter traffic communication. The noisy data collected from the sources diminish the functioning of the attack finding algorithms. Thus, the feature selection algorithms are used to enhance the detection ratio of machine learning classifier. In this work, a collaborative method using wrapper-based whale optimization algorithm and filter-based mutual information is used to recognize the informational features. The identified features are feed as input to support vector machine classifier. The standard dataset ADFA-LD is employed to inspect the effectiveness of recommended method. The outputs certified the suitability of selected hybrid method for providing security to smart meter communication network.

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Author information

Authors and Affiliations

  1. Nagarjuna College of Engineering and Technology, Bengaluru, India

    R. Vijayanand

  2. Muthayammal Engineering College, Rasipuram, India

    N. Naveen Kumar

  3. PSR Engineering College, Sivakasi, India

    M. Ulaganathan

  4. Kalasalingam Academy of Research and Education, Srivilliputhur, India

    D. Devaraj

  5. Dr. Mahalingam College of Engineering and Technology, Pollachi, India

    B. Kannapiran

Authors
  1. R. Vijayanand
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  2. N. Naveen Kumar
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  3. M. Ulaganathan
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  4. D. Devaraj
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  5. B. Kannapiran
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Corresponding author

Correspondence to R. Vijayanand .

Editor information

Editors and Affiliations

  1. Computer Science And Engineering, CMR Institute of Technology, Hyderabad, Telangana, India

    Vinit Kumar Gunjan

  2. ELECTRICAL AND COMPUTER ENGINEERING, UNIVERSITY OF LOUISVILLE, Louisville, KY, USA

    Jacek M. Zurada

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Vijayanand, R., Naveen Kumar, N., Ulaganathan, M., Devaraj, D., Kannapiran, B. (2024). An Enhanced WOA and MI-Based Feature Selection Method for Attack Detection in Smart Meter Communication. In: Gunjan, V.K., Zurada, J.M. (eds) Proceedings of 4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. ICMISC 2023. Lecture Notes in Networks and Systems, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-99-9442-7_60

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  • DOI: https://doi.org/10.1007/978-981-99-9442-7_60

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9441-0

  • Online ISBN: 978-981-99-9442-7

  • eBook Packages: EngineeringEngineering (R0)

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