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Blackhole Attack Prediction in Wireless Sensor Networks Using Support Vector Machine

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Advances in Signal Processing, Embedded Systems and IoT

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 992))

Abstract

One of the most pressing concerns in the Internet of Things (IoT) is security. The majority of IoT deployments rely on the creation of a wireless sensor network (WSN), which builds Low Power and Lossy Networks (LLNs) between a large number of constrained devices. WSN is an IPv6 low-power personal area network (6LoWPAN) communication protocol that uses the routing protocol for Low Power and Lossy Networks (RPL) for routing. Providing security to RPL network devices is difficult due to the resource constraints of the devices attached to RPL /IPv6. This work introduces attack detection using SVM (ADSVM) protocol to detect attacks before they have a significant impact on the IoT. Different WSNs are created considering the mobility factor of nodes. To forecast the attack, a new dataset centered on IoT traits is created and analyzed. Using eightfold cross-validation for attack prediction, the dataset has an accuracy of 84.37%.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India

    Niharika Panda & M. Supriya

Authors
  1. Niharika Panda
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  2. M. Supriya
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Corresponding author

Correspondence to Niharika Panda .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Raghu Institute of Technology, Visakhapatnam, India

    V.V.S.S.S. Chakravarthy

  2. Department of Electronics Engineering, Faculty of Engineering and Technology, Veer Bahadur Singh Purvanchal University, Jaunpur, Uttar Pradesh, India

    Vikrant Bhateja

  3. Autonomous University of Baja California, Mexicali, Baja California, Mexico

    Wendy Flores Fuentes

  4. Department of Electronics and Telecommunication, Universitat Ramon Llull, Barcelona, Spain

    Jaume Anguera

  5. Shri Vishnu Engineering College for Women (A), Bhimavaram, Andhra Pradesh, India

    K. Padma Vasavi

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Panda, N., Supriya, M. (2023). Blackhole Attack Prediction in Wireless Sensor Networks Using Support Vector Machine. In: Chakravarthy, V., Bhateja, V., Flores Fuentes, W., Anguera, J., Vasavi, K.P. (eds) Advances in Signal Processing, Embedded Systems and IoT . Lecture Notes in Electrical Engineering, vol 992. Springer, Singapore. https://doi.org/10.1007/978-981-19-8865-3_30

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  • DOI: https://doi.org/10.1007/978-981-19-8865-3_30

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8864-6

  • Online ISBN: 978-981-19-8865-3

  • eBook Packages: EngineeringEngineering (R0)

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