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Neuro-Fuzzy Based Intrusion Detection System for Wireless Sensor Network

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Abstract

Malicious attacks like denial-of-service massively affect the network activities of wireless sensor network. These attacks exploit network layer vulnerabilities and affect all the layers of the network. Anomaly based intrusion detection system (AIDS) are designed for monitoring such unpredictable attacks but it generates high false positive. In the proposed study we design robust and efficient AIDS which use fuzzy and neural network (NN) based tools. The proposed system can be implemented in each node as it is lightweight and does not consume much overhead. Also it can independently monitor the local nodes behaviour and identify whether a node is trust, distrust or enemy. The use of a trained NN filters the false alarms generated due to fuzzy logic applied in the first step thus enhancing the system accuracy. We evaluate the system’s performance in NS2.35 and result shows a 100% true positive with 0% false positive.

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Authors and Affiliations

  1. Department of Computer Science, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham, Mysuru, India

    Somnath Sinha

  2. Department of Computer Science, Banasthali Vidyapith, Tonk, India

    Aditi Paul

Authors
  1. Somnath Sinha
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  2. Aditi Paul
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Correspondence to Somnath Sinha.

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Sinha, S., Paul, A. Neuro-Fuzzy Based Intrusion Detection System for Wireless Sensor Network. Wireless Pers Commun 114, 835–851 (2020). https://doi.org/10.1007/s11277-020-07395-y

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  • DOI: https://doi.org/10.1007/s11277-020-07395-y

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