Wormhole attack detection in ad hoc network using machine learning technique

Mahendra Prasad, Sachin Tripathi, Keshav Dahal

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Abstract

In this paper, we explore the use of machine learning technique for wormhole attack detection in ad hoc network. This work has categorized into three major tasks. One of our tasks is a simulation of wormhole attack in an ad hoc network environment with multiple wormhole tunnels. A next task is the characterization of packet attributes that lead to feature selection. Consequently, we perform data generation and data collection operation that provide large volume dataset. The final task is applied to machine learning technique for wormhole attack detection. Prior to this, a wormhole attack has detected using traditional approaches. In those, a Multirate-DelPHI is shown best results as detection rate is 90%, and the false alarm rate is 20%. We conduct experiments and illustrate that our method performs better resulting in all statistical parameters such as detection rate is 93.12% and false alarm rate is 5.3%. Furthermore, we have also shown results on various statistical parameters such as Precision, F-measure, MCC, and Accuracy.
Original languageEnglish
Title of host publication2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)
PublisherIEEE
Number of pages7
ISBN (Electronic)9781538659069
ISBN (Print)9781538659052
DOIs
Publication statusPublished - 30 Dec 2019
EventInternational Conference on Computing and Networking Technology - Kanpur, India
Duration: 6 Jul 20198 Jul 2019
Conference number: 10

Conference

ConferenceInternational Conference on Computing and Networking Technology
Abbreviated titleICCNT 2019
CountryIndia
CityKanpur
Period6/07/198/07/19

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Keywords

  • Ad hoc network
  • Wormhole attack
  • Feature selection
  • Naive Bayes
  • Stochastic gradient descent

Cite this

Prasad, M., Tripathi, S., & Dahal, K. (2019). Wormhole attack detection in ad hoc network using machine learning technique. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) [45670] IEEE. https://doi.org/10.1109/ICCCNT45670.2019.8944634