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Mitigation of black hole attacks using firefly and artificial neural network

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Abstract

In Mobile Ad hoc Network (MANET), network topology changes as devices/users/nodes move and nodes can serve as a source, destination, or router for the information. One of the challenging tasks in MANET is secure routing because of the change of network topology, open nature of wireless communications and frequent breakage in communication links. Thus, it is critically important to develop a robust and secure routing protocol for sending data from the source to the destination in MANET. In this paper, we investigate black hole attacks where compromised nodes utilize routing protocols for self-exposing which has the shortest route for data forwarding to the destination node. Specifically, the proposed approach enhances the Ad hoc On-Demand Distance Vector routing protocol for combating black hole attacks by leveraging the Firefly Algorithm with Artificial Neural Network. Performance evaluation is carried out using numerical results obtained from several experiments and considering different metrics such as computation overhead, packet delivery rate, throughput and delay. Numerical results show that the proposed approach outperforms the traditional approaches.

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Authors and Affiliations

  1. Department of Computer Science Engineering, Rayat Bahra University, Mohali, India

    Pooja Rani

  2. Department of Computer Science Engineering, Chandigarh University, Mohali, India

    Kavita & Sahil Verma

  3. Danda B. Rawat is with the Department of Electrical Engineering & Computer Science (EECS), Howard University, Washington, DC, 20059, USA

    Danda B. Rawat

  4. Department of Electronics and Communication Engineering, Raghu Institute of Technology (A), Visakhapatnam, Andhra Pradesh, 531162, India

    Sonali Dash

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  1. Pooja Rani
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  2. Kavita
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  3. Sahil Verma
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  4. Danda B. Rawat
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  5. Sonali Dash
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Correspondence to Danda B. Rawat.

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Rani, P., Kavita, Verma, S. et al. Mitigation of black hole attacks using firefly and artificial neural network. Neural Comput & Applic 34, 15101–15111 (2022). https://doi.org/10.1007/s00521-022-06946-7

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  • DOI: https://doi.org/10.1007/s00521-022-06946-7

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