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A Trust Model for Detecting Device Attacks in Mobile Ad Hoc Ambient Home Network

Author

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  • Akinboro Solomon

    (Department of Computer Science and Technology, Bells University of Technology, Ota, Nigeria)

  • Emmanuel Olajubu

    (Department of Computer Science and Engineering, Obafemi Awolowo University, Ile Ife, Nigeria)

  • Ibrahim Ogundoyin

    (Department of Information and Communications Technology, Osun State University, Osogbo, Nigeria)

  • Ganiyu Aderounmu

    (Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria)

Abstract

This study designed, simulated and evaluated the performance of a conceptual framework for ambient ad hoc home network. This was with a view to detecting malicious nodes and securing the home devices against attacks. The proposed framework, called mobile ambient social trust consists of mobile devices and mobile ad hoc network as communication channel. The trust model for the device attacks is Adaptive Neuro Fuzzy (ANF) that considered global reputation of the direct and indirect communication of home devices and remote devices. The model was simulated using Matlab 7.0. In the simulation, NSL-KDD dataset was used as input packets, the artificial neural network for packet classification and ANF system for the global trust computation. The proposed model was benchmarked with an existing Eigen Trust (ET) model using detection accuracy and convergence time as performance metrics. The simulation results using the above parameters revealed a better performance of the ANF over ET model. The framework will secure the home network against unforeseen network disruption and node misbehavior.

Suggested Citation

  • Akinboro Solomon & Emmanuel Olajubu & Ibrahim Ogundoyin & Ganiyu Aderounmu, 2016. "A Trust Model for Detecting Device Attacks in Mobile Ad Hoc Ambient Home Network," International Journal of Advanced Pervasive and Ubiquitous Computing (IJAPUC), IGI Global, vol. 8(2), pages 16-37, April.
    • Handle: RePEc:igg:japuc0:v:8:y:2016:i:2:p:16-37
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