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Optimized Deep Neuro Fuzzy Network for Cyber Forensic Investigation in Big Data-Based IoT Infrastructures

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  • Suman Thapaliya

    (Department of IT, Lincoln University College, Malaysia)

  • Pawan Kumar Sharma

    (Department of Faculty of Science Health and Technology, Nepal Open University, Nepal)

Abstract

Forensic skills analysts play an imperative support to practice streaming data generated from the IoT networks. However, these sources pose size limitations that create traffic and increase big data assessment. The obtainable solutions have utilized cybercrime detection techniques based on regular pattern deviation. Here, a generalized model is devised considering the MapReduce as a backbone for detecting the cybercrime. The objective of this model is to present an automatic model, which using the misbehavior in IoT device can be manifested, and as a result the attacks exploiting the susceptibility can be exposed by newly devised automatic model. The simulation of IoT is done such that energy constraints are considered as basic part. The routing is done with fractional gravitational search algorithm to transmit the information amongst the nodes. Apart from this, the MapReduce is adapted for cybercrime detection and is done at base station (BS) considering deep neuro fuzzy network (DNFN) for identifying the malwares.

Suggested Citation

  • Suman Thapaliya & Pawan Kumar Sharma, 2023. "Optimized Deep Neuro Fuzzy Network for Cyber Forensic Investigation in Big Data-Based IoT Infrastructures," International Journal of Information Security and Privacy (IJISP), IGI Global, vol. 17(1), pages 1-22, January.
    • Handle: RePEc:igg:jisp00:v:17:y:2023:i:1:p:1-22
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