Random neural network based intelligent intrusion detection for wireless sensor networks

Ahmed Saeed, Ali Ahmadinia, Abbas Javed, Hadi Larijani

    Research output: Contribution to journalArticlepeer-review

    14 Citations (Scopus)
    101 Downloads (Pure)


    Security and privacy of data are one of the prime concerns in today's embedded devices. Primitive security techniques like signature-based detection of malware and regular update of signature database are not feasible solutions as they cannot secure such systems, having limited resources, effectively. Furthermore, energy efficient wireless sensor modes running on batteries cannot afford the implementation of cryptography algorithms as such techniques have significant impact on the system power consumption. Therefore, in order to operate wireless embedded devices in a secure manner, the system must be able to detect and prevent any kind of intrusions before the network (i.e. sensor nodes and base station) is destabilized by the attackers. In this paper, we have presented an intrusion detection mechanism by implementing an intelligent security architecture using Random Neural Networks (RNN). To validate the feasibility of the proposed security solution, it is implemented for an existing wireless sensor network system and its functionality is practically demonstrated by successfully detecting the presence of any suspicious sensor node and anomalous activity in the base station with high accuracy and minimal performance overhead.
    Original languageEnglish
    Pages (from-to)2372-2376
    Number of pages5
    JournalProcedia Computer Science
    Early online date1 Jun 2016
    Publication statusPublished - 2016


    • random neural network
    • wireless sensor networks


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