Internet of Things: A Survey on Distributed Attack Detection Using Deep Learning Approach

The Internet of Things (IoT) is made up of large number of individuals, things, and utilities having the potential to intermingle using every one and their surroundings. Due to the upsurge in the amount, variety of existence of brilliant things and sophistication of attacks, IoT poses novel threats from a security, trust, and privacy perception. Cloud computing has radically reformed the world from business outlook. Integrated services in cloud have an enormous impact on security mechanisms of variety of use-cases in IoT discriminated with distributed services. This paper proposes an enhanced distributed attack detection model using NSL-KDD dataset that performs under two phases: (i) feature extraction and (ii) classification. The features namely, duration, rules, service, flag, source bytes, destination bytes, etc., will be extracted and classified using deep learning. The normal and attacks (DoS, probe, R2L, and U2R) could be detected effectively in IoT system by the proposed model.

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