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IoT authentication model with optimized deep Q network for attack detection and mitigation

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Abstract

The Internet of Things (IoT) is a promising area in day-to-day lives, like homes, health, military, and agriculture. The expansion of IoT technology has gained the attention of hackers to acquire the benefits of their communication abilities for performing various kinds of attacks. The main issue is devices of IoT pose various susceptibilities which simply exploit the IoT botnets. This paper devises an optimization-aware deep model for detecting and mitigating attacks in IoT. Here, the first step is a simulation of IoT nodes. In addition, the authentication with privacy preserved data encryption is performed with autoregressive poor and rich optimization (APRO) algorithm. Thereafter, the feature selection is performed with Jaro–Winkler distance for obtaining imperative features. After determining the optimum features, the attack detection is performed with autoregressive poor and rich spider monkey optimization-based deep Q network (APRSMO-based deep Q network). Here, the deep Q network training is done with developed APRSMO, which is devised by combining conditional autoregressive value at risk by regression quantiles (CAViaR), and poor and rich optimization (PRO) algorithm, and spider monkey optimization (SMO). The mitigation of attack is performed using data rates if the output generated by the proposed APRSMO-based deep Q network is an intruder. The proposed APRSMO-based deep Q network provided enhanced efficiency with elevated precision of 90.1%, recall of 89.1%, and F1-score of 89.6%.

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  1. Computer Science and Engineering, GITAM Institute of Technology, GIT, GITAM University, Vishakhapatnam, Andhra Pradesh, 530045, India

    Supriya Palekar & Y. Radhika

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  1. Supriya Palekar
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  2. Y. Radhika
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Correspondence to Supriya Palekar.

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Palekar, S., Radhika, Y. IoT authentication model with optimized deep Q network for attack detection and mitigation. Int J Intell Robot Appl 6, 350–364 (2022). https://doi.org/10.1007/s41315-022-00227-1

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