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Henry MaxNet: tversky index based feature selection and competitive swarm henry gas solubility optimization integrated Deep Maxout network for intrusion detection in IoT

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Abstract

Internet of Things (IoT) has got more attention in the research field of computer science. The extreme increase in the IoT appliances across various factors, such as smart industries and health care appears with extensive security hazards. It is not only restricted to the attacks on confidentiality but also broadens to the attacks on performance and availability of the network. Hence, an intrusion detection mechanism is mandatory for identifying the attacks on IoT to offer effectual protection and security. Even though various intrusion detection methods are developed, achieving higher classification performance still results a challenging task. Therefore, an effective intrusion detection method is developed using the proposed Competitive Swarm Henry Optimization (CSHO)-based Deep Maxout network to find intruders in the IoT environment. The process of detection strategy is carried out with the information captured by nodes distributed in network. Routing plays an essential role in transferring data from IoT devices to the base station (BS) to accomplish the task of intrusion discovery. The proposed solution offers security as a service and provides evidence in terms of scalability. However, the proposed method offers better results using the metrics, like energy, F-measure, precision, and recall as 0.1610, 0.9001, 0.9052, and 0.8993, respectively.

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Authors and Affiliations

  1. School of Information Technology, Vellore Institute of Technology, Katpadi, Vellore, 632014, India

    Mythili Boopathi

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  1. Mythili Boopathi
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Boopathi, M. Henry MaxNet: tversky index based feature selection and competitive swarm henry gas solubility optimization integrated Deep Maxout network for intrusion detection in IoT. Int J Intell Robot Appl 6, 365–383 (2022). https://doi.org/10.1007/s41315-022-00234-2

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  • DOI: https://doi.org/10.1007/s41315-022-00234-2

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