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Gated Capsule Networks for Intrusion Detection Systems to Improve the Security of WSN-LOT
U.V. Arivazhagu, Ilanchezhian P, Maytham N. Meqdad and V. Prithivirajan

Nowadays the technology is developing in the area of wireless communication which can increase the internet based reconfigurable wireless gadgets. This creates huge revolution in people lives and economy across the economy. These innovative devices can perform the sensing operations, data processing as well as communications among the nodes. Since the internet enabled gadgets are increasing everyday which also faces different cyber-attacks in the network. In order handle and defend those cyber-attacks and to improve the security, the intelligent framework is ultimate. Commonly the wireless gadgets are battery driven elements, at the same time IDS implementation consumes more energy which debilitates the attack detection accuracy. Consequently plan of the IDS is required which needs to lay out the great compromises between the energy and accuracy. The novel Gated Capsule Networks (GCN) is proposed in this paper to improve the detection accuracy of abnormal behaviour in wireless networks. Spotted Hyena Optimizer (SHO) and Gated Recurrent Unit (GRU) have been considered for an effective IDS design that can achieve a good trade-off between energy and accuracy. The experimentation has been carried out on the real time datasets under different attacks to measure the performance of the proposed method. Finally comparative analysis is done with existing learning models. For different attack predictions in WSN-IoT, the proposed framework achieved exemplary performance in terms of accuracy (99.99%), precision (99.98%), recall (99.99%), sensitivity (99.98%), and F1-score (99.98%). As a result, this framework provides extensive support for resource-constrained IP-enabled wireless devices.

Keywords: Reconfigurable Wireless gadgets, Spotted Hyena Optimizer, Gated Recurrent Unit (GRU), real time dataset, Capsule Network

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