Open Access

ARTICLE

A Novel Hybrid Deep Learning Framework for Intrusion Detection Systems in WSN-IoT Networks

M. Maheswari^1,2,*, R. A. Karthika¹

1 Vels Institute of Science, Technology and Advanced Studies, Chennai, 600117, India
2 SRM Institute of Science and Technology, Chennai, 603203, India

* Corresponding Author: M. Maheswari. Email:

(This article belongs to the Special Issue: AI powered Blockchain-Enabled privacy protected 5G Networks and Beyond)

Intelligent Automation & Soft Computing 2022, 33(1), 365-382. https://doi.org/10.32604/iasc.2022.022259

Received 02 August 2021; Accepted 07 November 2021; Issue published 05 January 2022

Abstract

With the advent of wireless communication and digital technology, low power, Internet-enabled, and reconfigurable wireless devices have been developed, which revolutionized day-to-day human life and the economy across the globe. These devices are realized by leveraging the features of sensing, processing the data and nodes communications. The scale of Internet-enabled wireless devices has increased daily, and these devices are exposed to various cyber-attacks. Since the complexity and dynamics of the attacks on the devices are computationally high, intelligent, scalable and high-speed intrusion detection systems (IDS) are required. Moreover, the wireless devices are battery-driven; implementing them would consume more energy, weakening the accuracy of detecting the attacks. Hence the design of the IDS is required, which has to establish the good trade-offs between Energy and accuracy. This research includes the Multi-tiered Intrusion Detection (MDIT) with hybrid deep learning models for improved detection accuracy in wireless networks; spotted hyena optimization (SHO) and Long short-term memory (LSTM) have been studied to design IDS effectively. Extensive experimentation has been carried out in real-time scenarios using the Node MCU Embedded boards and standard benchmarks such as CIDDS-001, UNSWNB15 and KDD++ datasets compared with the other traditional and existing learning models. The average prediction accuracy of 99.89% for all datasets has been achieved. The results show that the proposed system guarantees a high detection accuracy and reduces the prediction time, making this system suitable for resource-constrained IP-enabled wireless devices.

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Keywords

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