Article: DCCGAN based intrusion detection for detecting security threats in IoT Journal: International Journal of Bio-Inspired Computation (IJBIC) 2024 Vol.23 No.2 pp.111 - 124 Abstract: Internet of things (IoT) consists of wired/wireless network, sensor, and actuator, where security is more important when more devices are connected to IoT. To increase more security in IoT devices, this manuscript proposes a dual-channel capsule generation adversarial network (DCCGAN) espoused intrusion detection scheme for detecting security threats in IoT network (DCCGAN-IDF-DST-IoT). Data are collected from MQTT-IoT-IDS2020 dataset and Bot-IoT dataset. Then, the data are fed to local least squares, which eradicate the redundancy and replace the missing value. The pre-processed dataset is supplied to fertile field optimisation algorithm (FFOA), which selects the relevant features. Then DCCGAN is used for classifying the data as normal or anomalous. The proposed technique is activated in Python language. The performance of proposed technique for MQTT-IoT-IDS2020 dataset attains 16.55%, 21.37%, 32.99%, 27.66%, 26.45%, 21.47% and 22.86% higher accuracy compared with the existing methods. Inderscience Publishers - linking academia, business and industry through research

Title: DCCGAN based intrusion detection for detecting security threats in IoT

Authors: Robin Cyriac; Sundaravadivazhagn Balasubaramanian; V. Balamurugan; R. Karthikeyan

Addresses: Department of Information Technology, Federation University, Brisbane, Australia ' Department of Information Technology, University of Technology and Applied Sciences, Al Mussanah, Oman ' Department of Computer Science and Engineering, Mohamed Sathak Engineering College, Kilakarai, Tamil Nadu, India ' Department of Computer Science and Engineering (AI&ML), Vardhaman College of Engineering, Hyderabad, Telangana State, India

Abstract: Internet of things (IoT) consists of wired/wireless network, sensor, and actuator, where security is more important when more devices are connected to IoT. To increase more security in IoT devices, this manuscript proposes a dual-channel capsule generation adversarial network (DCCGAN) espoused intrusion detection scheme for detecting security threats in IoT network (DCCGAN-IDF-DST-IoT). Data are collected from MQTT-IoT-IDS2020 dataset and Bot-IoT dataset. Then, the data are fed to local least squares, which eradicate the redundancy and replace the missing value. The pre-processed dataset is supplied to fertile field optimisation algorithm (FFOA), which selects the relevant features. Then DCCGAN is used for classifying the data as normal or anomalous. The proposed technique is activated in Python language. The performance of proposed technique for MQTT-IoT-IDS2020 dataset attains 16.55%, 21.37%, 32.99%, 27.66%, 26.45%, 21.47% and 22.86% higher accuracy compared with the existing methods.

Keywords: channel capsule generation adversarial network; fertile field algorithm; IoT network; intrusion detection; local least squares.

DOI: 10.1504/IJBIC.2024.136755

International Journal of Bio-Inspired Computation, 2024 Vol.23 No.2, pp.111 - 124

Received: 31 Aug 2022
Accepted: 24 Oct 2023
Published online: 19 Feb 2024 *

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Title: DCCGAN based intrusion detection for detecting security threats in IoT

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