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Dynamic distributed generative adversarial network for intrusion detection system over internet of things

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Abstract

The Internet of Things (IoT) intelligently facilitates individuals interacting with the real-world applications which forms smart environment through internet connectivity at anywhere anytime (dynamic in nature), the devices in an IoT environment encounters several security threats. To overcome these security challenges numerous state of art approaches have been implemented to ensure the security of IoT appliances, but still innovative methods are desirable. The traditional Machine learning (ML) integrates with deep learning algorithm exhibits a potential of detecting abnormal intrusion patterns by formulating a seamless option for anomaly-based detection. This work proposed a Dynamic Distributed—Generative Adversarial Network (DD-GAN) with Improved Firefly Optimization- Hybrid Deep Learning based Convolutional Neural Network -Adaptive Neuro-Fuzzy Inference System (IFFO-HDLCNN + ANFIS) that takes gain of IoT's power, offers enhanced behavior for efficiently examining the entire traffic which traverses in the IoT. Initially, Synthetic Minority Over-sampling Technique (SMOTE) is engaged for pre-processing of data and then Modified Principal Component Analysis (MPCA) is being applied for feature reduction. The optimal features are selected through the Improve Firefly Optimization (IFFO) for optimum fitness value to enhance the classification accuracy of HDLCNN. Finally the intrusion detection is carried out by HDLCNN + ANFIS model, which is competent in detecting threats. The experimental results have proven that model demonstrates ability to perceive any kind of probable intrusion and anomalous behavior. In comparison to existing methods, the suggested IFFO-HDLCNN + ANFIS algorithm delivers improved intrusion detection performance regarding higher accuracy, precision, recall, f-measure, reduced False Positive Rate (FPR).

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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  1. Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India

    S. Balaji & S. Sankara Narayanan

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  1. S. Balaji
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  2. S. Sankara Narayanan
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Correspondence to S. Balaji.

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Balaji, S., Narayanan, S.S. Dynamic distributed generative adversarial network for intrusion detection system over internet of things. Wireless Netw 29, 1949–1967 (2023). https://doi.org/10.1007/s11276-022-03182-8

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