A Fuzzy-Based Deep Kronecker Stacked Autoencoder for Attack Detection in Social IoT
Received: 15 April 2025 | Revised: 29 April 2025, 22 May 2025, and 3 June 2025 | Accepted: 6 June 2025 | Online: 29 July 2025
Corresponding author: Divya S.
Abstract
The Social Internet of Things (SIoT) combines the IoT and social networks. This paper introduces the Fuzzy Deep Kronecker Stacked Autoencoder (Fuzzy-DKSA), an innovative attack detection model tailored to security issues in the SIoT. The model employs Z-score normalization for consistent data scaling, followed by feature fusion using a Deep Neural Network (DNN) enhanced by the Gower similarity measure. The detection phase utilizes the strengths of the Deep Kronecker Network (DKN) and the Deep Stacked Autoencoder (DSA), incorporating fuzzy logic to dynamically adapt to various attack patterns and network conditions. The Fuzzy-DKSA model demonstrates impressive performance, achieving 92% accuracy, 91% F-score, and 91% precision, outperforming existing models such as GAN, MH-CNN-AM, TM-MLA, and HAD in attack detection capabilities, showcasing the potential of fuzzy logic in enhancing security solutions for SIoT.
Keywords:
deep learning, Social Internet of Things (SIoT), malicious attacks, Deep Kronecker Network (DKN), intrusion detectionDownloads
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