Title: Deep multi-locality convolutional neural network for DDoS detection in smart home IoT

Authors: Mohammed Almehdhar; Mohammed M. Abdelsamea; Na Ruan

Addresses: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China ' School of Computing and Digital Technology, Birmingham City University, Birmingham, UK ' Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

Abstract: Internet of things (IoT) devices usually offer limited resources such as processing, memory, and network capacity, bringing more security threats to the environment. Distributed denial of service (DDoS) signal attacks are among the most serious threats. Software-defined networking (SDN) is a promising paradigm that could offer a scalable security solution optimised for the IoT ecosystem. However, investigating a robust security solution is still one of the most challenging problems that a smart home environment faces in SDN. In this paper, we introduce a multi-locality deep learning model for the detection of DDoS signals in an SDN-based smart home. It employs convolutional neural networks (CNNs) by learning different levels of local information from the data. In this work, an ensemble of two CNNs to detect malicious traffic flows with low computation overhead framework is proposed. Experimental results demonstrate the robustness, effectiveness, and efficiency of our solution in detecting DDoS attacks in SDN smart home.

Keywords: smart home; internet of things; IoT; deep convolutional neural networks; distributed denial of service; DDoS.

DOI: 10.1504/IJICS.2023.135902

International Journal of Information and Computer Security, 2023 Vol.22 No.3/4, pp.453 - 474

Received: 17 Mar 2022
Accepted: 11 Sep 2022
Published online: 09 Jan 2024 *

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