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

作     者：Almehdhar, Mohammed Abdelsamea, Mohammed M. Ruan, Na 

作者机构：Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Birmingham City Univ Sch Comp & Digital Technol Birmingham England 

出 版 物：《INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTER SECURITY》 (国际信息与计算机安全杂志)

年 卷 期：2023年第22卷第3-4期

页      面：453-474页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：smart home internet of things IoT deep convolutional neural networks distributed denial of service DDoS 

摘      要：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.