邓晓衡

教授 博士生导师 硕士生导师

入职时间:2006-01-05

所在单位:电子信息学院

职务:院长

学历:博士研究生毕业

性别:男

联系方式:Email:dxh@csu.edu.cn

学位:博士学位

在职信息:在职

主要任职:湖南省数据传感与交换设备工程中心 主任 IEEE RS Chapter长沙 主席CCF普适计算专委 委员 CCF长沙 执委

毕业院校:中南大学

学科:信息与通信工程
计算机科学与技术

曾获荣誉:

2024-12-31  当选:  湖南省“芙蓉学者”特聘教授

2020-12-31  当选:  中南大学励志教师奖励

2010-12-31  当选:  湖南省青年骨干教师

2008-12-31  当选:  2008年获西南铝业优秀教师奖

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Xiaoheng Deng; Haowen Tang; Xinjun Pei; Deng Li; Kaiping Xue, "MDHE: A Malware Detection System Based on Trust Hybrid User-Edge Evaluation in IoT Network," in IEEE Transactions on Information Forensics and Security, doi: 10.1109/TIFS.2023.3318947. IEEE Transactions on Information Forensics and Security. (CCF-A期刊, 2024)

发布时间:2024-03-13

点击次数:

摘要:Abstract— With the coming of the Internet of Things (IoT) era, malware attacks targeting IoT networks have posed serious threats to users. Recently, the emerging of edge computing have paved the way for new data processing paradigms in IoT networks, but it is still a challenge for deploying malware detection systems on the IoT devices. This paper develops an IoT malware detection system based on trust hybrid user-edge evaluation, namely MDHE. This system decomposes a large and complex deep learning model into two parts, which are deployed on edge servers and end devices, respectively. Specifically, a trust evaluation mechanism is used to select the trusted devices to participate the model training. Moreover, we develop a private feature generation that leverages a graph mining technology to extract the subgraph features, which then are perturbed by leveraging the differential privacy technology to prevent user privacy from leaking. Finally, we reconstruct the perturbed features on edge server, and propose a Capsule Network (CapsNet) to identify malware. Experimental results show that MDHE can effectively detect malware. Specifically, it can reduce sensitive inference while maintaining the utility of data.

备注:http://faculty.csu.edu.cn/dengxiaoheng/zh_CN/lwcg/10445/content/49306.htm

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附件:

  • 3-MDHE_A_Malware_Detection_System_Based_on_Trust_Hybrid_User-Edge_Evaluation_in_IoT_Network.pdf

  • 上一条: Xinjun Pei; Xiaoheng Deng; Shengwei Tian; Jianqing Liu; Kaiping Xue, "Privacy-Enhanced Graph Neural Network for Decentralized Local Graphs," in IEEE Transactions on Information Forensics and Security, doi: 10.1109/TIFS.2023.3329971. IEEE Transactions on Information Forensics and Security. (CCF-A期刊, 2024)

    下一条: Tingxuan Liang, Lingyi Chen, Mingfeng Huang, Xiaoheng Deng, Shaobo Zhang, Neal N. Xiong, Anfeng Liu, "RLTD: A Reinforcement Learning-based Truth Data Discovery scheme for decision support systems under sustainable environments," in Applied Soft Computing, doi: 10.1016/j.asoc.2023.110369. Applied Soft Computing. (中科院 1区, 2023)