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

当前位置: 邓晓衡 >> 论文成果

Xiaoheng Deng, Xinjun Pei, Shengwei Tian, Lan Zhang, "Edge-Based IIoT Malware Detection for Mobile Devices With Offloading," in IEEE Transactions on Industrial Informatics, doi: 10.1109/TII.2022.3216818. IEEE Transactions on Industrial Informatics. (中科院 1区, 2023)

发布时间:2024-03-13

点击次数:

摘要:Abstract:The advent of 5G brought new opportunities to leapfrog beyond current Industrial Internet of Things (IoT). However, the ever-growing IoT has also attracted adversaries to develop new malware attacks against various IoT applications. Although deep-learning-based methods are expected to combat the sophisticated malwares by exploring the latent attack patterns, such detection can be hardly supported by battery-powered end devices, such as Android-based smartphones. Edge computing enables the near-real-time analysis of IoT data by migrating artificial intelligence (AI)-enabled computation-intensive tasks from resource-constrained IoT devices to nearby edge servers. However, owing to varying channel conditions and the demanding latency requirements of malware detection, it is challenging to coordinate the computing task offloading among multiple users. By leveraging the computation capacity and the proximity benefits of edge computing, we propose a hierarchical security framework for IoT malware detection. Considering the complexity of the AI-enabled malware detection task, we provide a delay-aware computational offloading strategy with minimum delay. Specifically, we construct a coordinated representation learning model, named by Two-Stream Attention-Caps, to capture the latent behavioral patterns of evolving malware attacks. Experimental results show that our system consistently outperforms the state-of-the-art systems in detection performance on four benchmark datasets.

备注:http://faculty.csu.edu.cn/dengxiaoheng/zh_CN/lwcg/10445/content/49297.htm

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附件:

  • 12-Edge-Based IIoT Malware Detection for Mobile Devices With Offloading.pdf

  • 上一条: Xinjun Pei, Xiaoheng Deng, Shengwei Tian, Lan Zhang, Kaiping Xue, "A Knowledge Transfer-Based Semi-Supervised Federated Learning for IoT Malware Detection," in IEEE Transactions on Dependable and Secure Computing, doi: 10.1109/TDSC.2022.3173664. IEEE Transactions on Dependable and Secure Computing. (CCF-A期刊, 2023)

    下一条: Bo Li, Xiaoheng Deng, Xuechen Chen, Yiqin Deng, Jian Yin, "MEC-Based Dynamic Controller Placement in SD-IoV: A Deep Reinforcement Learning Approach," in IEEE Transactions on Vehicular Technology, doi: 10.1109/TVT.2022.3182048. IEEE Transactions on Vehicular Technology. (JCR 1区, 2022)