信息网络安全 ›› 2021, Vol. 21 ›› Issue (9): 67-73.doi: 10.3969/j.issn.1671-1122.2021.09.010

• 入选论文 • 上一篇    下一篇

基于深度可分离卷积的物联网设备识别模型

陈庆港1, 杜彦辉1(), 韩奕1,2, 刘翔宇1   

  1. 1.中国人民公安大学信息网络安全学院,北京 100038
    2.公安部第一研究所,北京 100048
  • 收稿日期:2021-06-02 出版日期:2021-09-10 发布日期:2021-09-22
  • 通讯作者: 杜彦辉 E-mail:dyh6889@126.com
  • 作者简介:陈庆港(1997—),男,河南,硕士研究生,主要研究方向为物联网安全|杜彦辉(1969—),男,山西,教授,博士,主要研究方向为网络安全|韩奕(1988—),女,辽宁,博士研究生,主要研究方向为社交网络、物联网安全|刘翔宇(1995—),男,安徽,博士研究生,主要研究方向为信息安全
  • 基金资助:
    中国人民公安大学 2021 年基本科研业务费重大项目(2021JKF105)

IoT Device Recognition Model Based on Depthwise Separable Convolution

CHEN Qinggang1, DU Yanhui1(), HAN Yi1,2, LIU Xiangyu1   

  1. 1. College of Information Network Security, People’s Public Security University of China, Beijing 100038, China;
    2. The First Research Institute of the Ministry of Public Security, Beijing 100048, China
  • Received:2021-06-02 Online:2021-09-10 Published:2021-09-22
  • Contact: DU Yanhui E-mail:dyh6889@126.com

摘要:

随着物联网设备数量的不断增长,物联网设备管理问题逐渐突出,如何在资源有限的物联网环境中准确地识别物联网设备是亟需解决的关键问题。针对物联网设备流量特征提取难的问题,文章提出了一种基于深度可分离卷积的物联网设备识别方法。该方法在会话粒度下利用载荷数据构造设备指纹,通过卷积层从设备指纹中提取深度特征。实验结果表明,该方法能在有限资源下有效识别设备类型。与标准CNN方法和人工特征提取技术相比,整体性能有所提高。

关键词: 物联网设备, 流量特征, 可分离卷积, 设备指纹

Abstract:

With the continuous growth of the number of IoT devices, the problem of IoT device management has become increasingly prominent. How to accurately identify IoT devices in the resource-limited IoT environment is a key problem to be solved urgently. To solve the difficulty in extracting the traffic features of devices in the Internet of Things (IoT), an Internet of Things device identification method based on deep separable convolution was proposed. In this method, device fingerprints were constructed using payload data at session granularity, and depth features were extracted from device fingerprints through convolutional layer. Experimental results show that this method can effectively identify device types with limited resources. Compared with the standard CNN method and manual feature extraction technique, the overall performance is improved.

Key words: Internet of Things device, flow characteristics, separable convolution, device fingerprinting

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