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Edge Computing and IoT: Systems, Management and Security. Third EAI International Conference, ICECI 2022, Virtual Event, December 13-14, 2022, Proceedings

Research Article

Adversarial Example Attacks in Internet of Things (IoT)

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-28990-3_16,
        author={Yuzhe Gu and Na Jiang and Yanjiao Chen and Xueluan Gong},
        title={Adversarial Example Attacks in Internet of Things (IoT)},
        proceedings={Edge Computing and IoT: Systems, Management and Security. Third EAI International Conference, ICECI 2022, Virtual Event, December 13-14, 2022, Proceedings},
        proceedings_a={ICECI},
        year={2023},
        month={3},
        keywords={Adversarial Example Attacks IoT Deep Learning},
        doi={10.1007/978-3-031-28990-3_16}
    }
    
  • Yuzhe Gu
    Na Jiang
    Yanjiao Chen
    Xueluan Gong
    Year: 2023
    Adversarial Example Attacks in Internet of Things (IoT)
    ICECI
    Springer
    DOI: 10.1007/978-3-031-28990-3_16
Yuzhe Gu1, Na Jiang1, Yanjiao Chen2, Xueluan Gong3,*
  • 1: School of Cyber Science and Engineering
  • 2: College of Electrical Engineering
  • 3: School of Computer Science
*Contact email: xueluangong@whu.edu.cn

Abstract

Recently, the Internet of Things (IoT) technology has made tremendous progress, and it is beginning to enter many areas of social life, such as autonomous driving, medical care, etc. Due to the massive data in IoT, deep neural networks (DNN) are often involved in helping process and analyzing data, but DNNs still face many security threats. Adversarial example attack is a common attack against DNN models, which interferes with model decisions through processed samples. It will undoubtedly threaten DNN-based IoT systems. This paper presents the possible attack scenarios of adversarial example attacks in IoT systems and extensively studies the defense methods of adversarial example attacks in IoT systems.

Keywords
Adversarial Example Attacks IoT Deep Learning
Published
2023-03-31
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-28990-3_16
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