Research on artificial intelligence enhancing Internet of Things security: A survey

Research output: Contribution to journalJournal articleResearchpeer-review

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Research on artificial intelligence enhancing Internet of Things security : A survey. / Wu, Hui; Han, Haiting; Wang, Xiao; Sun, Shengli.

In: IEEE Access, Vol. 8, 153848, 08.2020.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Wu, H, Han, H, Wang, X & Sun, S 2020, 'Research on artificial intelligence enhancing Internet of Things security: A survey', IEEE Access, vol. 8, 153848. https://doi.org/10.1109/ACCESS.2020.3018170

APA

Wu, H., Han, H., Wang, X., & Sun, S. (2020). Research on artificial intelligence enhancing Internet of Things security: A survey. IEEE Access, 8, [153848]. https://doi.org/10.1109/ACCESS.2020.3018170

Vancouver

Wu H, Han H, Wang X, Sun S. Research on artificial intelligence enhancing Internet of Things security: A survey. IEEE Access. 2020 Aug;8. 153848. https://doi.org/10.1109/ACCESS.2020.3018170

Author

Wu, Hui ; Han, Haiting ; Wang, Xiao ; Sun, Shengli. / Research on artificial intelligence enhancing Internet of Things security : A survey. In: IEEE Access. 2020 ; Vol. 8.

Bibtex

@article{85e08fdf42c9403e9cf338d977dc7f83,
title = "Research on artificial intelligence enhancing Internet of Things security: A survey",
abstract = "Through three development routes of authentication, communication, and computing, the Internet of Things (IoT) has become a variety of innovative integrated solutions for specific applications. However, due to the openness, extensiveness and resource constraints of IoT, each layer of the three-tier IoT architecture suffers from a variety of security threats. In this work, we systematically review the particularity and complexity of IoT security protection, and then find that Artificial Intelligence (AI) methods such as Machine Learning (ML) and Deep Learning (DL) can provide new powerful capabilities to meet the security requirements of IoT. We analyze the technical feasibility of AI in solving IoT security problems and summarize a general process of AI solutions for IoT security. For four serious IoT security threats: device authentication, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks defense, intrusion detection and malware detection, we summarize representative AI solutions and compare the different algorithms and technologies used by various solutions. It should be noted that although AI provides many new capabilities for the security protection of IoT, it also brings new potential challenges and possible negative effects to IoT in terms of data, algorithm and architecture. In the future, how to solve these challenges can serve as potential research directions.",
author = "Hui Wu and Haiting Han and Xiao Wang and Shengli Sun",
year = "2020",
month = "8",
doi = "10.1109/ACCESS.2020.3018170",
language = "English",
volume = "8",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

TY - JOUR

T1 - Research on artificial intelligence enhancing Internet of Things security

T2 - A survey

AU - Wu, Hui

AU - Han, Haiting

AU - Wang, Xiao

AU - Sun, Shengli

PY - 2020/8

Y1 - 2020/8

N2 - Through three development routes of authentication, communication, and computing, the Internet of Things (IoT) has become a variety of innovative integrated solutions for specific applications. However, due to the openness, extensiveness and resource constraints of IoT, each layer of the three-tier IoT architecture suffers from a variety of security threats. In this work, we systematically review the particularity and complexity of IoT security protection, and then find that Artificial Intelligence (AI) methods such as Machine Learning (ML) and Deep Learning (DL) can provide new powerful capabilities to meet the security requirements of IoT. We analyze the technical feasibility of AI in solving IoT security problems and summarize a general process of AI solutions for IoT security. For four serious IoT security threats: device authentication, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks defense, intrusion detection and malware detection, we summarize representative AI solutions and compare the different algorithms and technologies used by various solutions. It should be noted that although AI provides many new capabilities for the security protection of IoT, it also brings new potential challenges and possible negative effects to IoT in terms of data, algorithm and architecture. In the future, how to solve these challenges can serve as potential research directions.

AB - Through three development routes of authentication, communication, and computing, the Internet of Things (IoT) has become a variety of innovative integrated solutions for specific applications. However, due to the openness, extensiveness and resource constraints of IoT, each layer of the three-tier IoT architecture suffers from a variety of security threats. In this work, we systematically review the particularity and complexity of IoT security protection, and then find that Artificial Intelligence (AI) methods such as Machine Learning (ML) and Deep Learning (DL) can provide new powerful capabilities to meet the security requirements of IoT. We analyze the technical feasibility of AI in solving IoT security problems and summarize a general process of AI solutions for IoT security. For four serious IoT security threats: device authentication, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks defense, intrusion detection and malware detection, we summarize representative AI solutions and compare the different algorithms and technologies used by various solutions. It should be noted that although AI provides many new capabilities for the security protection of IoT, it also brings new potential challenges and possible negative effects to IoT in terms of data, algorithm and architecture. In the future, how to solve these challenges can serve as potential research directions.

U2 - 10.1109/ACCESS.2020.3018170

DO - 10.1109/ACCESS.2020.3018170

M3 - Journal article

VL - 8

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

M1 - 153848

ER -

ID: 247956254