Research on artificial intelligence enhancing Internet of Things security: A survey
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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, 2020.Research output: Contribution to journal › Journal article › peer-review
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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
Y1 - 2020
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