Knowledge graph and behavior portrait of intelligent attack against path planning

International Journal of Intelligent SystemsEarly View RESEARCH ARTICLE Knowledge graph and behavior portrait of intelligent attack against path planning Li Zhang, Li Zhang Department of Media Engineering, Communication University of Zhejiang, Hangzhou, ChinaSearch for more papers by this authorZhao Li, Zhao Li School of Computer Science, Beijing Institute of Technology, Beijing, ChinaSearch for more papers by this authorHuali Ren, Corresponding Author Huali Ren rhl717445@163.com Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China Correspondence Huali Ren, Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China. Email: rhl717445@163.com Quanxin Zhang, School of Computer Science, Beijing Institute of Technology, Beijing 100081, China. Email: zhangqx@bit.edu.cnSearch for more papers by this authorXiao Yu, Xiao Yu Department of Computer Science and Technology, Shandong University of Technology, Zibo, ChinaSearch for more papers by this authorYuxi Ma, Yuxi Ma School of Computer Science, Beijing Institute of Technology, Beijing, ChinaSearch for more papers by this authorQuanxin Zhang, Corresponding Author Quanxin Zhang zhangqx@bit.edu.cn orcid.org/0000-0002-5094-7388 School of Computer Science, Beijing Institute of Technology, Beijing, China Correspondence Huali Ren, Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China. Email: rhl717445@163.com Quanxin Zhang, School of Computer Science, Beijing Institute of Technology, Beijing 100081, China. Email: zhangqx@bit.edu.cnSearch for more papers by this author Li Zhang, Li Zhang Department of Media Engineering, Communication University of Zhejiang, Hangzhou, ChinaSearch for more papers by this authorZhao Li, Zhao Li School of Computer Science, Beijing Institute of Technology, Beijing, ChinaSearch for more papers by this authorHuali Ren, Corresponding Author Huali Ren rhl717445@163.com Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China Correspondence Huali Ren, Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China. Email: rhl717445@163.com Quanxin Zhang, School of Computer Science, Beijing Institute of Technology, Beijing 100081, China. Email: zhangqx@bit.edu.cnSearch for more papers by this authorXiao Yu, Xiao Yu Department of Computer Science and Technology, Shandong University of Technology, Zibo, ChinaSearch for more papers by this authorYuxi Ma, Yuxi Ma School of Computer Science, Beijing Institute of Technology, Beijing, ChinaSearch for more papers by this authorQuanxin Zhang, Corresponding Author Quanxin Zhang zhangqx@bit.edu.cn orcid.org/0000-0002-5094-7388 School of Computer Science, Beijing Institute of Technology, Beijing, China Correspondence Huali Ren, Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China. Email: rhl717445@163.com Quanxin Zhang, School of Computer Science, Beijing Institute of Technology, Beijing 100081, China. Email: zhangqx@bit.edu.cnSearch for more papers by this author First published: 20 March 2022 https://doi.org/10.1002/int.22874Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract The broad application of artificial intelligence (AI) shows more and more vulnerabilities. Adversaries have more opportunities to attack AI systems. For example, unmanned vehicles may be interfered with by adversaries in path planning, resulting in unmanned vehicles being unable to move according to the planned route, and even serious safety problems. On the other side, the portrait technology can extract highly refined characteristics of different attack strategies, so that unmanned vehicles can defend themselves based on the characteristics of each attack. Existing research lacks intelligent attack research on path planning in the field of unmanned vehicles, and lacks portraits of attack behaviors in this scenario. This paper combines multiagent reinforcement learning technology, time-series segmentation clustering technology, and knowledge graph technology to study the portrait technology of adversary intelligent attack behavior in the field of unmanned vehicle path planning. First, the simulation results of unmanned vehicle path planning are obtained, and the steps of adversary attack behavior are extracted by using Toeplitz inverse covariance-based clustering time-series segmentation cluster technology. Second, the knowledge graph is used to save the attack strategy, so as to form the attack behavior portrait of unmanned vehicle path planning. The test on the Neo4j platform shows that our method is universal, can effectively describe the attack steps for unmanned vehicle path planning, and provides the basis for attack detection to establish the defense system of unmanned vehicles. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation