TY - GEN
T1 - Intelligent Attack Behavior Portrait for Path Planning of Unmanned Vehicles
AU - Li, Zhao
AU - Ma, Yuxi
AU - Zhang, Zhibin
AU - Yu, Xiao
AU - Zhang, Quanxin
AU - Li, Yuanzhang
N1 - Publisher Copyright:
© 2021, Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - With the rapid development of artificial intelligence, opponents can use AI technology to influence the path planning algorithm of unmanned vehicles, making unmanned vehicles face severe safety issues. Aiming at the opponent’s intelligent attack in the scenario of unmanned vehicle path planning, this paper studies the opponent’s intelligent attack behavior portrait technique and proposes an attack behavior portrait scheme based on the knowledge graph. First, according to the simulation experiment of unmanned vehicle path planning based on reinforcement learning, we use Toeplitz Inverse Covariance-based Clustering (TICC) time-series segmentation clustering technology to extract the steps of an opponent’s attack behavior. Then, the attack strategy rules are stored in the knowledge graph to form a portrait of attack behavior for unmanned vehicle path planning. We verified the proposed scheme on the Neo4j platform. The results proved that the method could describe the steps of intelligent attacks on unmanned vehicles well and provide a basis for unmanned vehicle attack detection and establishing an unmanned vehicle defense system. Furthermore, it has good generalizability.
AB - With the rapid development of artificial intelligence, opponents can use AI technology to influence the path planning algorithm of unmanned vehicles, making unmanned vehicles face severe safety issues. Aiming at the opponent’s intelligent attack in the scenario of unmanned vehicle path planning, this paper studies the opponent’s intelligent attack behavior portrait technique and proposes an attack behavior portrait scheme based on the knowledge graph. First, according to the simulation experiment of unmanned vehicle path planning based on reinforcement learning, we use Toeplitz Inverse Covariance-based Clustering (TICC) time-series segmentation clustering technology to extract the steps of an opponent’s attack behavior. Then, the attack strategy rules are stored in the knowledge graph to form a portrait of attack behavior for unmanned vehicle path planning. We verified the proposed scheme on the Neo4j platform. The results proved that the method could describe the steps of intelligent attacks on unmanned vehicles well and provide a basis for unmanned vehicle attack detection and establishing an unmanned vehicle defense system. Furthermore, it has good generalizability.
KW - Attack behavior portrait
KW - Knowledge graph
KW - Time series clustering
KW - Unmanned vehicle path planning simulation
UR - http://www.scopus.com/inward/record.url?scp=85119607499&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-7502-7_6
DO - 10.1007/978-981-16-7502-7_6
M3 - Conference contribution
AN - SCOPUS:85119607499
SN - 9789811675010
T3 - Communications in Computer and Information Science
SP - 53
EP - 60
BT - Data Mining and Big Data - 6th International Conference, DMBD 2021, Proceedings
A2 - Tan, Ying
A2 - Shi, Yuhui
A2 - Zomaya, Albert
A2 - Yan, Hongyang
A2 - Cai, Jun
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International Conference on Data Mining and Big Data, DMBD 2021
Y2 - 20 October 2021 through 22 October 2021
ER -