A multi-agent based mechanism for collaboratively detecting distributed denial of service attacks in internet of vehicles
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE(2022)
Abstract
Distributed denial of service (DDoS) attacks have become a hidden danger in the development of the internet of vehicles (IoV). DDoS attacks for TCP protocol are studied to improve the information security environment of IoV. For the distribution characteristics of DDoS attacks, an information sharing and collaborative detection mechanism based on multi-agent is proposed. Considering the relationship between the features of adjacent moments in the TCP communication, the DDoS detection model based on hidden Markov model is built, and the Viterbi algorithm is improved for the problem of the false alarm in the observation sequence. The optimal communication strategy among agents is determined by deep reinforcement learning, and fusion algorithm is designed to improve the current strategy of agents. Three groups of comparative experiments are designed and analyzed. The simulation results show that proposed algorithms are effective.
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Key words
deep reinforcement learning, distributed denial of service attacks, hidden Markov model, internet of vehicles, multi-agent learning
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