At present, the train-ground communication system based on the wireless communication protocol is a very important component of communication-based train control (CBTC) systems in intelligent transportation.Its information security is worthy of attention.In order to guarantee the security of the train-ground communication system, this paper proposes an improved AdaBoost multi-classification intrusion detection method based on the n-gram model.
First, the n-gram model is rembrandt - acquerello stucco used to model the state transitions of the IEEE 802.11 protocol.Then, a typical normal behavior set and typical abnormal behavior sets are obtained by learning and biomane detangler they can portray typical behaviors of their respective classes.
Furthermore, a similarity measure algorithm is proposed to construct AdaBoost weak classifiers, which improves the classification effect of AdaBoost algorithm.At last, an AdaBoost multi-classification algorithm is presented to detect and identify the attacks.Experiments prove that the algorithm can effectively detect and distinguish attack types in the train-ground communication system.