A Dynamic Linear Hashing Method for Redundancy Management in Train Ethernet Consist Network

Nie, Xiaobo; Wang, Lide; Wang, Baohua; Liu, Biao; Shen, Peng

doi:10.1155/2016/1980867

Cited by 6 publications

(1 citation statement)

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“…The traditional network architecture of TCMS encompasses two busses: Wire Train Bus (WTB) and Multifunction Vehicle Bus (MVB) [1]. However, due to the constraints of communication rate and expansion capacity, the architecture of WTB&MVB cannot satisfy the actual needs of the railway industry [2]. In response to these requirements, train Ethernet is introduced as the new main network architecture for TCMS.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Intrusion Detection Method for Train Ethernet Consist Network Based on CNN and RNN

Yue

Wang

et al. 2021

IEEE Access

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The train Ethernet Consist Network (ECN) undertakes the task of transmitting critical train control instructions. With the increasing interactions between the train network and the outside environment, masses of network intrusions are threatening the data security of railway vehicles. The intrusion detection system has been proved to be an efficient method to detect network attacks. In this paper, a novel ensemble intrusion detection method is proposed to defense network attacks against the train ECN, in particular IP Scan, Port Scan, Denial of Service (DoS) and Man in the Middle (MITM). Thirty-four features of different protocol contents are extracted from the raw data generated from our ECN testbed to form a specific dataset. A data imaging method and a temporal sequence building method are designed to optimize the dataset. Six base classifiers are built based on several typical convolutional neural networks and recurrent neural networks: LeNet-5, AlexNet, VGGNet, SimpleRNN, LSTM and GRU. A dynamic weight matrix voting method is proposed to integrate all the base classifiers. The proposed method is evaluated based on our dataset. The experiment results show that our method has an outstanding ability to aggregate advantages of all the base classifiers and achieves a superior detection performance with the accuracy of 0.975.INDEX TERMS Train Ethernet Consist Network, industrial cyber security, intrusion detection system, ensemble method.

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Section: Introductionmentioning

confidence: 99%