A Cross-Layer Defense Scheme for Edge Intelligence-Enabled CBTC Systems Against MitM Attacks

Yang, Li; Zhu, Li; Wang, Hongwei; Yu, F. Richard; Liu, Shichao

doi:10.1109/tits.2020.3030496

Cited by 65 publications

(35 citation statements)

References 33 publications

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“…It is easy to imagine that it is about to cause a train operation incident and pose a threat to the safety of passengers. ere are three kinds of MITM attacks that are highly likely to occur in TCE: Tamper attacks [34], Replay attacks [35], and ARP spoofing attacks [36].…”

Section: Underlying Temporal Intrusions Against Tcementioning

confidence: 99%

Detecting Temporal Attacks: An Intrusion Detection System for Train Communication Ethernet Based on Dynamic Temporal Convolutional Network

Yue

Wang

et al. 2021

Security and Communication Networks

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The train communication Ethernet (TCE) of modern intelligent trains is under an ever-increasing threat of serious network attacks. Denial of service (DoS) and man in the middle (MITM), the two most destructive attacks against TCE, are difficult to detect by conventional methods. Aiming at their highly time-correlated properties, a novel dynamic temporal convolutional network-based intrusion detection system (DyTCN-IDS) is proposed in this paper to detect these temporal attacks. A semiphysical TCE testbed that is capable of simulating real situations in TCE-based trains is first built to generate an effective dataset for training and testing. DyTCN-IDS consists of two phases, and in the first phase, systematic feature engineering is designed to optimize the dataset. In the second phase, a basic detection model that is good at dealing with temporal features is first built by utilizing the temporal convolutional network with several architectural optimizations. Then, in order to decrease the computational consumption waste on network packet sequences with different lengths of inner temporal relationships, dynamic neural network technology is further adopted to optimize the basic detection model. Diverse experiments were carried out to evaluate the proposed system from different angles. The experimental results indicate that our system is easy to train, converges fast, costs less computational resources, and achieves satisfying detection performance with a macro false alarm rate of 0.09%, a macro F-score of 99.39%, and an accuracy of 99.40%. Compared to some canonical DL-based IDS and some latest IDS, our system acquires the best overall detection performance as well.

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Section: Underlying Temporal Intrusions Against Tcementioning

confidence: 99%

Detecting Temporal Attacks: An Intrusion Detection System for Train Communication Ethernet Based on Dynamic Temporal Convolutional Network

Yue

Wang

et al. 2021

Security and Communication Networks

View full text Add to dashboard Cite

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“…Lu et al [29] train the double-output branch deep network to detect the whole body and visible part of the pedestrian, respectively, and then combine the results of the two parts to improve the pedestrian detection rate in the case of occlusion [30] that introduces a supplementary subnetwork to generate a high-resolution feature map for small-scale pedestrian detection, thus improving the detection accuracy of a small-scale pedestrian. However, the existing methods are generally complex, and although they have achieved good results on relevant datasets, they are hard to meet the requirements [31][32][33] of application in a subway station. Therefore, a simple, efficient, and fast method is still needed to solve the problem of passenger detection in subway stations.…”

Section: Pedestrian Detectionmentioning

confidence: 99%

SPDNet: A Real‐Time Passenger Detection Method Based on Attention Mechanism in Subway Station Scenes

Yang

Zheng

Yan

et al. 2021

Wireless Communications and Mobile Computing

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In order to implement real-time detection of passengers in subway stations, this paper proposes the SPDNet based on YOLOv4. Aiming at the low detection accuracy of passengers in the subway station due to uneven light conditions, we introduce the attention mechanism CBAM to recalibrate the extracted features and improve the robustness of the network. For the crowded areas in the subway station, we use the K-means++ algorithm to generate anchors that are more consistent with the passenger aspect ratio based on the dataset KITTI, which mitigates the missing caused by the incorrect suppression of true positive boxes by the Nonmaximum Suppression algorithm. We train and test our SPDNet on the KITTI dataset and prove the superiority of our method. Then, we carry out transfer learning based on the subway surveillance video dataset collected by ourselves to make it conform to the distorted passenger targets under the angle of the surveillance camera. Finally, we apply our network in a Beijing subway station and achieve satisfactory results.

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“…Simultaneously, with the increase of the communication limit time, the improved range of reliability gradually decreases, indicating that the influence of the communication limit time on reliability gradually decreases. 12 Wireless Communications and Mobile Computing Without considering the delay and retransmission of cable transmission between ground equipment, the time for the roaming switching communication process conforms to the Gamma distribution, and its density function is Equation (2). Figure 15 shows the reliability of the communication scenario.…”

Section: Reliability Analysis Of Communication Scenarios For On-board Terminalmentioning

confidence: 99%

“…The existing CBTC system is technically very mature. However, there are still certain shortcomings: the system structure is involved, which leads to too many interfaces between subsystems; the information transmission process is cumbersome, and the on-board equipment cannot directly obtain relevant information, which limits the increase in train speed; the system consists of more equipment, resulting in higher system construction and operating costs, which is not conducive to the large-scale development of urban rail transit [2].…”

Section: Introductionmentioning

confidence: 99%

LTE-U based Train to Train Communication System in CBTC: System Desin and Reliability Analysis

Liang

Zhao

Wang

et al. 2020

Wireless Communications and Mobile Computing

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Communication-Based Train Control (CBTC) system is a critical signal system to ensure rail transit’s safe operation. Compared with the train-ground CBTC system, the train control system based on train-to-train (T2T) communication has the advantages of fast response speed, simple structure, and low operating cost. As the core part of the train control system based on T2T communication, the reliability of the data communication system (DCS) is of great significance to ensure the train’s safe and efficient operation. According to the T2T communication system requirements, this paper adopts Long-Term Evolution-Unlicensed (LTE-U) technology to design the DCS structure and establishes the reliability model of the communication system based on Deterministic and Stochastic Petri Nets (DSPN). Based on testing the real line’s communication performance parameters, the DSPN model is simulated and solved by π -tool, and the reliability index of the system is obtained. The research results show that the LTE-U-based T2T communication system designed in this paper meets the train control system’s needs for communication transmission. This paper’s reliability evaluation method can complete the reliability modeling of train control DCS based on T2T communication. The research in this paper will provide a strong practical and theoretical basis for the design and optimization of train control DCS based on T2T communication.

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A Cross-Layer Defense Scheme for Edge Intelligence-Enabled CBTC Systems Against MitM Attacks

Cited by 65 publications

References 33 publications

Detecting Temporal Attacks: An Intrusion Detection System for Train Communication Ethernet Based on Dynamic Temporal Convolutional Network

Detecting Temporal Attacks: An Intrusion Detection System for Train Communication Ethernet Based on Dynamic Temporal Convolutional Network

SPDNet: A Real‐Time Passenger Detection Method Based on Attention Mechanism in Subway Station Scenes

LTE-U based Train to Train Communication System in CBTC: System Desin and Reliability Analysis

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