A transfer learning-based intrusion detection system for zero-day attack in communication-based train control system

Lu, He; Zhao, Yanan; Song, Yajing; Yang, Yang; He, Guanjie; Yu, Haiyang; Ren, Yilong

doi:10.1007/s10586-024-04376-9

Cluster Comput

2024

DOI: 10.1007/s10586-024-04376-9

|View full text |Cite

A transfer learning-based intrusion detection system for zero-day attack in communication-based train control system

He Lu,

Yanan Zhao,

Yajing Song

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

MSRNet-GLAM: A novel intrusion detection method for train communication network

Chen,

He,

Jin

et al. 2025

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

MSRNet-GLAM: A novel intrusion detection method for train communication network

Chen,

He,

Jin

et al. 2025

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

Securing IoT devices with zero day intrusion detection system using binary snake optimization and attention based bidirectional gated recurrent classifier

Almuflih,

Abdullayev,

Bakhvalov

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The fast improvement of cyberattacks in the area of the Internet of Things (IoT) presents novel safety challenges to zero-day attacks. Intrusion detection systems (IDS) are generally focused on exact attacks to defend the use of IoT. However, the attacks were unidentified, for IDS still signifies tasks and concerns about consumers’ data privacy and safety. Anomaly-detection models are generally based on machine learning (ML) models. Conventional ML-based models have been recognized to have low estimate excellence and recognition rates. DL-based models, particularly convolutional neural networks (CNN) with regularization techniques, direct this problem, offer a superior prediction value with unidentified data, and prevent over-fitting. This manuscript presents a Binary Snake Optimizer with DL-Enabled Zero-Day Attack Detection and Classification (BSODL-ZDADC) method. The objective of the BSODL-ZDADC method is to employ metaheuristics with the DL method for enhanced recognition and classification of zero-day attacks. For data normalization, the BSODL-ZDADC method uses a Z-score normalization approach. To reduce the high dimensionality issue and improve the classification results, the BSODL-ZDADC technique designs a BSO method to choose a set of related features. Besides, the attention-based bidirectional gated recurrent unit (ABi-GRU) method helps recognize zero-day attacks. Since the hyperparameters play a vital part in the classification performance, the BSODL-ZDADC technique employs an improved sparrow search algorithm (ISSA). The experimental validation of the BSODL-ZDADC technique is verified by utilizing the ToN-IoT dataset. The performance validation of the BSODL-ZDADC technique portrayed a superior accuracy value of 98.28% over other models.

show abstract

Train Service Design for Rail Transit Cross-Line Operation Applying Virtual Coupling

Sun,

Liu,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The cross-line operation (CO) of trains in urban rail transit is an effective method to efficiently satisfy transfer passenger travel demand as well as relieve the pressure of transfer stations. The primary problem of CO is designing train services to satisfy travel demand with an uneven spatial distribution of passengers. This study constructs a nonlinear integer programming model with a novel train operation scheme, i.e., virtual coupling (VC) technology, which allows the coupling/decoupling of trains on different lines at both ends of each operation zone. This scheme makes the train capacity equitably distributed in each operation zone, thereby balancing train capacity utilization over the whole CO system. Regarding the nonlinear characteristics of the proposed model, an adaptive simulated annealing genetic algorithm (ASA-GA) was designed to quickly generate high-quality solutions. Based on real-world data from the Beijing Changping Line and Line 13, the effectiveness of the proposed model and algorithm were verified. The computation results show that in comparison to a single grouping train composition scheme without CO, a VC scheme with CO would reduce operation costs by 46.8%, with 80.6% savings of train capacity equity. Furthermore, the average passenger residence time would be reduced by 25.9%.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A transfer learning-based intrusion detection system for zero-day attack in communication-based train control system

Cited by 3 publications

References 50 publications

MSRNet-GLAM: A novel intrusion detection method for train communication network

MSRNet-GLAM: A novel intrusion detection method for train communication network

Securing IoT devices with zero day intrusion detection system using binary snake optimization and attention based bidirectional gated recurrent classifier

Train Service Design for Rail Transit Cross-Line Operation Applying Virtual Coupling

Contact Info

Product

Resources

About