{"title":"A Traffic Normalization Location Attention Network for cyber attack detection in Industrial Cyber-Physical Systems","authors":"Minpeng Cheng, Yongyi Chen, Dan Zhang","doi":"10.1016/j.adhoc.2025.103965","DOIUrl":null,"url":null,"abstract":"<div><div>Cyber attacks are known as one of the main threats of Industrial Cyber-Physical Systems (ICPSs). Although the existing Deep Learning (DL) -based methods can detect cyber attacks to a certain extent, they have shortcomings in weighting the location information of traffic sampling points and have poor suppression effect on redundant information in the spatial dimension, which may lead to an insufficient performance. Based on the above issues, a Traffic Normalization Location Attention Network (TNLAN) is proposed in this paper. Firstly, the location information between the sampling points is dynamically weighted to improve the network weights of the traffic locations. Then, the scale factors of the batch normalization layer are applied to help the detection network suppress the redundant information in the spatial dimension. The results show that TNLAN outperforms existing methods in detecting cyber attacks.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103965"},"PeriodicalIF":4.8000,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ad Hoc Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1570870525002136","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Cyber attacks are known as one of the main threats of Industrial Cyber-Physical Systems (ICPSs). Although the existing Deep Learning (DL) -based methods can detect cyber attacks to a certain extent, they have shortcomings in weighting the location information of traffic sampling points and have poor suppression effect on redundant information in the spatial dimension, which may lead to an insufficient performance. Based on the above issues, a Traffic Normalization Location Attention Network (TNLAN) is proposed in this paper. Firstly, the location information between the sampling points is dynamically weighted to improve the network weights of the traffic locations. Then, the scale factors of the batch normalization layer are applied to help the detection network suppress the redundant information in the spatial dimension. The results show that TNLAN outperforms existing methods in detecting cyber attacks.
期刊介绍:
The Ad Hoc Networks is an international and archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in ad hoc and sensor networking areas. The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks. Specific areas of interest include, but are not limited to:
Mobile and Wireless Ad Hoc Networks
Sensor Networks
Wireless Local and Personal Area Networks
Home Networks
Ad Hoc Networks of Autonomous Intelligent Systems
Novel Architectures for Ad Hoc and Sensor Networks
Self-organizing Network Architectures and Protocols
Transport Layer Protocols
Routing protocols (unicast, multicast, geocast, etc.)
Media Access Control Techniques
Error Control Schemes
Power-Aware, Low-Power and Energy-Efficient Designs
Synchronization and Scheduling Issues
Mobility Management
Mobility-Tolerant Communication Protocols
Location Tracking and Location-based Services
Resource and Information Management
Security and Fault-Tolerance Issues
Hardware and Software Platforms, Systems, and Testbeds
Experimental and Prototype Results
Quality-of-Service Issues
Cross-Layer Interactions
Scalability Issues
Performance Analysis and Simulation of Protocols.