首页|Path signature-based XAI-enabled network time series classification

Path signature-based XAI-enabled network time series classification

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Classifying network time series(NTS)is crucial for automating network administration and ensuring cyberspace security.It enables the detection of anomalies,the identification of network attacks,and the monitoring of performance issues,thereby providing valuable support for network protection and optimization.However,modern communication networks pose challenges for NTS classification methods.These include handling large-scale and complex NTS data,extracting features from intricate datasets,and addressing explainability requirements.These challenges are particularly pronounced for complex 5G net-works.Notably,explainability has become crucial for the widespread deployment of network automation for 5G networks and beyond.To tackle these challenges,we propose a path-signature-based NTS classi-fication model called recurrent signature(RecurSig).This innovative model is designed to overcome the time-consuming feature selection problem by utilizing deep-learning(DL)techniques.Additionally,it pro-vides a solution for addressing the black-box issue associated with DL models in network automation systems(NAS)by incorporating an explainable classification approach.Extensive experimentation on various public datasets demonstrates that RecurSig outperforms existing models in accuracy and explainability.The results indicate its potential for application in cyberspace security and automated network management,offering an explainable solution for network protection and optimization.

network time series classificationexplainable artificial intelligencepath signatureautomated network managementrecurrent neural network

Le SUN、Yueyuan WANG、Yongjun REN、Feng XIA

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Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET),Nanjing University of Information Science and Technology,Nanjing 210044,China

School of Computing Technologies,Royal Melbourne Institute of Technology,Melbourne VIC 3000,Australia

National Natural Science Foundation of ChinaNational Natural Science Foundation of China

6170227462072249

2024

10.1007/s11432-023-3978-y

中国科学:信息科学(英文版)
中国科学院

中国科学:信息科学(英文版)

CSTPCDEI
影响因子：0.715
ISSN：1674-733X
年,卷(期)：2024.67(7)