Hybrid and Spatiotemporal Detection of Cyberattack Network Traffic in Cloud Data Centers

Document Type

Article

Publication Date

5-15-2024

Abstract

The rapid expansion of Internet users results in an immense influx of network traffic within extensive cloud data centers. Accurate and instantaneous identification and forecasting of network traffic aid system managers in efficiently distributing resources, assessing network performance based on specific service demands and scrutinizing the health of network status. However, sources and distributions of traffic are different, which makes accurate warnings of cyberattack traffic difficult. Recently, emerging neural networks have demonstrated their efficacy in forecasting time series data of network cyberattacks. The time series has temporal and spatial features, which can be efficiently captured with Informer and convolutional neural networks (CNNs). To realize high-performance spatiotemporal detection of cyberattacks, this work for the first time designs a hybrid and spatiotemporal prediction framework, which integrates CNNs, Informer, and a Softmax classifier to realize high-classification accuracy of normal and abnormal cyberattacks. Real-life data are adopted to evaluate the proposed method, which yields significant improvement in classification accuracy over typical benchmark classification models.

Identifier

85187267949 (Scopus)

Publication Title

IEEE Internet of Things Journal

External Full Text Location

https://doi.org/10.1109/JIOT.2024.3360294

e-ISSN

23274662

First Page

18035

Last Page

18046

Issue

10

Volume

11

Grant

62173013

Fund Ref

National Natural Science Foundation of China

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