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

Haitao Yuan, Shen Wang, Jing Bi, Jia Zhang, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)18035-18046
Number of pages12
JournalIEEE Internet of Things Journal
Volume11
Issue number10
DOIs
StatePublished - May 15 2024

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

Keywords

  • Anomaly time series detection
  • deep learning
  • network cyberattacks
  • neural networks
  • spatiotemporal features

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