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Anomaly Detection and Access Control for Cloud-Edge Collaboration Networks

Bingcheng Jiang, Qian He*, Zhongyi Zhai, Hang Su

College of Computers and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China

* Corresponding Author: Qian He. Email: email

(This article belongs to this Special Issue: Advanced Achievements of Intelligent and Secure Systems for the Next Generation Computing)

Intelligent Automation & Soft Computing 2023, 37(2), 2335-2353.


Software-defined networking (SDN) enables the separation of control and data planes, allowing for centralized control and management of the network. Without adequate access control methods, the risk of unauthorized access to the network and its resources increases significantly. This can result in various security breaches. In addition, if authorized devices are attacked or controlled by hackers, they may turn into malicious devices, which can cause severe damage to the network if their abnormal behaviour goes undetected and their access privileges are not promptly restricted. To solve those problems, an anomaly detection and access control mechanism based on SDN and neural networks is proposed for cloud-edge collaboration networks. The system employs the Attribute Based Access Control (ABAC) model and smart contract for fine-grained control of device access to the network. Furthermore, a cloud-edge collaborative Key Performance Indicator (KPI) anomaly detection method based on the Gated Recurrent Unit and Generative Adversarial Nets (GRU-GAN) is designed to discover the anomaly devices. An access restriction mechanism based on reputation value and anomaly detection is given to prevent anomalous devices. Experiments show that the proposed mechanism performs better anomaly detection on several datasets. The reputation-based access restriction effectively reduces the number of malicious device attacks.


Cite This Article

B. Jiang, Q. He, Z. Zhai and H. Su, "Anomaly detection and access control for cloud-edge collaboration networks," Intelligent Automation & Soft Computing, vol. 37, no.2, pp. 2335–2353, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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