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Distributed Variational Bayes-Based In-Network Security for the Internet of Things | IEEE Journals & Magazine | IEEE Xplore

Distributed Variational Bayes-Based In-Network Security for the Internet of Things


Abstract:

The past few years have witnessed the compelling applications of the Internet of Things (IoT) in our daily life. The explosive growth of the number of IoT devices also pr...Show More

Abstract:

The past few years have witnessed the compelling applications of the Internet of Things (IoT) in our daily life. The explosive growth of the number of IoT devices also presents a great challenge in network security, especially the DDoS attack. Current DDoS defense mechanisms adopted out-of-band architecture, which is accomplished by a process that receives monitoring data from routers and switches, then analyzes that flow data to detect attacks. However, facing IoT devices growing rapidly, this out-of-band architecture confronted with limited processing capacity, bandwidth resources, and service assurance problems. Recently, with the development of the programming switch, it opens up new possibilities for in-network DDoS detection, where the detection algorithms could be directly implemented inside the routers and switches. Benefit from switch processing performance, the in-network mechanism could achieve high scalability and line speed performance. Therefore, in this article, we design a machine learning-based in-network DDoS detection framework. We implement the lightweight variational Bayes algorithm in each switch to detect the anomaly traffic. Besides, considering the shortage of training data in each switch, a centralized platform is introduced to synchronize parameters among distributed switches to realize collaborative learning. Extensive simulations are conducted to evaluate our proposed algorithm in comparison to some state-of-the-art schemes.
Published in: IEEE Internet of Things Journal ( Volume: 8, Issue: 8, 15 April 2021)
Page(s): 6293 - 6304
Date of Publication: 01 December 2020

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