Paper Info
Title
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 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.
Year
DOI
Venue
2021
10.1109/JIOT.2020.3041656
IEEE Internet of Things Journal
Keywords
DocType
Volume
Computer crime,Security,Internet of Things,Denial-of-service attack,Monitoring,Performance evaluation,Optimization
Journal
8
Issue
ISSN
Citations 
8
2327-4662
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Wenji He111.03
Yifeng Liu203.72
Haipeng Yao314317.59
Tianle Mai4273.43
Ni Zhang5101.81
Fei Yu65116335.58