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Towards anomalous diffusion sources detection in a large network

Zhang, Peng, He, Jing, Long, Guodong, Huang, Guangyan and Zhang, Chengqi 2016, Towards anomalous diffusion sources detection in a large network, ACM transactions on internet technology, vol. 16, no. 1, pp. 1-24, doi: 10.1145/2806889.

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Title Towards anomalous diffusion sources detection in a large network
Author(s) Zhang, Peng
He, Jing
Long, Guodong
Huang, Guangyan
Zhang, Chengqi
Journal name ACM transactions on internet technology
Volume number 16
Issue number 1
Start page 1
End page 24
Total pages 24
Publisher ACM Digital Library
Place of publication New York, N.Y.
Publication date 2016-02
ISSN 1533-5399
1557-6051
Summary Witnessing the wide spread of malicious information in large networks, we develop an efficient method to detect anomalous diffusion sources and thus protect networks from security and privacy attacks. To date, most existing work on diffusion sources detection are based on the assumption that network snapshots that reflect information diffusion can be obtained continuously. However, obtaining snapshots of an entire network needs to deploy detectors on all network nodes and thus is very expensive. Alternatively, in this article, we study the diffusion sources locating problem by learning from information diffusion data collected from only a small subset of network nodes. Specifically, we present a new regression learning model that can detect anomalous diffusion sources by jointly solving five challenges, that is, unknown number of source nodes, few activated detectors, unknown initial propagation time, uncertain propagation path and uncertain propagation time delay. We theoretically analyze the strength of the model and derive performance bounds. We empirically test and compare the model using both synthetic and real-world networks to demonstrate its performance.
Language eng
DOI 10.1145/2806889
Field of Research 080501 Distributed and Grid Systems
080303 Computer System Security
0801 Artificial Intelligence And Image Processing
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, ACM Digital Library
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083554

Document type: Journal Article
Collection: School of Information Technology
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