絞り込み

16535

広告

Distributed Online One-Class Support Vector Machine for Anomaly Detection Over Networks.

著者 Miao X , Liu Y , Zhao H , Li C
IEEE Trans Cybern.2018 Mar 01 ; ():.
この記事をPubMed上で見るPubMedで表示
この記事をGoogle翻訳上で見る Google翻訳で開く

スターを付ける スターを付ける     (15view , 0users)

Full Text Sources

Anomaly detection has attracted much attention in recent years since it plays a crucial role in many domains. Various anomaly detection approaches have been proposed, among which one-class support vector machine (OCSVM) is a popular one. In practice, data used for anomaly detection can be distributively collected via wireless sensor networks. Besides, as the data usually arrive at the nodes sequentially, online detection method that can process streaming data is preferred. In this paper, we formulate a distributed online OCSVM for anomaly detection over networks and get a decentralized cost function. To get the decentralized implementation without transmitting the original data, we use a random approximate function to replace the kernel function. Furthermore, to find an appropriate approximate dimension, we add a sparse constraint into the decentralized cost function to get another one. Then we minimize these two cost functions by stochastic gradient descent and derive two distributed algorithms. Some theoretical analysis and experiments are performed to show the effectiveness of the proposed algorithms. Experimental results on both synthetic and real datasets reveal that both of the proposed algorithms achieve low misdetection rates and high true positive rates. Compared with other state-of-the-art anomaly detection methods, the proposed distributed algorithms not only show good anomaly detection performance, but also require relatively short running time and low CPU memory consumption.
PMID: 29994292 [PubMed - as supplied by publisher]
印刷用ページを開く Endnote用テキストダウンロード