Anomaly detection based on efficient Euclidean projection. Issue 17 (13th April 2015)

• Record Type: Journal Article
• Authors: Yang, Longqi; Hu, Guyu; Li, Dong; Wang, Yibing; Jia, Bo; Pan, Zhisong
• Is Part Of: Security and communication networks. Volume 8:Issue 17(2015)
• Page Start: 3229
• Page End: 3237
• Publication Date: 2015-04-13
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1002/sec.1247
• Languages: English
• ISSNs: 1939-0114

Abstract

<abstract abstract-type="main" id="sec1247-abs-0001"> <title>Abstract</title> <p id="sec1247-para-0001">Machine‐learning algorithms are widely applied in traffic classification and anomaly detection. Due to the tremendous traffic on the network, an extremely challenging question arises: how to efficiently and accurately detect the anomalous flow from the backbone network. One solution is proposed, online anomaly‐detection scheme, which is based on the sparse feature selection method, Lasso. The sparse feature selection can be efficiently solved by reformulating the problem as an optimization problem with an <italic>ℓ</italic>₁‐ball constraint. At the evaluation stage, the authors preprocessed the raw data trace from the trans‐Pacific backbone link between Japan and the United States and generated an evaluation data set. Their empirical study shows that the feature selection step can be solved quickly by applying the efficient Euclidean projection method; indeed, doing so resolves the feature selection step faster than using three classical <italic>ℓ</italic>₁‐min solvers. In terms of overall accuracy, true positive rate, false positive rate, precision, and F‐measure, the proposed scheme improves the quality of detection. Copyright © 2015 John Wiley &amp; Sons, Ltd.</p> </abstract>