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Cluster Computing
Published in: Cluster Computing 3/2019

20-10-2017

An anomaly detection method based on Lasso

Authors: Shanxiong Chen, Maoling Peng, Hailing Xiong, Sheng Wu

Published in: Cluster Computing | Special Issue 3/2019

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Abstract

In many research and application fields, anomaly detection always is an important issue. In the article, a method of anomaly detection is presented which based on Lasso on the basis of variable linear regression solution of the Lasso problem. We transform the process of anomaly detection into a linear regression model, meanwhile, take the detection parameter as regression variables and establish the model of the regression variables and the dependent variable. Due to estimation of Lasso parameter own stable regression coefficient, can compress parameters of the model and reduce the number of parameters. Those characteristics accord with the requirement of stability, high-speed and simplicity which are needful for anomaly detection. Experimental results show that our method has higher detection accuracy and more rapid convergence ability under the constraints of the appropriate threshold.
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Literature
1.
V, C., A, B., V, K.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 15 (2009)
2.
Huang, J., Kalbarczyk, Z., Nicol, D.M.: Knowledge Discovery from Big Data for Intrusion Detection Using LDA. In: 2014 IEEE International Congress on Big Data (BigData Congress), pp. 760–761 (2014)
3.
Li-ming, Z., Peng, Z., Wei-hong, Z., et al.: Anomaly detection in backbone networks using filter-ary-sketch. J. Commun. 32(12), 151–160 (2011)
4.
M, X., S, H., Tian, B., et al.: Anomaly detection in wireless sensor networks: a survey. J. Netw. Comput. Appl. 34(4), 1302–1325 (2011)CrossRef
5.
Ye-Kui, Q., Ming, C., Li-Xin, Y., et al.: Network-wide anomaly detection method based on multiscale principal component analysis. J. Softw. 23(2), 361–377 (2012)CrossRef
6.
LI-Ming, Z., Peng, Z., Yan, J., et al.: How to extract and train classifier in traffic anomaly detection system. Chin. J. Comput. 35(4), 719–730 (2012)CrossRef
7.
Eskin, E., Arnold, A., Prerau, M., Portnoy, L., Stolfo, S.A.: Geometric framework for unsupervised anomaly detection. Adv. Inf. Sec. 6, 77–101 (2002)
8.
C, K., G, V., W, R.: A multi-model approach to the detection of web-based attacks. Comput. Netw. 48(5), 717–738 (2005)CrossRef
9.
J, S.S., F, A., Eskin, E., et al.: Aapplications of data mining in computer security. J. Comput. Sec. 13(4), 659–693 (2005)CrossRef
10.
R, P., Ariu, D., Fogla, P., et al.: McPAD: a multiple classifier system for accurate payload-based anomaly detection. Comput. Netw. 53(6), 864–881 (2009)MATHCrossRef
11.
Schölkopf, B., Platt, J.C., Shawe-Taylor, J., et al.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)MATHCrossRef
12.
13.
Tsang, C.H., Kwong, S., Wang, Hanli: Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection. Pattern Recognit. Lett. 40(9), 2373–2391 (2007)MATHCrossRef
14.
Hodge, V.J., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22(2), 85–126 (2004)MATHCrossRef
15.
Burbeck, K.: Current research and use of anomaly detection. In: Proceedings of the Fourteenth IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, pp. 138–138 (2005)
16.
Markou, M., Singh, S.: Novelty detection: a review—part 1: statistical approaches. Signal Process. 83(12), 2481–2497 (2003)MATHCrossRef
17.
Bolton, P.: Banking in emerging markets. J. Financ. Intermed. 11, 11362–11365 (2002)CrossRef
18.
Tax, D.M.J.: One-Class Classification: Concept-Learning in the Absence of Counter-Examples. Delft University of Technology, Delft (2001)
19.
Gupta, M., Sharma, A.B., Chen, H.: Context-aware time series anomaly detection for complex systems. In: Workshop Notes, vol. 14 (2013)
20.
Feng, A., Chen, S.: Study on one-class classifiers based on Kernel method. J. NanJing Normal Univ. 8(4), 1–5 (2008)
21.
Meinshausen, N., Yu, B.: Lasso-type recovery of sparse representations for high-dimensional data. Ann. Stat. 37(1), 246–270 (2009)MathSciNetMATHCrossRef
22.
Bickel, P.J., Ritov, Y.A., Tsybakov, A.B.: Simultaneous analysis of Lasso and Dantzig selector. Ann. Stat. 37(4), 1705–1732 (2009)MathSciNetMATHCrossRef
23.
24.
Li, F., Lu, Y.-Q., Li, G.: Variable selection of the adaptive LASSO of part the linear model. Chin. J. Appl. Probab. Stat. 28(6), 614–624 (2012)MATH
25.
26.
27.
Fu, S.: Hierarchical Bayesian LASSO for a negative binomial regression. J. Stat. Comput. Simul. 86(11), 2182–2203 (2016)MathSciNetCrossRef
28.
Lu, G., Zou, J., Wang, Y.: L1-norm and maximum margin criterion based discriminant locality preserving projections via trace Lasso. Pattern Recognit. Lett. 55, 207–214 (2016)MATHCrossRef
29.
Fosson, S., Matamoros, J., Anton-Haro, C., Magli, E.: Distributed recovery of jointly sparse signals under communication constraints. IEEE Trans. Signal Process. 64(13), 3470–3482 (2016)MathSciNetMATHCrossRef
Metadata
Title
An anomaly detection method based on Lasso
Authors
Shanxiong Chen
Maoling Peng
Hailing Xiong
Sheng Wu
Publication date
20-10-2017
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 3/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1255-z

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