Host Anomalies Detection Using Logistic Regression Modeling

Author

Gao, Cuixia ; Li, Zhitang ; Chen, Lin

Author_Institution

Sch. of Comput. Sci. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan

Volume

1

fYear

2009

fDate

7-8 March 2009

Firstpage

655

Lastpage

659

Abstract

Malicious activities will lead to abnormal host traffic patterns. This paper presents a model of host anomalies detection that can be used given bi-directional flow data. We first select a group of variables to represent the host traffic, and then use a Bayesian logistic regression, which was developed using a combination of expert experiences and manually-flagged training data to evaluate the probability of host anomaly. The primary experiment results indicate the approach is effective.

Keywords

Bayes methods; regression analysis; security of data; Bayesian logistic regression; abnormal host traffic patterns; bidirectional flow data; host anomalies detection; logistic regression modeling; malicious activities; manually-flagged training data; Bayesian methods; Computer science; Computer science education; Educational technology; Logistics; Pattern analysis; Statistics; Telecommunication traffic; Traffic control; Training data; host anomaly detection; logistic regression model;

fLanguage

English

Publisher

ieee

Conference_Titel

Education Technology and Computer Science, 2009. ETCS '09. First International Workshop on

Conference_Location

Wuhan, Hubei

Print_ISBN

978-1-4244-3581-4

Type

conf

DOI

10.1109/ETCS.2009.152

Filename

4958856