Statistical Based Waveform Classification for Cloud Intrusion Detection

Author

Liu, Yiming ; Tseng, Kuo-Kun ; Pan, Jeng-Shyang

Author_Institution

Dept. of Comput. Sci. & Technol., Harbin Inst. of Technol., Shenzhen, China

fYear

2012

fDate

7-9 July 2012

Firstpage

225

Lastpage

228

Abstract

In recent years, many approaches have been proposed for intrusion detection. In this paper, we propose a cloud intrusion detection with a new statistical waveform based classification. It records network connections over a period of time to form a waveform, and then computes the suspicious characteristics of the waveform. It classifies the intrusion with these selected waveform features. In our evaluation, a DARPA Intrusion Detection Data Sets has been used in our evaluation, and the preliminary results confirmed that our approach is feasible.

Keywords

cloud computing; pattern classification; security of data; statistical analysis; waveform analysis; DARPA intrusion detection data sets; cloud intrusion detection; network connections; statistical based waveform classification; suspicious characteristics; waveform features; Artificial neural networks; Bayesian methods; Computer science; Feature extraction; Intrusion detection; Support vector machines; statisitical based intrusion detection; behavior based intrusion detection; cloud intrusion detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Computing, Measurement, Control and Sensor Network (CMCSN), 2012 International Conference on

Conference_Location

Taiyuan

Print_ISBN

978-1-4673-2033-7

Type

conf

DOI

10.1109/CMCSN.2012.118

Filename

6245821