DocumentCode :
2691186
Title :
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
Link To Document :
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