DocumentCode
182985
Title
Computer anomaly detection based on the moving averages of the power series distributed random sequence
Author
Deqiang Chen
Author_Institution
Dept. of Inf. Sci. & Technol., East China Univ. of Political Sci. & Law, Shanghai, China
fYear
2014
fDate
19-21 Aug. 2014
Firstpage
312
Lastpage
316
Abstract
In order to quickly determine the distribution of anomaly detection model based on small amounts of collected data, the moving relative entropy density deviation method (MREDD) is introduced to test the power series distributed random sequence. Through the moving averages of data analysis and comparison, the anomaly detection models can quickly be established. Experimental results show that this method can be used not only to adaptively choose from the negative binomial model, the binomial distribution model and the Poisson distribution model, but also to reduce the false alarm rate.
Keywords
Poisson distribution; data analysis; entropy; moving average processes; random sequences; security of data; series (mathematics); MREDD; Poisson distribution model; anomaly detection model distribution; binomial distribution model; computer anomaly detection; data analysis; data comparison; moving relative entropy density deviation method; negative binomial model; power series distributed random sequence; Computational modeling; Computers; Data models; Educational institutions; Entropy; Intrusion detection; Random variables; anomaly detection; distribution of power series; moving average; moving relative entropy density deviation;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems and Knowledge Discovery (FSKD), 2014 11th International Conference on
Conference_Location
Xiamen
Print_ISBN
978-1-4799-5147-5
Type
conf
DOI
10.1109/FSKD.2014.6980852
Filename
6980852
Link To Document