NDLI: An LOF-Based Adaptive Anomaly Detection Scheme for Cloud Computing

Content Provider	IEEE Xplore Digital Library
Author	Tian Huang Yan Zhu Qiannan Zhang Yongxin Zhu Dongyang Wang Meikang Qiu Lei Liu
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Kentucky, Lexington, KY, USA (Meikang Qiu) \|\| China Unionpay Corp., Shanghai, China (Lei Liu) \|\| Sch. of Microelectron., Shanghai Jiao Tong Univ., Shanghai, China (Tian Huang; Yan Zhu; Qiannan Zhang; Yongxin Zhu; Dongyang Wang)
Abstract	One of the most attractive things about cloud computing from the perspective of business people is that it provides an effective means to outsource IT. The behaviors of business applications on cloud are constantly evolving due to technical upgrading, cloud migration as well as social outbreaks. These changes bring the challenge of detecting anomalies during the change of applications on cloud. LOF (Local Outlier Factor) algorithm has already been proven as the most promising outlier detection method for detecting network intrusions. To improve the performance of detection, LOF needs a complete set of normal behaviors of business applications, which is usually not available in cloud computing. We present an adaptive anomaly detection scheme for cloud computing based on LOF. Our scheme learns behaviors of applications both in training and detecting phase. It is adaptive to the change during detecting. The adaptability of our scheme reduces demand of efforts on collecting training data before detecting. It also enables the ability to detect contextual anomalies. Experimental results show that our scheme can effectively detect contextual anomalies with relatively low computational overhead.
Starting Page	206
Ending Page	211
File Size	284022
Page Count	6
File Format	PDF
ISBN	9781479921591
DOI	10.1109/COMPSACW.2013.28
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-22
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Cloud computing LOF Cloud Computing Adaptive Knowledge based systems Clustering algorithms Web servers Contextual anomaly Anomaly detection Testing Business
Content Type	Text
Resource Type	Article

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