Attack Detection System Using Unsupervised Learning and Multivariate Correlation Analysis
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 5
Abstract
Availability of network services are being vulnerable by the growing range of Denial-of-Service (DoS) attacks. Effective mechanisms for DoS attack detection are demanded. Different systems were projected for detection Dos attacks using machine learning, statistical analysis, data processing or mining, etc. This system is an improvement over earlier system in which k- means clustering is used over a training samples so that it can categorize the samples into different clusters and then it applies statistical methods to find the correlation between features to gain deep information standard deviation, mean and covariance matrix. Applying multivariate correlation analysis on each cluster will help us to get profile parameters according to their cluster and thus allowing us to know sharp boundary of characterizing a sample packet. This will help us to reduce false positive rate and to improve efficiency and detection accuracy.
Authors and Affiliations
Ms. Nisha Bhalke, Mr. Rahul Paikrao
Keywords
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