Accurately identifying online user behavior is challenging task because while identifying malicious users, legitimate user should be separated correctly. Normal and suspicious users should be differentiated. Various classification methods are useful in this behavior detection process. Some of them give good performance and accurate results. Few metrics are used to deviate malicious users from good one. Security is the main concern need to provide to various online applications. Characteristics of user behavior can be studied on variety of OSNs, online news website, shopping web site for prize comparison, browsing behavior, search engine behavior through queries, users' communication behavior through various online messaging platforms etc. This paper gives analysis of various characteristics of online user behavior models. User behavior methods are compared and analyzed.Keywords k-means, Markov Logic Network, online user behavior, social network, user behavior model

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more prevalent in MRSA (38.46%), followed by E. coli and K. pneumoniae (15.39% each). In post-op. infections, only one

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