A Probabilistic Generative Model for Mining Cybercriminal Network from Online Social Media: A Review

Social media has been increasingly utilized as an area of sharing and gathering of information. Data mining is the process of analyzing data from different context and summarizes them into useful information. It allows the users to analyze the data, categorize them and identifies the relationship in...

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Bibliographic Details
Published in:International journal of computer applications 2016-01, Vol.134 (14), p.1-4
Main Authors: G, Parvathy, S, Bindhu J
Format: Article
Language:English
Subjects:
Crime
Data mining
Digital media
Forensic engineering
Information retrieval
Networks
Social networks
Texts
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Summary:Social media has been increasingly utilized as an area of sharing and gathering of information. Data mining is the process of analyzing data from different context and summarizes them into useful information. It allows the users to analyze the data, categorize them and identifies the relationship inferred in them. Text mining often referred to as text data mining can be used to derive information from text. Text analysis can be used in information retrieval, information extraction, pattern recognition, frequency distribution and data mining techniques. An application of this is to scan a set of documents in natural language for predictive classification purposes. Recent researches shows that the number of crimes are increasing through social media that may cause tremendous loss to organizations. Existing security methods are weak in cyber crime forensics and predictions. The contribution of this paper is to mine cybercriminal network which can reveal both implicit and explicit meanings among cybercriminal based on their conversation messages.
ISSN:0975-8887
0975-8887
DOI:10.5120/ijca2016908121