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A Probabilistic Generative Model for Mining Cybercriminal Network from Online Social Media: A Review
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 134 - Number 14 |
| Year of Publication: 2016 |
| Authors: Parvathy G., Bindhu J.S. |
10.5120/ijca2016908121
|
Parvathy G., Bindhu J.S. . A Probabilistic Generative Model for Mining Cybercriminal Network from Online Social Media: A Review. International Journal of Computer Applications. 134, 14 ( January 2016), 1-4. DOI=10.5120/ijca2016908121
Abstract
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.
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