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

by Parvathy G., Bindhu J.S.
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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

@article{ 10.5120/ijca2016908121,
author = { Parvathy G., Bindhu J.S. },
title = { A Probabilistic Generative Model for Mining Cybercriminal Network from Online Social Media: A Review },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 134 },
number = { 14 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume134/number14/23979-2016908121/ },
doi = { 10.5120/ijca2016908121 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:34:10.784224+05:30
%A Parvathy G.
%A Bindhu J.S.
%T A Probabilistic Generative Model for Mining Cybercriminal Network from Online Social Media: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 134
%N 14
%P 1-4
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
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.

References
  1. R.Xia, C.Zong, X.Hu and E.Cambria,Feature ensemble plus samples selection:A comprehensive approach to domain adaptation for sentiment classification, IEEE Intell.Syst., vol 28, no.3, pp.10-18, 2013.
  2. R.Li,S.Bao,J.Wang,Y.Yu and Y.Cao, Cominer: An effective algorithm for mining competitors from the web, Data Mining, in Proc.Int. Conf. Data Mining, 2006, pp. 948-952.
  3. D.Rajagopal, D.Olsher, E.Cambria and K. Kwok(2013): Commonsense topic modeling In Proc.ACM Int. Conf. Knowledge Discovery Data mining, Chicago.
  4. Sangno Lee, Jeff Baker,Jaeki Song : An empirical comparison of four text mining methods Proceedings of the 43rd Hawaii International Conference on System Sciences 2010.
  5. A. Sidorova, N. Evangelopoulos, J. Valacich and T. Ramakrishnan, Uncovering the intellectual core of the information systems discipline, MIS Quarterly, 32 (2008), pp. 467-482.
  6. Lingfeng Niu ,Yong Shi :Semi-Supervised PLSA for Document Clustering:2010 International Conference On Data Mining Workshops
  7. M Blie and M.I Jordan(2003): Latent Dirichlet Allocation .J.Mach.Learn Res,993-1022
  8. S. Geman and D. Geman, Stochastic relaxation, Gibbs distributions, and the Bayesian relation of images, IEEE Trans. Pattern Anal. Mach. Intell., vol. 6, no. 6, pp. 721741, 1984
  9. Mining Social Network Data for Cyber Physical System: Manjushree Gokhale, Bhushan Barde, Ajinkya Bhuse, Sonali Kaklij: (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (2) , 2015, 1490-1492
  10. D.Maynard,V.Tablan and C.Ursu,(2001): Name enitity Recognition from diverse text types, In Proc,Conf.Recent Advances Natural Language processing.
  11. S.X .Wu and W.Banzhaf,The use of computational intelligence in intrusion detection systems:A review. Appl.Soft.Comput.,vol 10. No.1.pp.1-35,2010
  12. Y.Xia,W.Su,R.Y.K.Lau and Y.Lie,Discovery latent commercial networks from online financial news article, Enterprise inform.Syst.,vol 7,no.3,pp.303-331,2013
  13. Chris H.Q.Ding A Similarity Based Probability Model for Latent Semantic IndexingProc Of 22nd ACM SIGIR99 Conference, pp.59-65
  14. R. Y. K. Lau, D. Song, Y. Li, C. H. Cheung, and J. X. Hao, Towards a fuzzy domain ontology extraction method for adaptive e-learning, IEEE Trans. Knowl. Data Eng., vol. 21, no. 6, pp. 800813, 2009.
  15. Y. Song, S. Pan, S. Liu, M. X. Zhou, and W. Qian, Topic and keyword re-ranking for LDA-based topic modeling, in Proc. 18th ACM Conf. Information Knowledge Management, 2009, pp. 17571760.
  16. J.Y.Nie,G.Cao and J.Bai,Inferential language models for information retrieval,ACM Trans. Asian Lang.Inf.Process., vol 5, no.4, pp.296-322,2006
  17. Dynamic Social Network Analysis of a DarkNetwork: Identifying Significant Facilitators, Siddharth Kaza, Daning Hu, and Hsinchun Chen, Fellow, IEEE
  18. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering: Mikhail Belkin and Partha Niyogi
Index Terms

Computer Science
Information Sciences

Keywords

Latent Dirichlet Allocation(LDA) Laplacian Semantic Ranking Inferential Language Model Text Mining

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