A Method for finding threated web sites through crime data mining and sentiment analysis

  • Authors

    • K V. Daya Sagar
    • Ch Shyam Krishna
    • G Lalith Kumar
    • P Surya Teja
    • G Charless Babu
    https://doi.org/10.14419/ijet.v7i2.7.10261

    Received date: March 18, 2018

    Accepted date: March 18, 2018

    Published date: March 18, 2018

  • Cybercrime, Data Mining, Semantic Analysis, Sentiment Analysis, Clustering, Most Used Sites For Crime.

  • Abstract

    A Fast growth on the internet provided an opportunity for crime to develop in it. This includes using technology for planning illegal activities and message passing during the crime. It also includes replacing the documents with others which results in different effects. Majority of this done in the form of text. There may be a chance of using regular websites for doing crimes. This paper helps to find such websites through crime data mining and sentiment analysis. Sentiment analysis helps to find the regularly used sites of a user. Crime data mining helps to find the illegal text on the website. By combining both these techniques we can find the most threatened and most regularly used websites for the crime.

  • References

    1. K. Ahmad, Y. Almas, Visualising sentiments in financial texts? Proceedings of the Ninth International Conference on Infor-mation Visualisation (2005) 363–368.
    2. C. Asavathiratham, The Influence Model: A Tractable Represen-tation for the Dynamics of Networked Markov Chains, Dept. of EECS. 2000, MIT, Cambridge, 2000, p. 188.
    3. P. Chaovalit, L. Zhou, Movie review mining: a comparison be-tween supervised and unsupervised classification approaches, Proceedings of the 38th Hawaii International Conference on System Sciences, 2005.
    4. K.W. Cheung, J.T. Kwok, M.H. Law, K.C. Tsui, Mining cus-tomer product ratings for personalized marketing, Decision Sup-port Systems 35 (2) (2003) 231–243.
    5. J. Coble, D. Cook, R. Rathi, L. Holder, Iterative structure dis-covery in graph-based data, International Journal of Artificial In-telligence Techniques 1–2 (14) (2005) 101–124.
    6. Fayyad, U.M., and Uthurusamy, R. ( Aug. 2002). Evolving Data Mining into Solutions for Insights. Comm. ACM. 28-31.
    7. Hosseinkhani, J., Chuprat, S., and Taherdoost, H., (2012a). Criminal Network Mining by WebStructure and Content Mining. 11th WSEAS International Conference on Information Security and Privacy (ISP '12), Prague, Czech Republic September 24-26.
    8. Hosseinkhani, J., Chuprat, S., Taherdoost, H., and Shahraki Moghaddam, Amin., (2012b). Propose a Framework for Criminal Mining by Web Structure and Content Mining. International Journal of Advanced Computer Science and Information Tech-nology (IJACSIT), Helvetic Editions. 1(1). 1-13.
    9. Keyvanpour, M., Javideh, M., and Ebrahimi, M. (2011). Detect-ing and investigating crime by means of data mining: a general crime matching framework, Elsevier, Procedia Computer Science 3 (2011) 872–880.

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  • How to Cite

    V. Daya Sagar, K., Shyam Krishna, C., Lalith Kumar, G., Surya Teja, P., & Charless Babu, G. (2018). A Method for finding threated web sites through crime data mining and sentiment analysis. International Journal of Engineering and Technology, 7(2.7), 62-65. https://doi.org/10.14419/ijet.v7i2.7.10261