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Detection of Criminally Convicted Email Users by Behavioral Dissimilarity

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Advances in Nature and Biologically Inspired Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 419))

Abstract

The phenomenon of social interactions is prevailing charismatically like a spider net in nowadays society despite the people busy lives. In this fashion, people willingly supply their private or public data without sensing the threat of any information theft. These kinds of information could be easily misused and could be analyzed by any third party for malicious or non-malicious purposes. In this paper, detection of irregular or anomalous individual are focused. Individual with behavioral dissimilarity are discovered and validated with the real denounced victims. An affluent feature set of 15 characteristics is anticipated for deviation detection. The kth nearest neighbour technique is applied on the Enron dataset for finding accused email users. Noteworthy outputs are achieved by implication of the KNN method.

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References

  1. Qureshi, P.A.R., Memon. N., Wiil, U.K.: EWaS: novel approach for generating early warnings to prevent terrorist attacks 2010. In: Second International Conference on Computer Engineering and Applications, pp. 410–414 (2010)

    Google Scholar 

  2. Gupta, N., Dey, L.: Detection and characterization of anomalous entities in social communication networks. In: 20th International Conference on Pattern Recognition, pp. 738–741, Aug 2010

    Google Scholar 

  3. Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Texas, United States (2000)

    Google Scholar 

  4. Nithi, Dey, L.: Anomaly detection from call data records. Soc. Netw. 237–242 (2009)

    Google Scholar 

  5. Tarapata, Z., Kasprzyk, R.: An application of multicriteria weighted graph similarity method to social networks analyzing. In: International Conference on Advances in Social Network Analysis and Mining, vol. 1, pp. 366–368, Jul 2009

    Google Scholar 

  6. Weinstein, C., Campbell, W., Delaney, B., Leary, G.O., Street, W.: Modeling and Detection Techniques for Counter-Terror Social Network Analysis and Intent Recognition (2009)

    Google Scholar 

  7. Lu, W., Tavallaee, M., Ghorbani, A.A.: Detecting network anomalies using different wavelet basis functions. In: 6th Annual Communication Networks and Services Research Conference (cnsr 2008), pp. 149–156, May 2008

    Google Scholar 

  8. Kurkovsky, S., Strimple, D., Nuzzi, E., Verdecchia, K.: Mobile voice access in social networking systems. In: Fifth International Conference on Information Technology: New Generations (itng 2008), pp. 982–987, Apr 2008

    Google Scholar 

  9. Larsen, H.L., Vejin, N.B.: Practical Approaches for Analysis, Visualization and Destabilizing Terrorist Networks (2006)

    Google Scholar 

  10. Negnevitsky, M., huey Lim, M.J., Hartnett, J., Reznik, L.: Email communications analysis: how to use computational intelligence methods and tools? System 16–23 (2005)

    Google Scholar 

  11. Mining, U.W.E.B., S. Network, A. To, S. The, E. Of, C. Communities, and I.N. Blogs.: Using web mining and social network analysis to study the emergence of cyber communities in blogs. Computer

    Google Scholar 

  12. de Lin, S., Chalupsky, H.: Discovering and explaining abnormal nodes in semantic graphs. IEEE Trans. Knowl. Data Eng. 20, 1039–1052 (2008)

    Article  Google Scholar 

  13. Bhatia, M.P.S, Gaur, P.: Statistical approach for community mining in social networks. In: IEEE International Conference on Service Operations and Logitics, and Informatics, pp. 207–211, Oct 2008

    Google Scholar 

  14. Hui-Yi, H., Hung-Yuan, P.: Use behaviors and website experiences of Facebook community. Statistics 1, 379–383, 2010

    Google Scholar 

  15. Carrier, B., Spafford, E.H.: Getting physical with the digital investigation process. Int. J. 2, 1–20 (2003)

    Google Scholar 

  16. Saferstein, R.: Criminalistics: An Introduction to Forensic Science, 7th edn. Pearson, London (2000)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Sciences, ALMAAREFA Colleges of Science and Technology (MCST), Riyadh, Kingdom of Saudi Arabia

    Maqsood Mahmud

  2. Department of Computer Sciences, Sardar Patel University, Anand, India

    Pranavkumar Pathak & Vaishalibahen Pathak

  3. Department of Public Administration, Kenshu Institute of VU, Wah Campus, Virtual University of Pakistan, Wah, Pakistan

    Zahra Afridi

Authors
  1. Maqsood Mahmud
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  2. Pranavkumar Pathak
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  3. Vaishalibahen Pathak
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  4. Zahra Afridi
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Corresponding author

Correspondence to Maqsood Mahmud .

Editor information

Editors and Affiliations

  1. School of Mathematics, Statistics and CS, University of KwaZulu-Natal, Pietermaritzburg, South Africa

    Nelishia Pillay

  2. Sch of IT, Dept of CS, University of Pretoria, Pretoria, South Africa

    Andries P. Engelbrecht

  3. Sci N/w for Innova and Research Exc, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  4. Department of Computing Sciences,, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa

    Mathys C. du Plessis

  5. Faculty of Electrical Engineering and, Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Václav Snášel

  6. Fakulti Teknologi Maklumat dan Komunikas, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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© 2016 Springer International Publishing Switzerland

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Mahmud, M., Pathak, P., Pathak, V., Afridi, Z. (2016). Detection of Criminally Convicted Email Users by Behavioral Dissimilarity. In: Pillay, N., Engelbrecht, A., Abraham, A., du Plessis, M., Snášel, V., Muda, A. (eds) Advances in Nature and Biologically Inspired Computing. Advances in Intelligent Systems and Computing, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-319-27400-3_38

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  • DOI: https://doi.org/10.1007/978-3-319-27400-3_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27399-0

  • Online ISBN: 978-3-319-27400-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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