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Identification of User Ownership in Digital Forensic using Data Mining Technique

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 50 - Number 4
Year of Publication: 2012
Authors:
Kailash Kumar
Sanjeev Sofat
Naveen Aggarwal
S. K. Jain
10.5120/7756-0818

Kailash Kumar, Sanjeev Sofat, Naveen Aggarwal and S.k.jain. Article: Identification of User Ownership in Digital Forensic using Data Mining Technique. International Journal of Computer Applications 50(4):1-5, July 2012. Full text available. BibTeX

@article{key:article,
	author = {Kailash Kumar and Sanjeev Sofat and Naveen Aggarwal and S.k.jain},
	title = {Article: Identification of User Ownership in Digital Forensic using Data Mining Technique},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {50},
	number = {4},
	pages = {1-5},
	month = {July},
	note = {Full text available}
}

Abstract

As existing technology used by criminal rapidly changes and growing, digital forensics is also growing and important ?elds of research for current intelligence, law enforcement and military organizations today. As huge information is stored in digital form, the need and ability to analyze and process this information for relevant evidence has grown in complexity. During criminal activities crime committed use digital devices, forensic examiners have to adopt practical frameworks and methods to recover data for analysis which can comprise as evidence. Data Preparation/ Generation, Data warehousing and Data Mining, are the three essential features involved in the investigation process. The purpose of data mining technique is to find the valuable relationships between data items. This paper proposes an approach for preparation, generation, storing and analyzing of data, retrieved from digital devices which pose as evidence in forensic analysis. Attribute classification model has been presented to categorized user files. The data mining tools has been used to identify user ownership and validating the reliability of the pre-processed data. This work proposes a practical framework for digital forensics on hard drives.

References

  • Agrawal, R. , Imielinski, T. & Swami 1993 A. Mining association rules between sets of items in large databases. Proceedings of the ACM SIGMOD International Conference on Management of Data, 207 – 216.
  • Ankit Agarwal, Megha Gupta, Saurabh Gupta, S. C. Gupta 2011. Systematic Digital Forensic Investigation Model. IJCSS, Volume 5, Issue 1.
  • Brian D. Carrier, Eugene H. Spafford 2005. Automated Digital Evidence Target Definition Using Outlier Analysis and Existing Evidence, Digital Forensic Research Workshop (DFRWS).
  • Brown, Ross A. , Pham, Binh L. , & De Vel, Olivier Y. 2003. A Grammar for the Specification of Forensic Image Mining Searches. In Lovell, Brian, Campbell, Duncan, & Fookes, Clinton (Eds. ) Eighth Australian and New Zealand Intelligent Information Systems Conference, December, Sydney, Australia.
  • Brown, Ross A. & Pham, Binh L. 2005. Image Mining and Retrieval Using Hierarchical Support Vector Machines. In Chen, Yi-Ping (Ed. ) 11th International Conference on Multi-Media Modeling, Jan, Melbourne, Australia 1550-5502, IEEE.
  • Carney, M. and Rogers, M. 2004. The Trojan Made Me Do It: A First Step in Statistical Based Computer Forensics Event Reconstruction. International Journal of Digital Evidence, 2(4). 1-11.
  • Chen, Y. , J. R. Miller, J. A. Francis, G. L. Russell, and F. Aires 2003. Observed and modeled relationships among Arctic climate variables. J. Geophys. Volume. 108.
  • Corney, M. , de Vel, O. , Anderson, A. , and Mohay, G. 2002. Gender preferential Text Mining of E-mail Discourse, The 18th annual Computer Security Applications Conference (ACSAC2002).
  • Data Mining Concepts and Techniques, 2ed by Jiawei Han, Kamber M Morgan 2005. Kaufmann Publishers.
  • De Vel, O. , Corney, M. and Mohay, G. 2001. Mining E-Mail Content for Author Identification Forensics, SIGM OD Record, ACM Press, Volume 30, Issue 4, 55–64.
  • DFRWS. 2001. A road map for digital forensic research. DTR - T001-01 FINAL - DFRWS Technical Report, 1(1).
  • Fayyad, U. , G. Piatetsky-Shapiro and P. Smyth, 1996. Advances in Knowledge Discovery and Data Mining, MIT Press, ISBN-10: 0262560976,560.
  • F. Pernkopf 2004. Detection of Surface Defects on Raw Steel Blocks Using Bayesian Network Classifiers. Pattern Analysis and Applications, Vol. 7, No. 3, 333–342.
  • Guidance Software Inc. Encase Forensics. http://www. guidancesoftware. com.
  • Padhraic Smyth, David Hand, Mannila Heikki 2001. Principles of Data Mining. The MIT Press.
  • Joachims T. 2002. Optimizing search engines using click through data. In Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD).
  • Veena H Bhat, Member, IAENG, Prasanth G Rao, Abhilash V. R. , P. Deepa Shenoy, Venugopal K. R. and L. M. Patnaik 2010. A Data Mining Approach for Data Generation and Analysis for Digital Forensic Application IACSIT, Vol. 2, No. 3, ISSN: 1793-8236.