• DocumentCode
    2314002
  • Title

    A Novel Data Generation Approach for Digital Forensic Application in Data Mining

  • Author

    Bhat, Veena H. ; Rao, Prasanth G. ; Abhilash, R.V. ; Shenoy, P. Deepa ; Venugopal, K.R. ; Patnaik, L.M.

  • Author_Institution
    IT & Syst. Dept., IBS (ICFAI Bus. Sch.), Bangalore, India
  • fYear
    2010
  • fDate
    9-11 Feb. 2010
  • Firstpage
    86
  • Lastpage
    90
  • Abstract
    With the rapid advancements in information and communication technology in the world, crimes committed are also becoming technically intensive. When crimes committed use digital devices, forensic examiners have to adopt practical frameworks and methods for recovering data for analysis as evidence. Data Generation, Data Warehousing and Data Mining, are the three essential features involved in this process. This paper proposes a unique way of generating, storing and analyzing data, retrieved from digital devices which pose as evidence in forensic analysis. A statistical approach is used in validating the reliability of the pre-processed data. This work proposes a practical framework for digital forensics on flash drives.
  • Keywords
    computer forensics; data analysis; data mining; data warehouses; statistical analysis; Information and Communication Technology; data analysis; data generation approach; data mining; data recovery; data retrieval; data warehousing; digital forensic application; flash drives; statistical approach; Application software; Computer crime; Data analysis; Data mining; Digital forensics; Law; Legal factors; Machine learning; Personal digital assistants; Space technology; Digital forensic; data preprocessing; flash drive;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Computing (ICMLC), 2010 Second International Conference on
  • Conference_Location
    Bangalore
  • Print_ISBN
    978-1-4244-6006-9
  • Electronic_ISBN
    978-1-4244-6007-6
  • Type

    conf

  • DOI
    10.1109/ICMLC.2010.24
  • Filename
    5460764