Forensics in Telecommunications, Information, and Multimedia. Third International ICST Conference, e-Forensics 2010, Shanghai, China, November 11-12, 2010, Revised Selected Papers

Research Article

Analysis of Telephone Call Detail Records Based on Fuzzy Decision Tree

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  • @INPROCEEDINGS{10.1007/978-3-642-23602-0_30,
        author={Liping Ding and Jian Gu and Yongji Wang and Jingzheng Wu},
        title={Analysis of Telephone Call Detail Records Based on Fuzzy Decision Tree},
        proceedings={Forensics in Telecommunications, Information, and Multimedia. Third International ICST Conference, e-Forensics 2010, Shanghai, China, November 11-12, 2010, Revised Selected Papers},
        proceedings_a={E-FORENSICS},
        year={2012},
        month={10},
        keywords={Forensics digital evidence telephone call records fuzzy decision tree},
        doi={10.1007/978-3-642-23602-0_30}
    }
    
  • Liping Ding
    Jian Gu
    Yongji Wang
    Jingzheng Wu
    Year: 2012
    Analysis of Telephone Call Detail Records Based on Fuzzy Decision Tree
    E-FORENSICS
    Springer
    DOI: 10.1007/978-3-642-23602-0_30
Liping Ding1, Jian Gu2, Yongji Wang1, Jingzheng Wu1
  • 1: Chinese Academy of Sciences
  • 2: The Third Research Institute of Ministry of Public Security

Abstract

Digital evidences can be obtained from computers and various kinds of digital devices, such as telephones, mp3/mp4 players, printers, cameras, etc. Telephone Call Detail Records (CDRs) are one important source of digital evidences that can identify suspects and their partners. Law enforcement authorities may intercept and record specific conversations with a court order and CDRs can be obtained from telephone service providers. However, the CDRs of a suspect for a period of time are often fairly large in volume. To obtain useful information and make appropriate decisions automatically from such large amount of CDRs become more and more difficult. Current analysis tools are designed to present only numerical results rather than help us make useful decisions. In this paper, an algorithm based on fuzzy decision tree (FDT) for analyzing CDRs is proposed. We conducted experimental evaluation to verify the proposed algorithm and the result is very promising.