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Digital Forensics to Intelligent Forensics

Author

Listed:
  • Alastair Irons

    (The University of Sunderland, David Goldman Informatics Centre, St Peters Campus, Sunderland SR6 0DD, UK)

  • Harjinder Singh Lallie

    (University of Warwick (WMG), Coventry CV4 7AL, UK)

Abstract

In this paper we posit that current investigative techniques—particularly as deployed by law enforcement, are becoming unsuitable for most types of crime investigation. The growth in cybercrime and the complexities of the types of the cybercrime coupled with the limitations in time and resources, both computational and human, in addressing cybercrime put an increasing strain on the ability of digital investigators to apply the processes of digital forensics and digital investigations to obtain timely results. In order to combat the problems, there is a need to enhance the use of the resources available and move beyond the capabilities and constraints of the forensic tools that are in current use. We argue that more intelligent techniques are necessary and should be used proactively. The paper makes the case for the need for such tools and techniques, and investigates and discusses the opportunities afforded by applying principles and procedures of artificial intelligence to digital forensics intelligence and to intelligent forensics and suggests that by applying new techniques to digital investigations there is the opportunity to address the challenges of the larger and more complex domains in which cybercrimes are taking place.

Suggested Citation

  • Alastair Irons & Harjinder Singh Lallie, 2014. "Digital Forensics to Intelligent Forensics," Future Internet, MDPI, vol. 6(3), pages 1-13, September.
    • Handle: RePEc:gam:jftint:v:6:y:2014:i:3:p:584-596:d:40179
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    References listed on IDEAS

    as
    1. Jana Diesner & Terrill L. Frantz & Kathleen M. Carley, 2005. "Communication Networks from the Enron Email Corpus “It's Always About the People. Enron is no Different”," Computational and Mathematical Organization Theory, Springer, vol. 11(3), pages 201-228, October.
    2. P. S. Keila & D. B. Skillicorn, 2005. "Structure in the Enron Email Dataset," Computational and Mathematical Organization Theory, Springer, vol. 11(3), pages 183-199, October.
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