LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Mitchell, Faye
Languages: English
Types: Article
Subjects:
Dr Faye Mitchell argues that the use of Artificial Intelligence, which is a well-established area of modern computer science that is capable of dealing with computationally large or complex problems, could be useful for digital forensics. Digital forensics is becoming increasingly important, and often requires the intelligent analysis of large amounts of complex data. Artificial Intelligence could help to bridge the gap.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 3 George F. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving (6th edition, 2009 Addison-Wesley); Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (3rd edition, 2010, Prentice Hall).
    • 4 W3C (2004) RDF Primer at http://www.w3.org/TR/rdf-primer/; W3C (2006) Extensible Markup Language (XML) 1.1 (Second Edition) at http://www.w3.org/TR/xml11/.
    • 5 Adam Farquhar, Richard Fikes and James Rice, 'The Ontolingua Server: a Tool for Collaborative Ontology Construction', International Journal of Human-Computer Studies, 1997, Volume 46, pp 707-727.
    • 6 Philip Turner, 'Unification of digital evidence from disparate sources (Digital Evidence Bags)', Digital Investigation (2005) 2(3), pp 223-228.
    • 7 D. A. Duce, F. R. Mitchell and P. Turner, 'Digital Forensics: Challenges and Opportunities', in John Haggerty and Madjid Merabti, (eds), ACSF 2007: Proceedings of the 2nd Conference on Advances in Computer Security and Forensics, (Liverpool John Moores University, School of Computing & Mathematical Sciences, 2007).
    • 8 The UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/) is an example of such a case repository, and is used by the Machine Learning community to test new algorithms.
    • 20 Bruno W. P. Hoelz, CĂ©lia G. Ralha and Rajiv Geeverghese, Artificial intelligence applied to computer forensics in Proceedings of the 2009 ACM symposium on Applied Computing, Honolulu, Hawaii, (ACM, 2009), pp 883-888.
    • 21 Simon L. Garfinkel, 'Forensic feature extraction and cross-drive analysis', Digital Investigation, 3S (2006), pp S71-81
    • 22 Clive Blackwell, 'Managing evidence with an expert system', 2nd Workshop of the AI in forensics SIG, Cybersecurity KTN (London 2009), available at http://www.ktn.qinetiqtim.net/resources.php?page=rs_ktnpublications.
    • 23 Philip Turner, 'Unification of digital evidence from disparate sources (Digital Evidence Bags)', Digital Investigation (2005) 2(3), pp 223-228.
    • 24 D. A. Duce, F. R. Mitchell and P. Turner, 'Digital Forensics: Challenges and Opportunities', in John Haggerty and Madjid Merabti, (eds.), ACSF 2007: Proceedings of the 2nd Conference on Advances in Computer Security and Forensics, (Liverpool John Moores University, School of Computing & Mathematical Sciences, 2007).
  • No related research data.
  • No similar publications.

Share - Bookmark

Download from

Cite this article