The use of self-organising maps for anomalous behaviour detection in a digital investigation

• Published in: Forensic science international Vol. 162; no. 1; pp. 33 - 37
• Main Authors: Fei, B.K.L., Eloff, J.H.P., Olivier, M.S., Venter, H.S.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Kidlington Elsevier Ireland Ltd 16.10.2006; Elsevier; The Lancet Publishing Group, a division of Elsevier Science Ltd; Elsevier Limited
• Subjects: Analysis; Anomalous behaviours; Behavior; Biological and medical sciences; Computers; Crime; Criminal investigations; Data mining; Digital forensics; Digital investigation; Digital mapping; Evidence; Evidence, Demonstrative; Forensic medicine; Forensic sciences; Forensic Sciences - methods; General aspects; Human acts; Human behavior; Humans; Internet; Internet access; Investigative techniques, diagnostic techniques (general aspects); Medical sciences; Neural networks; Neural Networks (Computer); Public health. Hygiene; Public health. Hygiene-occupational medicine; Self-organising map; Visualisation; Workplace; Visualisation; Digital investigation; Self-organising map; Anomalous behaviours; Digital forensics; Genetic mapping; Cartography; Forensic science; Legal medicine; Use; Forensic aspect; Self; Behavior; Diagnosis; Detection
• ISSN: 0379-0738; 1872-6283
• DOI: 10.1016/j.forsciint.2006.06.046

The dramatic increase in crime relating to the Internet and computers has caused a growing need for digital forensics. Digital forensic tools have been developed to assist investigators in conducting a proper investigation into digital crimes. In general, the bulk of the digital forensic tools available on the market permit investigators to analyse data that has been gathered from a computer system. However, current state-of-the-art digital forensic tools simply cannot handle large volumes of data in an efficient manner. With the advent of the Internet, many employees have been given access to new and more interesting possibilities via their desktop. Consequently, excessive Internet usage for non-job purposes and even blatant misuse of the Internet have become a problem in many organisations. Since storage media are steadily growing in size, the process of analysing multiple computer systems during a digital investigation can easily consume an enormous amount of time. Identifying a single suspicious computer from a set of candidates can therefore reduce human processing time and monetary costs involved in gathering evidence. The focus of this paper is to demonstrate how, in a digital investigation, digital forensic tools and the self-organising map (SOM) – an unsupervised neural network model – can aid investigators to determine anomalous behaviours (or activities) among employees (or computer systems) in a far more efficient manner. By analysing the different SOMs (one for each computer system), anomalous behaviours are identified and investigators are assisted to conduct the analysis more efficiently. The paper will demonstrate how the easy visualisation of the SOM enhances the ability of the investigators to interpret and explore the data generated by digital forensic tools so as to determine anomalous behaviours.