A near-miss management system architecture for the forensic investigation of software failures.

Digital forensics has been proposed as a methodology for doing root-cause analysis of major software failures for quite a while. Despite this, similar software failures still occur repeatedly. A reason for this is the difficulty of obtaining detailed evidence of software failures. Acquiring such evidence can be challenging, as the relevant data may be lost or corrupt following a software system's crash. This paper proposes the use of near-miss analysis to improve on the collection of evidence for software failures. Near-miss analysis is an incident investigation technique that detects and subsequently analyses indicators of failures. The results of a near-miss analysis investigation are then used to detect an upcoming failure before the failure unfolds. The detection of these indicators - known as near misses - therefore provides an opportunity to proactively collect relevant data that can be used as digital evidence, pertaining to software failures. A Near Miss Management System (NMS) architecture for the forensic investigation of software failures is proposed. The viability of the proposed architecture is demonstrated through a prototype.

A fraud management system architecture for next-generation networks.

This paper proposes an original architecture for a fraud management system (FMS) for convergent. Next-generation networks (NGNs), which are based on the Internet protocol (IP). The architecture has the potential to satisfy the requirements of flexibility and application-independency for effective fraud detection in NGNs that cannot be met by traditional FMSs. The proposed architecture has a thorough four-stage detection process that analyses billing records in IP detail record (IPDR) format - an emerging IP-based billing standard - for signs of fraud. Its key feature is its usage of neural networks in the form of self-organising maps (SOMs) to help uncover unknown NGN fraud scenarios. A prototype was implemented to test the effectiveness of using a SOM for fraud detection and is also described in the paper.

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

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.