Machine-independent audit trail analysis - a tool for continuous audit assurance

Article


Best, Peter J.. 2004. "Machine-independent audit trail analysis - a tool for continuous audit assurance." Intelligent Systems in Accounting, Finance and Management. 12 (2), pp. 85-102. https://doi.org/10.1002/isaf.243
Article Title

Machine-independent audit trail analysis - a tool for continuous audit assurance

ERA Journal ID32090
Article CategoryArticle
Authors
AuthorBest, Peter J.
Journal TitleIntelligent Systems in Accounting, Finance and Management
Journal Citation12 (2), pp. 85-102
Number of Pages18
Year2004
Place of PublicationUnited Kingdom
ISSN1055-615X
1099-1174
1550-1949
2160-0074
Digital Object Identifier (DOI)https://doi.org/10.1002/isaf.243
Abstract

[Summary]: This paper reports the results of a research project which examines the feasibility of developing a machine-independent audit trail analyser (MIATA). MIATA is a knowledge based system which performs intelligent analysis of operating system audit trails. Such a system is proposed as a decision support tool for auditors when assessing the risk of unauthorised user activity in multi-usercomputer systems. It is also relevant to the provision of a continuous assurance service to clients by internal and external auditors. Monitoring user activity in system audit trails manually is impractical because of the vast quantity of events recorded in those audit trails. However, if done manually, an expert security auditor would be needed to look for 2 main types of events - user activity rejected by the system's security settings (failed actions) and user's behaving abnormally (e.g. unexpected changes in activity such as the purchasing clerk attempting to modify payroll data). A knowledge based system is suited to applications that require expertise to perform well-defined, yet complex, monitoring activities (e.g. controlling nuclear reactors and detecting intrusions in computer systems). To permit machine-independent intelligent audit trail analysis, an anomaly-detection approach is adopted. Time series forecasting methods are used to develop and maintain the user profile database (knowledge base) that allows identification of users with rejected behaviour as well as
abnormal behaviour. The knowledge based system maintains this knowledge base and permits reporting on the potential intruder threats (summarized in Table 1). The intelligence of the MIATA system is its ability to handle audit trails from any system, its knowledge base capturing rejected user activity and detecting anomalous activity, and its reporting capabilities focusing on known methods of intrusion. MIATA also updates user profiles and forecasts of behaviour on a daily basis. As such, it also 'learns' from changes in user behaviour. The feasibility of generating machine-independent audit trail records, and the applicability of the anomaly-detection approach and time series forecasting methods are demonstrated using three case studies. These results support the proposal that developing a machine-independent audit trail analyser is feasible. Such a system will be an invaluable aid to an auditor in detecting potential computer intrusions and monitoring user activity.

Keywordsaudit trails, intrusion detection, continuous assurance
ANZSRC Field of Research 2020350102. Auditing and accountability
460908. Information systems organisation and management
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Byline AffiliationsQueensland University of Technology
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