Machine-independent audit trail analysis – a decision support tool for continuous audit assurance

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-user computer 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.

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