Abstract
This paper describes a method, by means of an example, which may be utilized to identify risk patterns in an audit log file. A prototype for auditing is develop by looking into several techniques, such as Artificial Intelligence (AI), Expert Systems (ES), Compression, Visualisation and Neural Networks (NN). The MASS (Model for an Auditing Security System) model described in this paper consists out of three components — an Expert System, a Neural Network and the Visualisation of the output. MASS is demonstrated by means of an example which uses an online sales system audit log file.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-0-387-35515-3_53
Chapter PDF
Similar content being viewed by others
Key words
References
Ye,LR, The Value of Explanation in Expert Systems for Auditing: An experimental Investigation, Expert Systems with Applications, Vol. 9, No. 4, pp. 543–556, 1995.
Abdolmohammadi,M.,J., Identification of tasks for Expert Systems development in Auditing, Expert Systems with Applications, Vol. 3, pp. 99–107, 1991
Chiu, Chi-Tien, Scott, Robert, An intelligent Forecasting Support Systems in Auditing: Expert System and Neural Network approach, Proceedings of the twenty-seventh annual Hawaii International Conference on Systems Sciences
Faussett, Laurene, Fundamentals of Neural Networks, Architectures, Algorithms, and Applications, 1994
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 IFIP International Federation for Information Processing
About this paper
Cite this paper
Liebenberg, A., Eloff, J.H.P. (2000). MASS. In: Qing, S., Eloff, J.H.P. (eds) Information Security for Global Information Infrastructures. SEC 2000. IFIP — The International Federation for Information Processing, vol 47. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35515-3_15
Download citation
DOI: https://doi.org/10.1007/978-0-387-35515-3_15
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4757-5479-7
Online ISBN: 978-0-387-35515-3
eBook Packages: Springer Book Archive