Data Mining Usage in Corporate Information Security: Intrusion Detection Applications
Affiliation:
1. Embassy of the Kingdom of Saudi Arabia in Bosnia and Herzegovina, Sarajevo , Bosnia and Herzegovina
Abstract
Abstract
Background: The globalization era has brought with it the development of high technology, and therefore new methods of preserving and storing data. New data storing techniques ensure data are stored for longer periods of time, more efficiently and with a higher quality, but also with a higher data abuse risk. Objective: The goal of the paper is to provide a review of the data mining applications for the purpose of corporate information security, and intrusion detection in particular. Methods/approach: The review was conducted using the systematic analysis of the previously published papers on the usage of data mining in the field of corporate information security. Results: This paper demonstrates that the use of data mining applications is extremely useful and has a great importance for establishing corporate information security. Data mining applications are directly related to issues of intrusion detection and privacy protection. Conclusions: The most important fact that can be specified based on this study is that corporations can establish a sustainable and efficient data mining system that will ensure privacy and successful protection against unwanted intrusions.
Publisher
Walter de Gruyter GmbH
Subject
Management of Technology and Innovation,Economics, Econometrics and Finance (miscellaneous),Information Systems,Management Information Systems
Reference31 articles.
1. 1. Baesens, B., Mues, C., Martens, D., Vanthienen, J. (2009), “50 years of data mining and OR: upcoming trends and challenges“, Journal of the Operational Research Society, Vol. 60, pp. S16-S23. 2. 2. Bose, R. (2006), “Intelligent technologies for managing fraud and identity theft“, in Third International Conference on Information Technology: New Generations, (ITNG 2006), IEEE, pp. 446-451. 3. 3. Chen, W. H., Hsu, S. H., Shen, H. P. (2005), “Application of SVM and ANN for intrusion detection“, Computors & Operations Research, Vol. 32 No. 10, pp. 2617-2634. 4. 4. Chen, Y., Abraham, A., Yang, B. (2007), “Hybrid flexible neural‐tree‐based intrusion detection systems”, International Journal of Intelligent Systems, Vol. 22 No. 4, pp. 337-352. 5. 5. Dlamini, M. T., Eloff, J. H., Eloff, M. M. (2009), “Information security: The moving target“, Computers & Security, Vol. 28 No. 3-4, pp. 189-198.
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|