Surveying Different Aspects of Anomaly Detection and Its Applications
-
2889
Downloads
-
3654
Views
Authors
Neda Noori
- Islamic Azad University, Zanjan branch, Zanjan, Iran
Leila Boti
- Islamic Azad University, Zanjan branch, Zanjan, Iran
Ebrahim Nowzarpoor Shami
- Islamic Azad University, Zanjan branch, Zanjan, Iran
Abstract
Detecting anomalies is a significant issue which is being investigated in different levels of research and application. Many techniques of anomaly detection are widely and specifically developed for special applied domains while other techniques are mostly general. The purpose of this article is to provide a concise and comprehensive summary of surveys and researches related to anomaly detection. We have categorized the available techniques based on the certified methods; and to distinguish between normal and abnormal behaviors, we have defined some key hypotheses which are used by techniques. When a given technique turns out to be efficient for a specific domain, its hypotheses can provide strategies for accessing technique-efficiency in that domain. For each category, we have provided a basic technique for anomaly detection, and then we have shown how different basic techniques which are available in any category are distinguished from the defined basic technique. This procedure gives us a brief and simple understanding of the techniques which belong to any category. In addition, we will define the pros and cons of the techniques of each category. We hope that this article provides a better understanding of different aspects of the investigated issues, and that how the techniques which are developed in one area can turn out to be efficient in other areas which have not been part of the presuppositions at first.
Share and Cite
ISRP Style
Neda Noori, Leila Boti, Ebrahim Nowzarpoor Shami, Surveying Different Aspects of Anomaly Detection and Its Applications, Journal of Mathematics and Computer Science, 4 (2012), no. 2, 129--138
AMA Style
Noori Neda, Boti Leila, Nowzarpoor Shami Ebrahim, Surveying Different Aspects of Anomaly Detection and Its Applications. J Math Comput SCI-JM. (2012); 4(2):129--138
Chicago/Turabian Style
Noori, Neda, Boti, Leila, Nowzarpoor Shami, Ebrahim. "Surveying Different Aspects of Anomaly Detection and Its Applications." Journal of Mathematics and Computer Science, 4, no. 2 (2012): 129--138
Keywords
- Anomaly detection
- Outlier detection.
MSC
References
-
[1]
V. Kumar, Parallel and distributed computing for cybersecurity, Distributed Systems Online, Vol. 6, 9 pages, (2005)
-
[2]
C. Spence, L. Parra, P. Sajda, Detection, synthesis and compression in mammographic image analysis with a hierarchical image probability model. In Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, 3--10, (2001)
-
[3]
E. Aleskerov, B. Freisleben, B. Rao, Cardwatch: A neural network based database mining system for credit card fraud detection, Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr), 1997 (1997), 220--226
-
[4]
R. Fujimaki, T. Yairi, K. Machida, An approach to spacecraft anomaly detection problem using kernel feature space. In Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, 2005 (2005), 401--410
-
[5]
H. Teng, K. Chen, S. Lu, Adaptive real-time anomaly detection using inductively generated sequential patterns, Proceedings. 1990 IEEE Computer Society Symposium on Research in Security and Privacy, 1990 (1990), 278--284
-
[6]
P. J. Rousseeuw, A. M. Leroy, Robust regression and outlier detection, John Wiley & Sons, New York (1987)
-
[7]
X. Song, M. Wu, C. Jermaine, S. Ranka, Conditional anomaly detection, IEEE Transactions on Knowledge and Data Engineering, 19 (2007), 631--645
-
[8]
J. Theiler, D. M. Cai, Resampling approach for anomaly detection in multispectral images, In: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, 2003 (2003), 230--240
-
[9]
V. V. Phoha, The Springer Internet Security Dictionary, Springer-Verlag, New York (2002)
-
[10]
W.-K. Wong, A. Moore, G. Cooper, M. Wagner, Bayesian network anomaly pattern detection for disease outbreaks, Proceedings of the 20th International Conference on Machine Learning, 2003 (2003), 808--815