Advanced SearchSearch Tips
Data Fusion Algorithm based on Inference for Anomaly Detection in the Next-Generation Intrusion Detection
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Data Fusion Algorithm based on Inference for Anomaly Detection in the Next-Generation Intrusion Detection
Kim, Dong-Wook; Han, Myung-Mook;
  PDF(new window)
In this paper, we propose the algorithms of processing the uncertainty data using data fusion for the next generation intrusion detection. In the next generation intrusion detection, a lot of data are collected by many of network sensors to discover knowledge from generating information in cyber space. It is necessary the data fusion process to extract knowledge from collected sensors data. In this paper, we have proposed method to represent the uncertainty data, by classifying where is a confidence interval in interval of uncertainty data through feature analysis of different data using inference method with Dempster-Shafer Evidence Theory. In this paper, we have implemented a detection experiment that is classified by the confidence interval using IRIS plant Data Set for anomaly detection of uncertainty data. As a result, we found that it is possible to classify data by confidence interval.
Data fusion;Dempster-Shafer;Inferential Method;Anomaly Detection;
 Cited by
Intruder Detection System Based on Pyroelectric Infrared Sensor, Journal of Korean Institute of Intelligent Systems, 2016, 26, 5, 361  crossref(new windwow)
Bass, Tim. Intrusion detection systems and multisensor data fusion. Communications of the ACM 43.4 99-105 :(2000).

Barford, Paul, Somesh Jha, and Vinod Yegneswaran. Fusion and filtering in distributed intrusion detection systems. Proc. Allerton Conference on Communication, Control and Computing. 2004.

Klein, Lawrence A. Sensor and data fusion: a tool for information assessment and decision making. Vol. 324. Bellingham eWA WA: Spie Press, 2004.

Khaleghi, Bahador, et al. Multisensor data fusion: A review of the state-of-the-art. Information Fusion 14.1 pp. 28-44. 2013. crossref(new window)

Lalmas, Mounia. A formal model for data fusion. Flexible Query Answering Systems. Springer Berlin Heidelberg, 274-288. 2002.

Seo, Young Mi Jee, Hong Ke, Soontak Lee, Rainfall Frequency Analysis and Uncertainty Quantification Using Dempster-Shafer Theory, Korea Water Resources Association 2010 KWRA conference pp. 1390-1394, 2010

MLA Deng, Xinyang, and Yong Deng. Multisensor Information Fusion Based on Dempster-shafer Theory and Power Average Operator. Journal of Computational Information Systems 9.16 pp. 6417-6424. 2013

Castanedo, Federico. A review of data fusion techniques. The Scientific World Journal 2013 (2013).

Yuan, Ye, Shuyuan Shang, and Li Li. Network intrusion detection using DS evidence combination with generalized regression neural network."Journal of Computational Information Systems 7.5 (2011): 1802-1809.

Yu, Dong, and Deborah Frincke. Alert confidence fusion in intrusion detection systems with extended Dempster-Shafer theory. Proceedings of the 43rd annual Southeast regional conference-Volume 2. ACM, 2005.

Burroughs, Daniel J., Linda F. Wilson, and George V. Cybenko. Analysis of distributed intrusion detection systems using Bayesian methods. Performance, Computing, and Communications Conference, 2002. 21st IEEE International. IEEE, 2002.

Chen, Qi, and Uwe Aickelin. Anomaly Detection Using the Dempster-Shafer Method. DMIN. 2006.

Chen, Qi, et al. Data classification using the Dempster-Shafer method" Journal of Experimental & Theoretical Artificial Intelligence 26.4, 493-517. (2014) crossref(new window)