Top > JACIII > A Threat Assessment Method Based on Hierarchies and Modules

single-jc.php

JACIII Vol.18 No.1 pp. 93-99
doi: 10.20965/jaciii.2014.p0093
(2014)

Paper:

Views over last 60 days: 1,044

A Threat Assessment Method Based on Hierarchies and Modules

Fang Deng, Xinan Liu, Zhihong Peng,
and Jie Chen

School of Automation, Beijing Institute of Technology, Beijing, 100081, China

Received:
May 22, 2013
Accepted:
December 10, 2013
Published:
January 20, 2014
Creative Commons License
Keywords:
threat assessment, membership cloud, particle swarm, dynamic bayesian network
Abstract
With the development of low-level data fusion technology, threat assessment, which is a part of high-level data fusion, is recognized by an increasing numbers of people. However, the method to solve the problem of threat assessment for various kinds of targets and attacks is unknown. Hence, a threat assessment method is proposed in this paper to solve this problem. This method includes tertiary assessments: information classification, reorganization, and summary. In the tertiary assessments model, various threats with multi-class targets and attacks can be comprehensively assessed. A case study with specific algorithms and scenarios is shown to prove the validity and rationality of this method.
Cite this article as:
F. Deng, X. Liu, Z. Peng, and J. Chen, “A Threat Assessment Method Based on Hierarchies and Modules,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.1, pp. 93-99, 2014.
Data files:
References
  1. [1] F. White, “A model for data fusion [C],” Proc. of the First National Symp. on Sensor Fusion, Vol.12, pp. 143-158, 1988.
  2. [2] A. Steinberg, C. Bowman, F.Whitef, “Revisions to the JDL data fusion model [C],” Joint NATO/IRIS Conf. Proc., pp. 430-441, 1998.
  3. [3] D. Hall and J. Llinas, “Handbook of multisensor data fusion [M],” London: CRC Press, 2001.
  4. [4] E. Little and G. Rogova, “Theoretical foundations and proposed applications of threat ontology to information fusion [R],” CUBRC, 2004.
  5. [5] Alan N. Steinberg, “An approach to threat assessment [J],” the 7th Inf. Conf. on Information Fusion (FUSION), Buffalo, NY. USA, 2005.
  6. [6] Li Deyi and Shi Xuemei, “The atomic model of language and likelihood inference [J],” Proc. of computer intelligent interface and intelligent applications, pp. 272-277, 1993.
  7. [7] Han Guozhu, Fan Ningjun, Cheng Li, and Zhang Jianyu, “Performance evaluation of neutralizing weapon system based on cloud theory,” Computer Measurement & Control, Vol.2, pp. 338-340, 2011.
  8. [8] Liu Ji, Deng Guishi, and Na Risa, “Analysis and design of Bayesian feedback cloud model,” Systems Engineering-Theory & Practice, Vol.28, No.7, pp. 138-143, 2008.
  9. [9] Ma Shidong, Han Liang, Gong Guanghong et al., “Target threat assessment technology based on cloud model,” J. of Beijing University of Aeronautics and Astronautics, Vol.36, No.2, pp. 150-153, 2010.
  10. [10] Li Deyi, Meng Haijun, and Shi Xuemei, “Membership Clouds and Membership Cloud Generators,” Comber R & D, Vol.32, No.6, pp. 16-21, 1995.
  11. [11] Li Deyi and Du Ci, “Artificial Intelligence of Uncertainty,” National Defense Industry Press, Beijing, China, 2005.
  12. [12] Li Deren, Wang Shuliang, and Li Deyi, “Spatial Data Mining Theory and Application,” Science Press, Beijing, China, 2006.
  13. [13] L. Timo, R. Janne, and Tommi Laitinen, “Joint threat assessment with asset profiling and entity bayes net,” Int. Conference on Information Fusion, Seattle, WA, USA, pp. 6-9, July 2009.
  14. [14] Shi Jianguo and Gao Xiaoguang, “Programming algorithm for reference of discrete dynamic Bayesian network,” J. of Computer Applications, Vol.32, No.7, pp. 1943-1946, 2012.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 22, 2024