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The Scientific World Journal
Volume 2014 (2014), Article ID 473504, 8 pages
http://dx.doi.org/10.1155/2014/473504
Research Article

Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm

1Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
2Bengbu Automobile NCO Academy, Bengbu 233011, China
3Tianjin Port (Group), Ltd., Tianjin 300456, China

Received 20 February 2014; Accepted 18 March 2014; Published 27 April 2014

Academic Editors: Y. Mao and Z. Zhou

Copyright © 2014 Yong Zeng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously.