title: Historical feature pattern extraction based network attack situation sensing algorithm.
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.
authors.affiliation: Department of Industrial Engineering, Tsinghua University, Beijing 100084, China ; Bengbu Automobile NCO Academy, Bengbu 233011, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Zeng
authors.affiliation: Department of Industrial Engineering, Tsinghua University, Beijing 100084, China.
authors.forename: Dacheng
authors.initials: D
authors.lastname: Liu
authors.affiliation: Department of Industrial Engineering, Tsinghua University, Beijing 100084, China ; Tianjin Port (Group), Ltd., Tianjin 300456, China.
authors.forename: Zhou
authors.initials: Z
authors.lastname: Lei
mesh_terms: D000465:Algorithms; D016494:Computer Security; D011336:Probability