Negative Selection of Written Language Using Character Multiset Statistics  

作　　者：Matti Pll Timo Honkela 

机构地区：[1]Department of Information and Computer Science,School of Science and Technology,Aalto University

出　　处：《Journal of Computer Science & Technology》2010年第6期1256-1266,共11页计算机科学技术学报（英文版）

基　　金：funded by the Academy of Finland under Grant No.214144

摘　　要：We study the combination of symbol frequence analysis and negative selection for anomaly detection of discrete sequences where conventional negative selection algorithms are not practical due to data sparsity.Theoretical analysis on ergodic Markov chains is used to outline the properties of the presented anomaly detection algorithm and to predict the probability of successful detection.Simulations are used to evaluate the detection sensitivity and the resolution of the analysis on both generated artificial data and real-world language data including the English Wikipedia.Simulation results on large reference corpora are used to study the effects of the assumptions made in the theoretical model in comparison to real-world data.We study the combination of symbol frequence analysis and negative selection for anomaly detection of discrete sequences where conventional negative selection algorithms are not practical due to data sparsity.Theoretical analysis on ergodic Markov chains is used to outline the properties of the presented anomaly detection algorithm and to predict the probability of successful detection.Simulations are used to evaluate the detection sensitivity and the resolution of the analysis on both generated artificial data and real-world language data including the English Wikipedia.Simulation results on large reference corpora are used to study the effects of the assumptions made in the theoretical model in comparison to real-world data.

关 键 词：negative selection anomaly detection frequency analysis 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]