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1 J S Sahambi, S N Tandon, and R K Bhatt, Quantitative analysis of errors due to power-line interference and base-line drift in detection of onsets and offsets in ECG using wavelets Med Biol Eng Comput, Vol 35, No 6, 1997, pp 747 751 2 C T Bailey, T Sapatinas, J K Powell, and J W Krzanowski, Signal detection in underwater sound using wavelets Journal of the American Statistical Association, Vol 93, No 1, 1998, pp 73 83 3 A Boggess, and F J Narcowich, The First Course in Wavelets with Fourier Analysis Upper Saddle River, NJ: Prentice Hall, 2001 4 I Daubechies, The Wavelet transform, time-frequency localization and signal analysis IEEE Transactions on Information Theory, Vol 36, No 5, 1990, pp 96 101 5 B Vidakovic, Statistical Modeling by Wavelets New York: John Wiley & Sons, Ltd, 1999 6 D K Lakshminarasimhan, Wavelet Based Cyber Attack Detection MS thesis, Arizona State University, 2005 7 Statistica, wwwstatsoftcom
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Signature recognition is a conventional methodology used by most intrusion detection systems in practical use This methodology takes the following steps: 1 Capture, represent and store signature patterns of attack data 2 Monitor data from a computer and network system to look for a match to some of attack signatures, and generate an alarm of an attack if a match is found Attack signatures can be captured manually by human analysts Attack signatures can also be learned automatically from computer and network data collected under attack and normal use conditions, using data mining techniques such as arti cial neural networks, support vector machines, decision trees, association rules, supervised clustering, and so on Part IV illustrates the application of two data mining techniques, supervised clustering and arti cial neural networks, to the automatic learning of attack signatures and the use of the discovered attack signatures to detect cyber attacks Although only attack signatures are needed to recognize attacks for cyber attack detection, both attack data and normal use data are required to allow data mining techniques to learn the distinction of attack data from normal use data As a result, signature patterns of both attacks and normal use activities are learned by data mining techniques to classify attacks and normal use activities Through the comparison of the signature recognition techniques with the attack-norm separation techniques described in s 16 and 17 in their detection performance, this part points out the shortcoming of the signature recognition methodology in lack of handling the mixed attack and norm data and capturing advanced data features which can help uncover subtle differences between attack data and normal use data
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Techniques for mining data to discover data patterns generally fall into two categories which deal with two types of data, respectively [1]: (1) data with predictor variables only, and (2) data with both predictor variables and a target variable For an object of interest, both a predictor variable and a target variable describe a given attribute of the object However, a target variable assigns the object into a special class or value which depends on the values of the predictor variables In other words, the values of the predictor variables are used to predict or classify the value of the target variable For cyber attack detection, for example, computer and network events may be represented as the predictor variables, and the target variable assigns computer and network events into one of two classes: attack and normal use Statistically, predictor variables are called independent variables, and the target variable is called the dependent variable Data used to learn or mine patterns is called training data, and data used to test the use of learned patterns for prediction or classi cation accuracy is called testing data Examples of techniques for mining data with predictor variables only are hierarchical clustering, self-organized maps, association rules, principal component/independent component analysis, factor analysis, anomaly detection such as statistical control charts, and Bayesian networks [1] There are also a variety of data mining techniques that deal with data with both predictor variables and the target variable, such as decision trees and Classi cation And Regression Tree (CART), arti cial neural networks, support vector machines, regression, latent variable modeling, time series modeling, and Bayesian networks [1] Learning the signature patterns of cyber attacks automatically from computer and network data requires both attack data and normal use data in contrast because normal use data is necessary to make sure that attack signature patterns do not appear in normal use data That is, training data used to learn attack signature patterns has both predictor variables and the target variable which indicates the class of a given data record: attack or normal use The next chapter describes the application of arti cial neural networks to learning and classifying computer and network data for cyber attack detection This chapter introduces a supervised clustering algorithm, called Clustering and Classi cation Algorithm Supervised (CCAS), which can be used for cyber attack detection by rst grouping data points in a training data set into clusters of data points with the same target class of either attack or normal use
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