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Guide Data Warehousing and Data Mining Techniques for Cyber Security (Advances in Information Security)

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Darin England and Jon B.

Data mining

Sonnek and Jon B. Jon B. Weissman, Seonho Kim, and Darin England. Kumar and S. Seonho Kim and Jon B. Please report errors in award information by writing to: awardsearch nsf.

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How to Manage Your Award. Grant Policy Manual. Grant General Conditions. Applications Of Data Mining In Computer Security is designed to meet the needs of a professional audience composed of researchers and practitioners in industry and graduate level students in computer science.

data mining IEEE PAPER

Applications of Data Mining in Computer Security. Adaptive Model Generation. Email Authorship Attribution for Computer Forensics. Sushil Jajodia is Professor and Chairman of the Dept. The three key computational steps are the model-learning process, model evaluation, and use of the model. This division is clearest with classification of data. Model learning occurs when one algorithm is applied to data about which the group or class attribute is known in order to produce a classifier, or an algorithm learned from the data.

The classifier is then tested with an independent evaluation set that contains data with known attributes. If the model is sufficiently accurate, it can be used to classify data for which the target attribute is unknown. There are many types of data mining, typically divided by the kind of information attributes known and the type of knowledge sought from the data-mining model.

data mining IEEE PAPER 2016

Predictive modeling is used when the goal is to estimate the value of a particular target attribute and there exist sample training data for which values of that attribute are known. An example is classification, which takes a set of data already divided into predefined groups and searches for patterns in the data that differentiate those groups. These discovered patterns then can be used to classify other data where the right group designation for the target attribute is unknown though other attributes may be known.

For instance, a manufacturer could develop a predictive model that distinguishes parts that fail under extreme heat, extreme cold, or other conditions based on their manufacturing environment , and this model may then be used to determine appropriate applications for each part. Another technique employed in predictive modeling is regression analysis, which can be used when the target attribute is a numeric value and the goal is to predict that value for new data.

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Descriptive modeling, or clustering, also divides data into groups. With clustering, however, the proper groups are not known in advance; the patterns discovered by analyzing the data are used to determine the groups. For example, an advertiser could analyze a general population in order to classify potential customers into different clusters and then develop separate advertising campaigns targeted to each group.