By Marcus A. Maloof
"Machine studying and knowledge Mining for machine Security" offers an summary of the present country of analysis in desktop studying and knowledge mining because it applies to difficulties in laptop safeguard. This publication has a robust specialize in info processing and combines and extends effects from computing device defense.
The first a part of the e-book surveys the information resources, the educational and mining equipment, overview methodologies, and previous paintings proper for computing device safeguard. the second one a part of the booklet includes articles written by way of the head researchers operating during this sector. those articles offers with subject matters of host-based intrusion detection in the course of the research of audit trails, of command sequences and of process calls in addition to community intrusion detection throughout the research of TCP packets and the detection of malicious executables.
This ebook fills the nice desire for a e-book that collects and frames paintings on constructing and using tools from computer studying and knowledge mining to difficulties in laptop protection.
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Additional info for Machine Learning and Data Mining for Computer Security: Methods and Applications (Advanced Information and Knowledge Processing)
Example text
2 From Data to Examples Three important activities in computer security are prevention, detection, and recovery [1]. If taking a machine learning or data mining approach to computer security, then the first step is to identify a data source supporting our desired activity. Such data sources include keystroke dynamics, command sequences, audit trails, HTTP logs, packet headers, and malicious executables. For instance, one could improve preventive measures by mining logs to discover the most frequent type of attack.
Ultimately, we want to determine how well the induced model approximates the true model. Since we have only a finite set of examples, evaluation involves splitting the available examples into a training set and a testing set. Generally, we apply an algorithm to the examples in the training set and evaluate the resulting model using the examples in the test set. Since we know the labels of the examples in the test set, we can compute a variety of performance metrics. One such metric is simply the percentage of testing examples the model predicts correctly.
2). When evaluating algorithms, it is critically important to apply these operations only to the training examples and not to the testing examples. We must apply such operations after creating training and testing sets. For example, selecting the most relevant attributes before dividing a set of examples into training and testing sets will invariably bias the evaluation of the resulting model. The outcome of such an evaluation will probably be that the model’s accuracy is higher than is the case.