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Anti-virus systems traditionally use signatures to detect malicious executables, but signatures are over fitted features that are of little use in machine learning. Other methods seek to utilize more general features, with some degree of success. Through this project, the authors presented a data mining approach that conducts an exhaustive feature search on a set of computer viruses. Data mining methods detect patterns in large amounts of data, and use these patterns to detect future instances in similar data. They can also use classifiers to detect malicious executables. A classifier is a rule set, or detection model, generated by the data mining approach that was trained over a given set of training data.
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