CSE 5800 Mining/Learning and the Internet—HW2Due 6:30pm, Wed, Oct 5Submit Server: course=cse5800 , project=hw2Implement and evaluate LERAD (LEarning Rules for Anomaly Detection). Do not generate “wildcard rules” inStep 1 (in the paper) since they get relatively high scores in small data sets.1. Allow parameters:(a) number of pairs of examples for generating candidate rules (Lin the paper)(b) maximum number of rules per pair of examples (Min the paper)(c) number of examples in the sample set (|S|in the paper)(d) number of examples in the validation set as a percentage of the entire training set [e.g. 10% means 90%for training, 10% for validation]2. Vary the score threshold, report AUC (area under curve) upto 1%, 10%, and 100% false alarm rate.3. Three data sets:(a) toy data set on the course web site(b) intrusion detection on the course web site(c) your own data set4. A report (in pdf) that discusses the following:(a) Sensitivity analysis of parameters: for the second data set,i. vary each of the four parameters (keeping the other three constant),
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