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This paper proposes a novel active defense strategy focuses on users' behavior patterns which to classify the behaviors accurately by SVM for virus detecting. Differential evolution was introduced to improve the precision of SVM and turns it into an optimization problem which object is the classification precision. And the parameters are regarded as the variables to be optimized. The experimental results show that the proposed model has a higher precision than the compared methods, such as BPNN, SVM, GA-SVM, etc. In addition, the method is more efficient so that, it can be quickly updated and applied.