A multi-objective sequential three-way decision approach for real-time malware detection

In order to solve the problem that traditional two-way decision based malicious code detection methods fail to consider the influence of decision making under the condition of insufficient information when facing complex and massive data in dynamic environment, this paper proposes a malicious code detection model based on sequential three-way decision. This model introduces sequential three-way decision into the domain of malicious code to avoid the risk of possible error detection due to insufficient information. In order to improve the overall performance of the detection model and eliminate the subjectivity of threshold selection, this paper designs a multi-objective sequential three-way decision model based on the above model, while considering the decision efficiency and decision accuracy of the model. In addition, the multi-objective optimization algorithm is used to solve the model. The simulation results show that the model not only guarantees the detection performance, but also improves the decision efficiency effectively. The real dynamic detection environment is better fitted.