In view of the problem that traditional two-way decision based malicious code detection methods fail to con-sider the impact of decision making under the condition of insufficient information when facing complex and massive data in a dynamic environment,this paper proposes a sequential three-way decision malware detection model based on convolutional neural network.Firstly,the features of sample data were extracted and multi-granularity feature sets were constructed through convolutional neural networks,and then the sequential three-way decision theory was introduced to detect malicious code.To improve the overall performance of the detection model and eliminate the subjectivity of threshold selection,a high-dimensional multi-objective sequential three-way decision model was built based on the above model,taking account of the comprehensive classification performance,decision efficiency and decision risk cost of the model.In addition,the high-dimensional multi-objective optimization algorithm was used to solve the model.The simulation results show that the model can not only guarantee the detection performance,but also effectively improve the decision efficiency and reduce the decision risk cost.It better fits the real dynamic detection environment.
维普
万方数据