[1]CUI Zhihua,LAN Zhuoxuan,ZHANG Jingbo,et al.Malicious code detection model based on high-dimensional multi-objective sequential three-way decision[J].CAAI Transactions on Intelligent Systems,2024,19(1):97-105.[doi:10.11992/tis.202306013]
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Malicious code detection model based on high-dimensional multi-objective sequential three-way decision

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 1 Page number: 97-105 Column: 学术论文—机器感知与模式识别 Public date: 2024-01-05
Title:
Malicious code detection model based on high-dimensional multi-objective sequential three-way decision
Author(s):
CUI Zhihua1;  LAN Zhuoxuan1;  ZHANG Jingbo1;  ZHANG Wensheng2
1. Shanxi Key Laboratory of Big Data Analysis and Parallel Computing, Taiyuan University of Science and Technology, Taiyuan 030024, China;
2. Institute of Automation, Chinese Academy of Sciences, Beijing 100089, China
Keywords:
malware detection; sequential three-way decision; convolutional neural network; high-dimensional multi-objective optimization; NSGA-III; multi-granularity; delay decision; decision threshold
CLC:
TP309
DOI:
10.11992/tis.202306013
Abstract:
In view of the problem that traditional two-way decision based malicious code detection methods fail to consider 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.
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