重庆大学学报2024，Vol.47Issue(6)：86-93,8.DOI:10.11835/j.issn.1000.582X.2024.06.009
基于神经网络平滑聚合机制的恶意代码增量训练及检测
Malware incremental training and detection method based on neural network smooth aggregation mechanism
摘要
Abstract
To ensure the timeliness of malware variant detection models,traditional machine (deep) learning-based detection methods integrate historical and incremental data and retrain to update detection models. However,this approach often suffers from low training efficiency. Therefore,this paper proposes an incremental learning method based on a neural network smooth aggregation mechanism for detecting malware variants,facilitating the smooth evolution of detection models. The method introduces a training scale factor to prevent the decrement of accuracy in the aggregated incremental model due to small training scales. Experimental results show that the proposed incremental learning method can improve training efficiency while maintaining the accuracy of the detection model compared to the re-training method.关键词
恶意代码变种检测/增量学习/神经网络/模型聚合Key words
malware variants detection/incremental learning/neural network/model aggregation分类
信息技术与安全科学引用本文复制引用
郭志民,陈岑,李暖暖,蔡军飞,张铮..基于神经网络平滑聚合机制的恶意代码增量训练及检测[J].重庆大学学报,2024,47(6):86-93,8.基金项目
国家电网有限公司总部科技项目资助(5700-202024193A-0-0-00).Supported by the Science and Technology Project of State Grid Corporation of China(5700-202024193A-0-0-00). （5700-202024193A-0-0-00）
