摘要
With the rapid development of Internet of Things(IoT)technologies,the detection and analysis of malware have become a matter of concern in the industrial application of Cyber-Physical System(CPS)that provides various services using the IoT paradigm.Currently,many advanced machine learning methods such as deep learning are popular in the research of malware detection and analysis,and some achievements have been made so far.However,there are also some problems.For example,considering the noise and outliers in the existing datasets of malware,some methods are not robust enough.Therefore,the accuracy of malware classification still needs to be improved.Aiming at this issue,we propose a novel method that combines the correntropy and the deep learning model.In our proposed method for malware detection and analysis,given the success of the mixture correntropy as an effective similarity measure in addressing complex datasets with noise,it is therefore incorporated into a popular deep learning model,i.e.,Convolutional Neural Network(CNN),to reconstruct its loss function,with the purpose of further detecting the features of outliers.We present the detailed design process of our method.Furthermore,the proposed method is tested both on a real-world malware dataset and a popular benchmark dataset to verify its learning performance.
基金
supported in part by the National Natural Science Foundation of China under Grants U1836106 and 81961138010
in part by the Beijing Natural Science Foundation under Grants M21032 and 19L2029
in part by the Beijing Intelligent Logistics System Collaborative Innovation Center under Grant BILSCIC-2019KF-08
in part by the Scientific and Technological Innovation Foundation of Foshan underGrants BK20BF010 and BK21BF001
in part by the Scientific and Technological Innovation Foundation of Shunde Graduate School,USTB,under Grant BK19BF006,USTB,under Grants BK20BF010 and BK19BF006
in part by the Fundamental Research Funds for the University of Science and Technology Beijing under Grant FRF-BD-19-012A.
