Towards improving detection performance for malware with a correntropy-based deep learning method

作     者：Xiong Luo Jianyuan Li Weiping Wang Yang Gao Wenbing Zhao Xiong Luo;Jianyuan Li;Weiping Wang;Yang Gao;Wenbing Zhao

作者机构：School of Computer and Communication EngineeringUniversity of Science and Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials ScienceBeijing100083China Beijing Intelligent Logistics System Collaborative Innovation CenterBeijing101149China China Information Technology Security Evaluation CenterBeijing100085China Department of Electrical Engineering and Computer ScienceCleveland State UniversityClevelandOH44115USA 

出 版 物：《Digital Communications and Networks》 (数字通信与网络（英文版）)

年 卷 期：2021年第7卷第4期

页      面：570-579页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 0839[工学-网络空间安全] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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 

主　　题：Malware detection Mixture correntropy Deep learning Convolutional neural network(CNN) 

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 ***,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 ***,there are also some *** example,considering the noise and outliers in the existing datasets of malware,some methods are not robust ***,the accuracy of malware classification still needs to be *** at this issue,we propose a novel method that combines the correntropy and the deep learning *** 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 *** present the detailed design process of our ***,the proposed method is tested both on a real-world malware dataset and a popular benchmark dataset to verify its learning performance.