基于KNN分类算法的恶意软件检测  

Malware Detection Based on KNN Classification Algorithm

作　　者：赵飞 蔡东蛟[1] 姜其师 ZHAO Fei;CAI Dongjiao;JIANG Qishi(Fuzhou Vocational and Technical College,Fuzhou 350121,China;Fujian Haichuang Optoelectronic Technology Co.,Ltd.,Fuzhou 350108,China)

机构地区：[1]福州职业技术学院,福建福州350121 [2]福建海创光电技术有限公司,福建福州350108

出　　处：《数字通信世界》2023年第3期42-44,共3页Digital Communication World

基　　金：福建省教育厅中青年教师教育科研项目,基金编号JAT210821,课题名称:基于机器学习的恶意软件检测方法研究。

摘　　要：随着科技的发展,层出不穷的恶意软件对用户计算机系统的数据都构成了极大的威胁,如何准确、高效地检测出恶意软件是令人担忧的问题。几十年来,恶意软件检测已引起反恶意软件行业和研究人员的关注。面对日益复杂的恶意软件,需要新的防御技术来检测和打击新奇的攻击和威胁。人工智能、深度学习也为Windows恶意软件检测提供了新的技术。文章研究如何在现有的一些恶意软件检测方法的基础上,改进特征码的提取和检测模型算法,以提高恶意软件检测的准确度,保护用户计算机系统以及数据的安全性。With the development of science and technology,the endless emergence of malware poses a great threat to the host or the data on the host.How to accurately and efficiently detect malware has become a worrying problem.For decades,malware detection has attracted the attention of anti malware industry and researchers.The increasingly complex malware needs new defense technologies to detect and combat novel attacks and threats.Artificial intelligence and deep learning also provide new technologies for Windows malware detection.Based on some existing malware detection methods,this project plans to continuously improve the extraction of signatures and detection model algorithms to improve the accuracy of malware detection and protect the security of host and data.

关 键 词：Windows恶意软件检测 特征选择 最近邻分类 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]