使用机器学习技术检测Java编译的恶意软件

Gheorghe Balan, Adrian-Stefan Popescu
{"title":"使用机器学习技术检测Java编译的恶意软件","authors":"Gheorghe Balan, Adrian-Stefan Popescu","doi":"10.1109/SYNASC.2018.00073","DOIUrl":null,"url":null,"abstract":"Malicious software using Java Language in order to implement the attack evolved rapidly in the past years. Initially we were used to find malicious Applets and exploitation methods to escape the controlled environments and to gain access to victims. Nowadays, as a react to the security measurements implemented in browsers, it is common to distribute the malware through spear-phishing emails. This paper presents two methods to detect the Java malicious code. One method is using an unsupervised machine learning algorithm while the other is using the Perceptron algorithm in order to shape a detection model. Combining their capacities we obtained a very good solution to detect Java threats in a proactive manner and to make sure that the known malware variants are still detected. The detection is focused on the class files as a response to the Malware as a Service concept.","PeriodicalId":273805,"journal":{"name":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Detecting Java Compiled Malware using Machine Learning Techniques\",\"authors\":\"Gheorghe Balan, Adrian-Stefan Popescu\",\"doi\":\"10.1109/SYNASC.2018.00073\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Malicious software using Java Language in order to implement the attack evolved rapidly in the past years. Initially we were used to find malicious Applets and exploitation methods to escape the controlled environments and to gain access to victims. Nowadays, as a react to the security measurements implemented in browsers, it is common to distribute the malware through spear-phishing emails. This paper presents two methods to detect the Java malicious code. One method is using an unsupervised machine learning algorithm while the other is using the Perceptron algorithm in order to shape a detection model. Combining their capacities we obtained a very good solution to detect Java threats in a proactive manner and to make sure that the known malware variants are still detected. The detection is focused on the class files as a response to the Malware as a Service concept.\",\"PeriodicalId\":273805,\"journal\":{\"name\":\"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SYNASC.2018.00073\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SYNASC.2018.00073","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

利用Java语言来实现攻击的恶意软件近年来发展迅速。最初,我们被用来发现恶意小程序和利用方法,以逃离受控环境并获得受害者的访问权限。如今,作为对浏览器中实现的安全措施的反应,通过鱼叉式网络钓鱼电子邮件分发恶意软件是很常见的。本文提出了两种检测Java恶意代码的方法。一种方法是使用无监督机器学习算法,而另一种方法是使用感知器算法来形成检测模型。结合它们的能力,我们获得了一个非常好的解决方案,以主动的方式检测Java威胁,并确保仍然检测到已知的恶意软件变体。检测集中在类文件上,作为对恶意软件即服务概念的响应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting Java Compiled Malware using Machine Learning Techniques
Malicious software using Java Language in order to implement the attack evolved rapidly in the past years. Initially we were used to find malicious Applets and exploitation methods to escape the controlled environments and to gain access to victims. Nowadays, as a react to the security measurements implemented in browsers, it is common to distribute the malware through spear-phishing emails. This paper presents two methods to detect the Java malicious code. One method is using an unsupervised machine learning algorithm while the other is using the Perceptron algorithm in order to shape a detection model. Combining their capacities we obtained a very good solution to detect Java threats in a proactive manner and to make sure that the known malware variants are still detected. The detection is focused on the class files as a response to the Malware as a Service concept.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信