[1]李雪妍,陈 伟,杜俊雄.物联网僵尸网络的恶意域名检测技术研究[J].计算机技术与发展,2019,29(08):113-118.[doi:10. 3969 / j. issn. 1673-629X. 2019. 08. 022]
　LI Xue-yan,CHEN Wei,DU Jun-xiong.Research on Malicious Domain Name Detection Technology in IoT Botnet[J].,2019,29(08):113-118.[doi:10. 3969 / j. issn. 1673-629X. 2019. 08. 022]
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物联网僵尸网络的恶意域名检测技术研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年08期
页码:
113-118
栏目:
安全与防范
出版日期:
2019-08-10

文章信息/Info

Title:
Research on Malicious Domain Name Detection Technology in IoT Botnet
文章编号:
1673-629X(2019)08-0113-06
作者:
李雪妍; 陈 伟; 杜俊雄
南京邮电大学 计算机学院,江苏 南京 210023
Author(s):
LI Xue-yan; CHEN Wei; DU Jun-xiong
School of Computer,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
关键词:
物联网; 僵尸网络; 恶意域名; 自动评分算法; 信誉特征
Keywords:
IoT; Botnet; malicious domain name; automatic scoring algorithm; reputation
分类号:
TP301
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 08. 022
摘要:
随着物联网智能设备的普及,所带来的社会安全隐患也越来越多。正如 2016 年爆发的 Mirai 恶意软件,它正是由物联网智能设备中漏洞的入侵和渗透形成的一个大型僵尸网络。 其变种内置的域名生成算法大大增强了自身的健壮性,极大程度上延长了其自身的生命周期。域名系统作为互联网重要资源,也带来了很大的安全威胁。文中分析研究了现有的恶意域名识别技术,并提出一种基于信誉评分体制的全新检测系统。选取了基于域名维度与 IP 维度的特征集,同时设计并实现了异常值自动评分算法,算法可以自动选择最可疑的恶意域名事件且无需已标记数据集。实验结果表明,将文中采用的自动评分技术与标准异常检测技术相比较,误报率低至 0.003%,该系统的准确率比标准检测技术平均提升 5 ~10 倍。
Abstract:
With the popularization of Internet of things devices,there exists more and more security risks. Like the Mirai malware outbreak in 2016,it is a large Botnet created by the intrusion and penetration of vulnerabilities in smart devices in the Internet of things. The Mirai variant built-in domain name generation algorithm greatly enhances its robustness and extends its life cycle. As an important resource of Internet,DNS (domain name system) also brings great security threat. We analyze the existing malicious domain name recognition technology,and propose a new detection system based on the credit rating system. The feature set based on domain name dimension and IP dimension is selected, and the outliers automatic scoring algorithm is designed and implemented, which can automatically select the most suspect malicious domain name events through unmarked datasets. The experiment shows that compared with the standard abnormal detection technology,the false alarm rate of the proposed automatic scoring technology is as low as 0.003%.The accuracy of the system is 5 ~10 times higher than that of the standard detection technology.

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更新日期/Last Update: 2019-08-10