Malicious Domain Names Detection Algorithm Based on N-Gram

Malicious domain name attacks have become a serious issue for Internet security. In this study, a malicious domain names detection algorithm based on N-Gram is proposed. The top 100,000 domain names in Alexa 2013 are used in the N-Gram method. Each domain name excluding the top-level domain is segme...

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Published in:	Journal of computer networks and communications Vol. 2019; no. 2019; pp. 1 - 9
Main Authors:	Zeng, Xiang-Yan, Bao, Guangbin, Chang, Zhaobin, Hong, Zhao
Format:	Journal Article
Language:	English
Published:	Cairo, Egypt Hindawi Publishing Corporation 01.01.2019 Hindawi John Wiley & Sons, Inc Wiley
Subjects:	Algorithms Behavior Blacklisting Computer networks Computer simulation Cybersecurity Domain names International conferences Internet Malware Mathematical analysis Semantics Servers Trademarks URLs Weight
ISSN:	2090-7141, 2090-715X
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Abstract	Malicious domain name attacks have become a serious issue for Internet security. In this study, a malicious domain names detection algorithm based on N-Gram is proposed. The top 100,000 domain names in Alexa 2013 are used in the N-Gram method. Each domain name excluding the top-level domain is segmented into substrings according to its domain level with the lengths of 3, 4, 5, 6, and 7. The substring set of the 100,000 domain names is established, and the weight value of a substring is calculated according to its occurrence number in the substring set. To detect a malicious attack, the domain name is also segmented by the N-Gram method and its reputation value is calculated based on the weight values of its substrings. Finally, the judgment of whether the domain name is malicious is made by thresholding. In the experiments on Alexa 2017 and Malware domain list, the proposed detection algorithm yielded an accuracy rate of 94.04%, a false negative rate of 7.42%, and a false positive rate of 6.14%. The time complexity is lower than other popular malicious domain names detection algorithms.
AbstractList	Malicious domain name attacks have become a serious issue for Internet security. In this study, a malicious domain names detection algorithm based on N-Gram is proposed. The top 100,000 domain names in Alexa 2013 are used in the N-Gram method. Each domain name excluding the top-level domain is segmented into substrings according to its domain level with the lengths of 3, 4, 5, 6, and 7. The substring set of the 100,000 domain names is established, and the weight value of a substring is calculated according to its occurrence number in the substring set. To detect a malicious attack, the domain name is also segmented by the N-Gram method and its reputation value is calculated based on the weight values of its substrings. Finally, the judgment of whether the domain name is malicious is made by thresholding. In the experiments on Alexa 2017 and Malware domain list, the proposed detection algorithm yielded an accuracy rate of 94.04%, a false negative rate of 7.42%, and a false positive rate of 6.14%. The time complexity is lower than other popular malicious domain names detection algorithms. Malicious domain name attacks have become a serious issue for Internet security. In this study, a malicious domain names detection algorithm based on N -Gram is proposed. The top 100,000 domain names in Alexa 2013 are used in the N -Gram method. Each domain name excluding the top-level domain is segmented into substrings according to its domain level with the lengths of 3, 4, 5, 6, and 7. The substring set of the 100,000 domain names is established, and the weight value of a substring is calculated according to its occurrence number in the substring set. To detect a malicious attack, the domain name is also segmented by the N -Gram method and its reputation value is calculated based on the weight values of its substrings. Finally, the judgment of whether the domain name is malicious is made by thresholding. In the experiments on Alexa 2017 and Malware domain list, the proposed detection algorithm yielded an accuracy rate of 94.04%, a false negative rate of 7.42%, and a false positive rate of 6.14%. The time complexity is lower than other popular malicious domain names detection algorithms.
Author	Bao, Guangbin Zeng, Xiang-Yan Chang, Zhaobin Hong, Zhao
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Cites_doi	10.1007/s11063-017-9666-7 10.1016/j.diin.2017.12.002 10.1155/2017/7674594 10.1016/j.comnet.2015.12.008 10.1109/mnet.2013.6423188 10.1109/tnet.2012.2184552 10.1109/lcomm.2013.112413.132231 10.1145/2584679 10.1016/j.diin.2014.11.001
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Copyright	Copyright © 2019 Hong Zhao et al. Copyright © 2019 Hong Zhao et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0
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Snippet	Malicious domain name attacks have become a serious issue for Internet security. In this study, a malicious domain names detection algorithm based on N-Gram is... Malicious domain name attacks have become a serious issue for Internet security. In this study, a malicious domain names detection algorithm based on N -Gram...
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SubjectTerms	Algorithms Behavior Blacklisting Computer networks Computer simulation Cybersecurity Domain names International conferences Internet Malware Mathematical analysis Semantics Servers Trademarks URLs Weight
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Title	Malicious Domain Names Detection Algorithm Based on N-Gram
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