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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2018
عنوان انگلیسی مقاله:
From big data to knowledge: A spatio temporal approach to malware detection
ترجمه فارسی عنوان مقاله:
از داده های بزرگ به دانش: یک رویکرد زمان فضایی به تشخیص نرم افزارهای مخرب
منبع:
Sciencedirect - Elsevier - Computers & Security, 74 (2018) 167-183: doi:10:1016/j:cose:2017:12:005
نویسنده:
Weixuan Mao a,b, Zhongmin Cai a,*, Yuan Yang a, Xiaohong Shi c, Xiaohong Guan a,d
چکیده انگلیسی:
The deployment of endpoint protection has been gradually migrated from individual clients
to remote cloud servers, which is termed as cloud based security service. The new para
digm of security defense produces a large amount of data and log files, and motivates data
driven techniques for detecting malicious software. This paper conducts an empirical study
on the log of a real cloud based security service to characterize the occurrence of execut
able files in end hosts, which concerns 124,782 benign and 113,305 malicious executable
files occurred in 165,549,417 end hosts. The end hosts and the timestamps that an execut
able file occurs in provide insights into the distribution of software in wild from spatial and
temporal perspectives, respectively. Meanwhile, we investigate the strategies behind the char
acterizations, and observe the preferential attachment process and the periodicity of file
occurrence in end hosts. The observed different occurrence patterns of benign and mali
cious files in end hosts inspire us a new scalable approach to malware detection. We learn
from the characterizations that, the associated files shared more spatial and temporal in
formation in common are more likely to be same in their labels, either benign or malicious.
Thus, we devise a graph based semi-supervised learning algorithm for real-time malware
detection by taking into account the spatio-temporal information of the distribution of ex
ecutable files. Experimental results demonstrate that our approach increases the performance
on malware detection by 14.7% over previous techniques on average.
Keywords: Malware detection ، Data-driven security analysis ، File co-occurrence ، Graph based semi-supervised ، learning ، Content-agnostic
قیمت: رایگان
توضیحات اضافی:
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