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- W2293617811 abstract "Nowadays, malware is growing rapidly through the last few years and becomes more and more sophisticated as well as dangerous. A striking malware is obfuscation malware that is very difficult to detect. This kind of malware can create new variants that are similar to original malware feature but different about code. In order to deal with such types of malware, many approaches have been proposed, however, some of these approaches are ineffective due to their limited detection range, huge overheads or manual stages. Malware detection based on signature, for example, cannot overcome the obfuscation techniques of malware. Likewise, the behavior-based methods have the natural problems of a monitoring system such as recovery costs and long-lasting detection time. In this paper, we propose a new method (semantic set method) to detect metamorphic malware effectively by using semantic set (a set of changed values of registers or variables allocated in memory when a program is executed). For more details, this semantic set is analyzed by n-gram separator and Naive Bayes classifier to increase detection accuracy and reduce detection time. This system has been already experimented on different datasets and got the accuracy up to 98% and detection rate almost 100%." @default.
- W2293617811 created "2016-06-24" @default.
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- W2293617811 date "2014-01-01" @default.
- W2293617811 modified "2023-09-29" @default.
- W2293617811 title "Semantic Set Analysis for Malware Detection" @default.
- W2293617811 cites W135165129 @default.
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- W2293617811 doi "https://doi.org/10.1007/978-3-662-45237-0_62" @default.
- W2293617811 hasPublicationYear "2014" @default.
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