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A Review Paper of Malware Detection Using API Call Sequences

机译:使用API​​调用序列进行恶意软件检测的评论论文

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摘要

Traditional signature-based malware detection techniques have been used for many years owing to their high detection rates and low false positive rates. However, signature-based techniques are ineffective as they do not detect new, polymorphic or transient malware. To beat weaknesses in signature-detection techniques, researchers have turned to behaviour-based ones, which create a malware behaviour profile by capturing malicious API calls throughout execution. In this context, API matching techniques use API calls to calculate similarities between malware. However, API concatenation techniques need significant API call process resources, which makes these methods slow owing to process quality, and thus cannot scale to large API call sequences outside the lab. In this paper, we review present malware detection strategies supported by API call sequences.
机译:由于传统的基于签名的恶意软件检测技术具有很高的检测率和较低的误报率,因此已经使用了许多年。但是,基于签名的技术无效,因为它们无法检测到新的,多态的或短暂的恶意软件。为了克服签名检测技术的弱点,研究人员转向了基于行为的方法,该方法通过捕获整个执行过程中的恶意API调用来创建恶意软件行为配置文件。在这种情况下,API匹配技术使用API​​调用来计算恶意软件之间的相似性。但是,API串联技术需要大量的API调用过程资源,这会由于过程质量而使这些方法变慢,因此无法扩展到实验室外的大型API调用序列。在本文中,我们回顾了API调用序列支持的当前恶意软件检测策略。

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