I-Filter:用于加速恶意软件变体分类的相同结构化控制流字符串过滤器

Taegyu Kim, Woomin Hwang, Ki-Woong Park, Kyungoh Park
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引用次数: 2

摘要

随着恶意软件变种数量的迅速增长,分类速度在安全问题中变得至关重要。虽然已经提出了几种恶意软件变体分类技术,但它们都涉及速度和准确性的权衡。为了实现快速准确的恶意软件变体分类,我们彻底分析了以前提出的方法,并确定了字符串到字符串匹配的关键性能瓶颈。本文提出并评估了一种称为I-Filter的技术,该技术提高了先前方法的性能,即近似匹配。I-Filter具有以下新颖的机制,基于哈希的等效过程匹配技术。我们的性能评估证实,通过I-Filtering,性能平均提高了1,043倍。
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I-Filter: Identical Structured Control Flow String filter for accelerated malware variant classification
As the number of malware variants has grown rapidly, classification speed has become crucial in security issues. While several techniques for malware variant classification have been proposed, they involve a speed-accuracy trade-off. In an attempt to achieve a speedy and accurate malware variant classification, we thoroughly analyze previously proposed methods and identify a critical performance bottleneck in string-to-string matching. This paper presents and evaluates a technique called I-Filter that enhances the performance of the previous approach, approximate matching. I-Filter has the following novel mechanism, the hash-based equivalent procedure matching technique. Our performance evaluation confirms that a performance improvement of on average 1,043 times through I-Filtering.
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