Uncovering Malware Traits Using Hybrid Analysis

Reischaga, Charles Lim, Yohanes Syailendra Kotualubun
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Abstract

Malware, its volume increases each year and its threat becoming ever more prevalent, is responsible for a large portion of security incidents. Unfortunately, most of the time information regarding the threat that it poses are notional. In this paper, we conduct heuristic static and dynamic analysis in order to extract the necessary static analysis and dynamic analysis features for detecting, assessing and measuring malware threats. Based on the given datasets, i.e. 876 malware and 49 benignware, our proposed method was able to quantitatively assess the threat level of malware and detect malware with promising results.
使用混合分析发现恶意软件特征
恶意软件的数量每年都在增加,其威胁变得越来越普遍,是造成很大一部分安全事件的原因。不幸的是,大多数时候,关于它所构成的威胁的信息都是名义上的。在本文中,我们进行启发式静态和动态分析,以提取检测、评估和测量恶意软件威胁所需的静态分析和动态分析特征。基于给定的数据集,即876种恶意软件和49种恶意软件,我们提出的方法能够定量评估恶意软件的威胁级别并检测恶意软件,结果令人满意。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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