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Analysis and Comparison of Opcode-based Malware Detection Approaches

机译:基于Opcode的恶意软件检测方法的分析与比较

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Malicious software (Malwares) become major threats for digital assets in the digital environment. Traditional malware detection systems use the signatures of the malware executables to detect them. However, the complexity and diversity of malwares increases day by day with metamorphic ones that quickly change its structure and signature. Therefore, most of the researches have focused on the detection of these kinds of malwares. In this work, five different malware detection approaches have been implemented and tested on real and synthetic malware and benign samples. We have collected a new malware data set including 6857 benign and 8701 malicious samples. Experiments have shown that the real malware executables decrease the performance of the methods.
机译:恶意软件(恶意恶魔)成为数字环境中数字资产的主要威胁。传统恶意软件检测系统使用恶意软件可执行文件的签名来检测它们。然而,棕褐色的复杂性和多样性与变质的,迅速改变其结构和签名的变质。因此,大多数研究都集中在检测这些种类的恶魔之中。在这项工作中,已经在真实和合成恶意软件和良性样本上实现和测试了五种不同的恶意软件检测方法。我们收集了一个新的恶意软件数据集,包括6857个良性和8701个恶意样本。实验表明,真正的恶意软件可执行文件降低了方法的性能。

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