Abstract The malware detection methods are classified into two categories, namely, dynamic analysis (active analysis) and static analysis (passive analysis). These methods undergo unusual obstruction, and challenges that are process complexity, limitation over detection accuracy. The static method serves to discover malicious applications using various parameters like permission analysis, signature verification. It can be regularly obfuscated. Dynamic techniques entail investigating the performance of an application by administering it in a restricted environment. The complex version of a portable executable often emerges with an intervention by hardening the dynamic analysis centric malware detection methods. The various constraints of these dynamic and static models contribute to this manuscript represents a Multi-Level Malware detection using Triad Scale (MLMTS) built on regression coefficients. The proposed method MLMTS spans into three levels, such that the first and second level performs static analysis, and the third level performs the dynamic analysis. The second and third levels of the hierarchy invoke upon the ambiguous decision of their respective predecessor level. The proposed work is based on the Machine Learning (ML) model that determines the triad scale by applying linear regression for each level of malware detection. The call sequences of the portable executable, arguments passed to these call sequences and their fallouts (resultant values) in respective order of three levels of the MLMTS method. The experimental study manifests the significance of the proposal compared to the other recent malware detection methods.

中文翻译：

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回归系数作为恶意软件检测的三元组尺度

摘要 恶意软件检测方法分为动态分析（active analysis）和静态分析（passive analysis）两大类。这些方法经历了不寻常的障碍，以及过程复杂性、检测精度限制等挑战。静态方法用于使用权限分析、签名验证等各种参数来发现恶意应用程序。它可以定期混淆。动态技术需要通过在受限环境中管理应用程序来调查应用程序的性能。通过强化以动态分析为中心的恶意软件检测方法，可移植可执行文件的复杂版本通常会随着干预而出现。这些动态和静态模型的各种约束对本手稿有贡献，代表了使用基于回归系数的 Triad Scale (MLMTS) 进行的多级恶意软件检测。所提出的方法MLMTS跨越三个层次，第一和第二层次进行静态分析，第三层次进行动态分析。层次结构的第二层和第三层调用它们各自的前驱层的模糊决定。拟议的工作基于机器学习 (ML) 模型，该模型通过对恶意软件检测的每个级别应用线性回归来确定三元组规模。可移植可执行文件的调用序列、传递给这些调用序列的参数以及它们的结果（结果值），它们以 MLMTS 方法的三个级别的相应顺序排列。