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Evasion-resistant malware signature based on profiling kernel data structure objects

机译:基于性能分析内核数据结构对象的防回避恶意软件签名

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Malware authors attempt in an endless effort to find new methods to evade the malware detection engines. A popular method is the use of obfuscation technologies that change the syntax of malicious code while preserving the execution semantics. This leads to the evasion of signatures that are built based on the code syntax. In this paper, we propose a novel approach to develop an evasion-resistant malware signature. This signature is based on the malware's execution profiles extracted from kernel data structure objects and neither uses malicious code syntax specific information code execution flow information. Thus, proposed signature is more resistant to obfuscation methods and resilient in detecting malicious code variants. To evaluate the effectiveness of the proposed approach, a prototype signature generation tool called SigGENE is developed. The effectiveness of signatures generated by SigGENE evaluated using an experimental root kit-simulation tool that employs techniques commonly found in rootkits. This simulationtool is obfuscated using several different methods. In further experiments, real-world malware samples that have different variants with the same behavior used to verify the real-world applicability of the approach. The experiments show that the proposed approach is effective, not only in generating a signature that detects the malware and its variants and defeats different obfuscation methods, but also, in producing an execution profiles that can be used to characterize different malicious attacks.
机译:恶意软件作者不断努力寻找新的方法来逃避恶意软件检测引擎。一种流行的方法是使用混淆技术,该技术在保留执行语义的同时更改恶意代码的语法。这导致规避了基于代码语法构建的签名。在本文中,我们提出了一种新颖的方法来开发可避免规避的恶意软件签名。该签名基于从内核数据结构对象提取的恶意软件的执行配置文件,并且均不使用恶意代码语法特定的信息代码执行流信息。因此,建议的签名更能抵抗混淆方法,并在检测恶意代码变体方面具有弹性。为了评估所提出方法的有效性,开发了一种称为SigGENE的原型签名生成工具。 SigGENE生成的签名的有效性使用实验性的root kit模拟工具进行了评估,该工具采用了rootkit中常见的技术。使用几种不同的方法来混淆此仿真工具。在进一步的实验中,真实世界的恶意软件样本具有不同的变体,具有相同的行为,用于验证该方法在现实中的适用性。实验表明,提出的方法不仅有效,不仅可以生成检测恶意软件及其变种的特征码,并且可以击败不同的混淆方法,而且还可以生成可用于表征不同恶意攻击的执行配置文件。

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