基于语义的恶意代码检测算法研究

代码迷惑是一种以增加理解难度为目的的代码变换技术,主要来保护软件免遭逆向分析。恶意代码的作者为了躲避检测经常采用代码迷惑技术对程序进行转换。但是商用反病毒软件采用基于特征码的模式匹配技术而忽略了恶意代码的语义,因此最容易受到代码迷惑或病毒变种的攻击。文章中提出一种基于语义匹配的检测算法,能准确的检测出经过代码迷惑处理的恶意代码。该方法应用数据流分析技术,以变量定义使用链为单元检测每个模板及程序节点。最后通过部分实验展示了原型系统的检测效果。     

基于归一化的变形恶意代码检测

许多未知恶意代码是由已知恶意代码变形而来。该文针对恶意代码常用的变形技术,包括等价指令替换、插入垃圾代码和指令重排,提出完整的归一化方案,以典型的变形病毒Win32.Evol对原型系统进行测试,是采用归一化思想检测变形恶意代码方面的有益尝试。     

基于抽象特征检测变形恶意代码

传统的恶意代码检测方法通常以固定的指令或字节序列这些具体特征作为检测依据,因此难以检测变形恶意代码.使用抽象特征是解决该问题的一个思路.本文针对恶意代码常用的变形技术,即等价指令替换、垃圾代码插入以及指令乱序进行研究.定义了一种抽象特征,同时提出了依据该抽象特征检测变形恶意代码的方法.最后,以典型变形病毒Win32.Evol为对象进行了实验,将该方法与其它方法进行了对比.实验结果验证了该方法的有效性.     

针对指令乱序变形技术的归一化研究

新出现的恶意代码大部分是在原有恶意代码基础上修改转换而来.许多变形恶意代码更能自动完成该过程,由于其特征码不固定,给传统的基于特征码检测手段带来了极大挑战.采用归一化方法,并结合使用传统检测技术是一种应对思路.本文针对指令乱序这种常用变形技术提出了相应的归一化方案.该方案先通过控制依赖分析将待测代码划分为若干基本控制块,然后依据数据依赖图调整各基本控制块中的指令顺序,使得不同变种经处理后趋向于一致的规范形式.该方案对指令乱序的两种实现手段,即跳转法和非跳转法,同时有效.最后通过模拟测试对该方案的有效性进行了验证.     

基于环境敏感分析的恶意代码脱壳方法

加壳技术是软件的常用保护手段,但也常被恶意代码用于躲避杀毒软件的检测.通用脱壳工具根据加壳恶意代码运行时的行为特征或统计特征进行脱壳,需要建立监控环境,因此易受环境敏感技术的干扰.文中提出了一种基于环境敏感分析的恶意代码脱壳方法,利用动静结合的分析技术检测并清除恶意代码的环境敏感性.首先,利用中间语言对恶意代码的执行轨迹进行形式化表示;然后,分析执行轨迹中环境敏感数据的来源和传播过程,提取脱壳行为的环境约束;最后,求解环境约束条件,根据求解结果对恶意代码进行二进制代码插装,清除其环境敏感性.基于此方法,作者实现了一个通用的恶意代码脱壳工具:MalUnpack,并对321个最新的恶意代码样本进行了对比实验.实验结果表明MalUnpack能有效对抗恶意代码的环境敏感技术,其脱壳率达到了89.1%,显著高于现有基于动态监控的通用脱壳工具的35.5%和基于特征的定向脱壳工具的28.0%.     

一种恶意代码特征选取和建模方法

针对恶意代码分析检测中静态分析技术难以检测变形、多态代码的问题,提出一种提取恶意代码语义动态特征的方法。该方法在虚拟环境下提取恶意代码动态特征,从而达到保护物理机的目的,提取出的原始特征经过进一步的筛选处理,得到各个代码样本的API调用序列信息。为了使得特征更加有效,改进传统n-gram模型,添加n-gram频次信息以及各API间的依赖关系,构建改进的n-gram模型。实验结果分析部分采用机器学习方法,分别使用了决策树、K近邻、支持向量机、贝叶斯网络等分类器对选定的样本特征进行10折交叉验证。实验结果显示该特征选取在决策树J48下的检测效果最好,可以有效检测采用混淆、多态技术的恶意代码。     

二进制代码中函数混淆调用的识别

函数调用相关信息识别是二进制代码静态分析的基础,也是恶意代码分析的重要线索。二进制代码混淆技术通过对函数调用指令call、参数传递过程和调用返回过程的混淆来隐藏代码中函数的信息。这大大增加了程序逆向分析的难度,此技术被广泛应用在变形和多态病毒中,使其逃脱杀毒软件的查杀。论文给出了一种静态分析方法,引入了抽象栈图的概念,给出了其构造算法,利用它能够有效识别出代码中对函数调用的混淆。     

恶意代码多态变形技术的研究

恶意代码的深层隐藏和检测技术已经成为当前计算机安全技术的一个研究热点.多态变形技术是一种新型隐藏技术,它使得传统的基于特征码的检测技术相对滞后,论文详细阐述了恶意代码变种生成技术,主要包括加密技术、多态技术、变形技术.深入研究了m序列的随机性和状态遍历特性,提出的基于m序列的多态方法,能够有效提高恶意代码多态的效率和随机性.     

恶意代码多态技术研究

随着恶意代码的发展,恶意代码的隐蔽性也在不断增强。多态技术便是躲避常规检测方法的一种有效的技术,本文对恶意代码检测技术进行分析,并举例介绍目前常用的多态技术,通过本文对多态有一个更深的认识。     

针对等价指令替换变形的归一化研究

针对等价指令替换常用变形技术提出了相应的归一化方法。该方法先通过引入标准指令和建立等价转换规则来对检测代码进行重写处理;然后,再根据各基本块的数据依赖图对标准指令顺序进行调整。在该方法基础上,提出了一种综合归一化方案,该方案旨在能有效应对现实中使用了多种常用变形技术的恶意代码。最后以Win32.Evol,Win32.Zperm和Win32.Bistro为对象的实例研究验证了该方案的有效性。     

Hunting for undetectable metamorphic viruses

Commercial anti-virus scanners are generally signature based, that is, they scan for known patterns to determine whether a file is infected. To evade signature-based detection, virus writers have employed code obfuscation techniques to create metamorphic viruses. Metamorphic viruses change their internal structure from generation to generation, which can provide an effective defense against signature-based detection. To combat metamorphic viruses, detection tools based on statistical analysis have been studied. A tool that employs hidden Markov models (HMMs) was previously developed and the results are encouraging—it has been shown that metamorphic viruses created by a reasonably strong metamorphic engine can be detected using an HMM. In this paper, we explore whether there are any exploitable weaknesses in an HMM-based detection approach. We create a highly metamorphic virus-generating tool designed specifically to evade HMM-based detection. We then test our engine, showing that we can generate metamorphic copies that cannot be detected using existing HMM-based detection techniques.     

Static analysis for the detection of metamorphic computer viruses using repeated-instructions counting heuristics

Metamorphic viruses are particularly insidious as they change their form at each infection, thus making detection hard. Many techniques have been proposed to produce metamorphic malware, and many approaches have been explored to detect it. This paper introduces a detection technique that relies on the assumption that a side effect of the most common metamorphic engines is the dissemination of a high number of repeated instructions in the body of the virus program. We have evaluated our technique on a population of 1,000 programs and the experimentation outcomes indicate that it is accurate in classifying metamorphic viruses and viruses of other nature, too. Virus writers use to introduce code from benign files in order to evade antivirus; our technique is able to recognize virus even if benign code is added to it.     

Metamorphic Malware Detection Using Code Metrics

ABSTRACT

Malware is becoming more and more aggressive and new techniques are emerging to allow malicious code to evade detection by antiviruses. Metamorphic malware is a particularly insidious kind of virus that changes its form at each infection. In this article, a technique for detecting metamorphic viruses is proposed that is based on identifying specific features of the assembly code, such as the instructions that change the contents of the registers, the instructions that change the control flow, and the potential code fragmentation. Such features have been derived by the analysis of a large dataset of malware. The experimentation suggests that the proposed technique produces very high precision (over 97%) in recognizing metamorphic malware, and allows also for distinguishing among different families of malware.     

Simple substitution distance and metamorphic detection

To evade signature-based detection, metamorphic viruses transform their code before each new infection. Software similarity measures are a potentially useful means of detecting such malware. We can compare a given file to a known sample of metamorphic malware and compute their similarity—if they are sufficiently similar, we classify the file as malware of the same family. In this paper, we analyze an opcode-based software similarity measure inspired by simple substitution cipher cryptanalysis. We show that the technique provides a useful means of classifying metamorphic malware.     

Metamorphic worm that carries its own morphing engine

Metamorphic malware changes its internal structure across generations, but its functionality remains unchanged. Well-designed metamorphic malware will evade signature detection. Recent research has revealed techniques based on hidden Markov models (HMMs) for detecting many types of metamorphic malware, as well as techniques for evading such detection. A worm is a type of malware that actively spreads across a network to other host systems. In this project we design and implement a prototype metamorphic worm that carries its own morphing engine. This is challenging, since the morphing engine itself must be morphed across replications, which imposes restrictions on the structure of the worm. Our design employs previously developed techniques to evade detection. We provide test results to confirm that this worm effectively evades signature and HMM-based detection, and we consider possible detection strategies. This worm provides a concrete example that should prove useful for additional metamorphic detection research.     

Code obfuscation techniques for metamorphic viruses

This paper deals with metamorphic viruses. More precisely, it examines the use of advanced code obfuscation techniques with respect to metamorphic viruses. Our objective is to evaluate the difficulty of a reliable static detection of viruses that use such obfuscation techniques. Here we extend Spinellis’ result (IEEE Trans. Inform. Theory, 49(1), 280–284, 2003) on the detection complexity of bounded-length polymorphic viruses to metamorphic viruses. In particular, we prove that reliable static detection of a particular category of metamorphic viruses is an -complete problem. Then we empirically illustrate our result by constructing a practical obfuscator which could be used by metamorphic viruses in the future to evade detection.     

Eigenvalue analysis for metamorphic detection

Metamorphic malware changes its internal structure on each infection while maintaining its function. Although many detection techniques have been proposed, practical and effective metamorphic detection remains a difficult challenge. In this paper, we analyze a previously proposed eigenvector-based method for metamorphic detection. The approach considered here was inspired by a well-known facial recognition technique. We compute eigenvectors using raw byte data extracted from executables belonging to a metamorphic family. These eigenvectors are then used to compute a score for a collection of executable files that includes family viruses and representative examples of benign code. We perform extensive testing to determine the effectiveness of this classification method. Among other results, we show that this eigenvalue-based approach is effective when applied to a family of highly metamorphic code that successfully evades statistical-based detection. We also experiment computing eigenvectors on extracted opcode sequences, as opposed to raw byte sequences. Our experimental evidence indicates that the use of opcode sequences does not improve the results.     

Nonnegative matrix factorization and metamorphic malware detection

Metamorphic malware change their internal code structure by adopting code obfuscation technique while maintaining their malicious functionality during each infection. This causes change of their signature pattern across each infection and makes signature based detection particularly difficult. In this paper, through static analysis, we use similarity score from matrix factorization technique called Nonnegative Matrix Factorization for detecting challenging metamorphic malware. We apply this technique using structural compression ratio and entropy features and compare our results with previous eigenvector-based techniques. Experimental results from three malware datasets show this is a promising technique as the accuracy detection is more than 95%.

     

Chi-squared distance and metamorphic virus detection

Metamorphic malware changes its internal structure with each generation, while maintaining its original behavior. Current commercial antivirus software generally scan for known malware signatures; therefore, they are not able to detect metamorphic malware that sufficiently morphs its internal structure. Machine learning methods such as hidden Markov models (HMM) have shown promise for detecting hacker-produced metamorphic malware. However, previous research has shown that it is possible to evade HMM-based detection by carefully morphing with content from benign files. In this paper, we combine HMM detection with a statistical technique based on the chi-squared test to build an improved detection method. We discuss our technique in detail and provide experimental evidence to support our claim of improved detection.     

Zero-day攻击多态蠕虫研究与进展

随着计算机网络的不断普及与发展,网络蠕虫已经成为网络系统安全的重要威胁之一。近年来,网络蠕虫又有了新的变化,出现了新的Zero-day攻击多态蠕虫,这种蠕虫采用＂多态＂技术并以＂Zero-day漏洞＂为攻击目标,可在短时间内有效地避开检测系统,成为未来互联网安全的一大隐患。因此,研究Zero-day攻击多态蠕虫及其检测技术是非常必要的。首先论述了Zero-day攻击多态蠕虫的攻击原理,接着对近几年提出的基于网络流过滤和模拟执行检测等方法进行了分析、总结,最后给出一些热点问题及展望。