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.     

Metamorphic malware identification using engine-specific patterns based on co-opcode graphs

A metamorphic virus is a type of malware that modifies its code using a morphing engine. Morphing engines are used to generate a large number of metamorphic malware variants by performing different obfuscation techniques. Since each metamorphic malware has its own unique structure, signature based anti-virus programs are ineffective to detect these metamorphic variants. Therefore, detection of these kind of viruses becomes an increasingly important task. Recently, many researchers have focused on extracting common patterns of metamorphic variants that can be used as micro-signatures to identify the metamorphic malware executables. With the similar motivation, in this work, we propose a novel metamorphic malware identification method, named HLES-MMI (Higher-level Engine Signature based Metamorphic Malware Identification). The proposed method firstly constructs a unique graph structure, called as co-opcode graph, for each metamorphic family, then extracts engine-specific opcode patterns from the graphs. Finally, it generates higher-level signature belonging to each family by representing the extracted opcode-patterns with a binary vector. Experimental results on four datasets produced by different morphing engines demonstrate the effectiveness and efficiency of the proposed method by comparing with several existing malware identification methods.     

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.     

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.     

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.     

Hunting for Pirated Software Using Metamorphic Analysis

ABSTRACT

In this article, we consider the problem of detecting software that has been pirated and modified. We analyze a variety of detection techniques that have been previously studied in the context of malware detection. For each technique, we empirically determine the detection rate as a function of the degree of modification of the original code. We show that the code must by substantially modified before we fail to reliably distinguish it, and we show that our results offer a significant improvement over previous related work. Our approach can be applied retroactively to existing software and does not require access to the source code, and hence it is both practical and effective.     

Malware detection using assembly and API call sequences

One of the major problems concerning information assurance is malicious code. To evade detection, malware has also been encrypted or obfuscated to produce variants that continue to plague properly defended and patched networks with zero day exploits. With malware and malware authors using obfuscation techniques to generate automated polymorphic and metamorphic versions, anti-virus software must always keep up with their samples and create a signature that can recognize the new variants. Creating a signature for each variant in a timely fashion is a problem that anti-virus companies face all the time. In this paper we present detection algorithms that can help the anti-virus community to ensure a variant of a known malware can still be detected without the need of creating a signature; a similarity analysis (based on specific quantitative measures) is performed to produce a matrix of similarity scores that can be utilized to determine the likelihood that a piece of code under inspection contains a particular malware. Two general malware detection methods presented in this paper are: Static Analyzer for Vicious Executables (SAVE) and Malware Examiner using Disassembled Code (MEDiC). MEDiC uses assembly calls for analysis and SAVE uses API calls (Static API call sequence and Static API call set) for analysis. We show where Assembly can be superior to API calls in that it allows a more detailed comparison of executables. API calls, on the other hand, can be superior to Assembly for its speed and its smaller signature. Our two proposed techniques are implemented in SAVE) and MEDiC. We present experimental results that indicate that both of our proposed techniques can provide a better detection performance against obfuscated malware. We also found a few false positives, such as those programs that use network functions (e.g. PuTTY) and encrypted programs (no API calls or assembly functions are found in the source code) when the thresholds are set 50% similarity measure. However, these false positives can be minimized, for example by changing the threshold value to 70% that determines whether a program falls in the malicious category or not.     

Using ontologies to perform threat analysis and develop defensive strategies for mobile security

Existing studies on the detection of mobile malware have focused mainly on static analyses performed to examine the code-structure signature of viruses, rather than the dynamic behavioral aspects. By contrast, the unidentified behavior of new mobile viruses using the self-modification, polymorphic, and mutation techniques for variants have largely been ignored. The problem of precision regarding malware variant detection has become one of the key concerns in mobile security. Accordingly, the present study proposed a threat risk analysis model for mobile viruses, using a heuristic approach incorporating both malware behavior analysis and code analysis to generate a virus behavior ontology associated with the Protégé platform. The proposed model can not only explicitly identify an attack profile in accordance with structural signature of mobile viruses, but also overcome the uncertainty regarding the probability of an attack being successful. This model is able to achieve this by extending frequent episode rules to investigate the attack profile of a given malware, using specific event sequences associated with the sandbox technique for mobile applications (apps) and hosts. For probabilistic analysis, defense evaluation metrics for each node were used to simulate the results of an attack. The simulations focused specifically on the attack profile of a botnet to assess the threat risk. The validity of the proposed approach was demonstrated numerically by using two malware cyber-attack examples. Overall, the results presented in this paper prove that the proposed scheme offers an effective countermeasure, evaluated using a set of security metrics, for mitigating network threats by considering the interaction between the attack profiles and defense needs.     

Fragmented malware through RFID and its defenses

Malware, in essence, is an infiltration to one’s computer system. Malware is created to wreak havoc once it gets in through weakness in a computer’s barricade. Anti-virus companies and operating system companies are working to patch weakness in systems and to detect infiltrators. However, with the advance of fragmentation, detection might even prove to be more difficult. Malware detection relies on signatures to identify malware of certain shapes. With fragmentation, functionality and size can change depending on how many fragments are used and how the fragments are created. In this paper we present a robust malware detection technique, with emphasis on detecting fragmentation malware attacks in RFID systems that can be extended to detect complex obfuscated and mutated malware. After a particular fragmented malware has been first identified, it can be analyzed to extract the signature, which provides a basis for detecting variants and mutants of similar types of malware in the future. Encouraging experimental results on a limited set of recent malware are presented.     

Behavior-based features model for malware detection

The sharing of malicious code libraries and techniques over the Internet has vastly increased the release of new malware variants in an unprecedented rate. Malware variants share similar behaviors yet they have different syntactic structure due to the incorporation of many obfuscation and code change techniques such as polymorphism and metamorphism. The different structure of malware variants poses a serious problem to signature-based detection technique, yet their similar exhibited behaviors and actions can be a remarkable feature to detect them by behavior-based techniques. Malware instances also largely depend on API calls provided by the operating system to achieve their malicious tasks. Therefore, behavior-based detection techniques that utilize API calls are promising for the detection of malware variants. In this paper, we propose a behavior-based features model that describes malicious action exhibited by malware instance. To extract the proposed model, we first perform dynamic analysis on a relatively recent malware dataset inside a controlled virtual environment and capture traces of API calls invoked by malware instances. The traces are then generalized into high-level features we refer to as actions. We assessed the viability of actions by various classification algorithms such as decision tree, random forests, and support vector machine. The experimental results demonstrate that the classifiers attain high accuracy and satisfactory results in the detection of malware variants.     

“VANILLA” malware: vanishing antiviruses by interleaving layers and layers of attacks

Malware are persistent threats to any networked systems. Recent years increase in multi-core, distributed systems created new opportunities for malware authors to exploit such capabilities. In particular, the distributed execution of a malware in multiple cores may be used to evade currently widespread single-core-based detectors (e.g., antiviruses, or AVs) and malware analysis solutions that are unable to correlate data from multiple sources. In this paper, we propose a technique for distributing the malware functions in several distinct “vanilla” processes to show that AVs can be easily evaded. Therefore, our technique allows malware to interleave of layers of attacks to remain undetected by current AVs. Our goal is to expose a real menace and to discuss it so as to provide insights for the development of better AVs. We discuss the role of distributed and multicore-based malware in current and future threat scenarios with practical examples that we specially crafted for testing (e.g., a distributed sample synchronized via cache side channels). We (i) review multi-threaded/processed implementation issues (from kernel and userland) and present a multi-core-based monitoring solution; (ii) present strategies for code distribution, exemplified via DLL injectors, and discuss their weak and strong points; and (iii) evaluate how real security solutions perform when exposed to distributed malware. We converted real, serial malware to parallel code and showed that current AVs are not fully able to detect multi-core malware.

     

Software transformations to improve malware detection

Malware is code designed for a malicious purpose, such as obtaining root privilege on a host. A malware detector identifies malware and thus prevents it from adversely affecting a host. In order to evade detection, malware writers use various obfuscation techniques to transform their malware. There is strong evidence that commercial malware detectors are susceptible to these evasion tactics. In this paper, we describe the design and implementation of a malware transformer that reverses the obfuscations performed by a malware writer. Our experimental evaluation demonstrates that this malware transformer can drastically improve the detection rates of commercial malware detectors.     

Packer identification using Byte plot and Markov plot

Malware is one of the major concerns in computer security. The availability of easy to use malware toolkits and internet popularity has led to the increase in number of malware attacks. Currently signature based malware detection techniques are widely used. However, malware authors use packing techniques to create new variants of existing malwares which defeat signature based malware detection. So, it is very important to identify packed malware and unpack it before analysis. Dynamic unpacking runs the packed executable and provides an unpacked version based on the system. This technique requires dedicated hardware and is computationally expensive. As each individual packer uses its own unpacking algorithm it is important to have a prior knowledge about the packer used, in order to assist in reverse engineering. In this paper, we propose an efficient framework for packer identification problem using Byte plot and Markov plot. First packed malware is converted to Byte plot and Markov plot. Later Gabor and wavelet based features are extracted from Byte plot and Markov plot. We used SVMs (Support Vector Machine) in our analysis. We performed our experiments on nine different packers and we obtained about 95 % accuracy for nine of the packers. Our results show features extracted from Markov plot outperformed features extracted from Byte plot by about 3 %. We compare the performance of Markov plot with PEID (Signature based PE identification tool). Our results show Markov plot produced better accuracy when compared to PEID. We also performed multi class classification using Random Forest and achieved 81 % accuracy using Markov plot based features.     

基于关键应用编程接口图的恶意代码检测

针对基于特征码的恶意代码检测方法无法应对混淆变形技术的问题,提出基于关键应用编程接口(API)图的检测方法。通过提取恶意代码控制流图中含关键API调用的节点,将恶意行为抽象成关键API图,采用子图匹配的方法判定可疑程序的恶意度。实验结果证明,该方法能有效检测恶意代码变体,漏报率较低。     

基于多级签名匹配算法的Android恶意应用检测*

针对Android恶意应用泛滥的问题,提出了一种基于恶意应用样本库的多级签名匹配算法来进行Android恶意应用的检测。以MD5哈希算法与反编译生成的smali文件为基础,生成API签名、Method签名、Class签名、APK签名。利用生成的签名信息,从每一类恶意应用样本库中提取出这类恶意行为的共有签名,通过匹配待检测应用的Class签名与已知恶意应用样本库的签名,将待测应用中含有与恶意签名的列为可疑应用,并回溯定位其恶意代码,确定其是否含有恶意行为。在测试中成功的发现可疑应用并定位了恶意代码,证明了本系统的有效性。     

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.     

Auto-Sign: an automatic signature generator for high-speed malware filtering devices

This research proposes a novel automatic method (termed Auto-Sign) for extracting unique signatures of malware executables to be used by high-speed malware filtering devices based on deep-packet inspection and operating in real-time. Contrary to extant string and token-based signature generation methods, we implemented Auto-Sign an automatic signature generation method that can be used on large-size malware by disregarding signature candidates which appear in benign executables. Results from experimental evaluation of the proposed method suggest that picking a collection of executables which closely represents commonly used code, plays a key role in achieving highly specific signatures which yield low false positives.     

基于语义的恶意代码行为特征提取及检测方法

提出一种基于语义的恶意代码行为特征提取及检测方法,通过结合指令层的污点传播分析与行为层的语义分析,提取恶意代码的关键行为及行为间的依赖关系;然后,利用抗混淆引擎识别语义无关及语义等价行为,获取具有一定抗干扰能力的恶意代码行为特征.在此基础上,实现特征提取及检测原型系统.通过对多个恶意代码样本的分析和检测,完成了对该系统的实验验证.实验结果表明,基于上述方法提取的特征具有抗干扰能力强等特点,基于此特征的检测对恶意代码具有较好的识别能力.     

Malicious code detection in android: the role of sequence characteristics and disassembling methods

The acceptance and widespread use of the Android operating system drew the attention of both legitimate developers and malware authors, which resulted in a significant number of benign and malicious applications available on various online markets. Since the signature-based methods fall short for detecting malicious software effectively considering the vast number of applications, machine learning techniques in this field have also become widespread. In this context, stating the acquired accuracy values in the contingency tables in malware detection studies has become a popular and efficient method and enabled researchers to evaluate their methodologies comparatively. In this study, we wanted to investigate and emphasize the factors that may affect the accuracy values of the models managed by researchers, particularly the disassembly method and the input data characteristics. Firstly, we developed a model that tackles the malware detection problem from a Natural Language Processing (NLP) perspective using Long Short-Term Memory (LSTM). Then, we experimented with different base units (instruction, basic block, method, and class) and representations of source code obtained from three commonly used disassembling tools (JEB, IDA, and Apktool) and examined the results. Our findings exhibit that the disassembly method and different input representations affect the model results. More specifically, the datasets collected by the Apktool achieved better results compared to the other two disassemblers.

     

A comprehensive survey on deep learning based malware detection techniques

Recent theoretical and practical studies have revealed that malware is one of the most harmful threats to the digital world. Malware mitigation techniques have evolved over the years to ensure security. Earlier, several classical methods were used for detecting malware embedded with various features like the signature, heuristic, and others. Traditional malware detection techniques were unable to defeat new generations of malware and their sophisticated obfuscation tactics. Deep Learning is increasingly used in malware detection as DL-based systems outperform conventional malware detection approaches at finding new malware variants. Furthermore, DL-based techniques provide rapid malware prediction with excellent detection rates and analysis of different malware types. Investigating recently proposed Deep Learning-based malware detection systems and their evolution is hence of interest to this work. It offers a thorough analysis of the recently developed DL-based malware detection techniques. Furthermore, current trending malwares are studied and detection techniques of Mobile malware (both Android and iOS), Windows malware, IoT malware, Advanced Persistent Threats (APTs), and Ransomware are precisely reviewed.