Static and Dynamic Analysis of Android Malware and Goodware Written with Unity Framework

Malware classification plays an important role in tracing the attack sources of computer security. However, existing static analysis methods are fast in classification, but they are inefficient in some malware using packing and obfuscation techniques; the dynamic analysis methods have better universality for packing and obfuscation, but they will cause excessive classification cost. To overcome these shortcomings, in this paper, we propose a classification system Malscore based on the probability scoring and machine learning, which sets the probability threshold to concatenate static analysis (called Phase 1) and dynamic analysis (called Phase 2). The convolutional neural networks with spatial pyramid pooling were used to analyze the grayscale images (static features) in Phase 1, and the variable n-grams and machine learning were used to analyze the native API call sequences (dynamic features) in Phase 2. Malscore combined static analysis with dynamic analysis not only accelerated the static analysis process by taking advantage of the CNN in image recognition but also appeared to be more resilient to obfuscation by the dynamic analysis. Different from other static and dynamic analysis techniques, when malware is detected, due to the fact that malware will most likely be labeled only by static analysis, we could reduce the overheads by dynamically analyzing a few malware that has less obvious features or greater confusion in static analysis. We performed experiments on 174607 malware samples from 63 malware families. The result showed that Malscore achieved 98.82% accuracy for malware classification. Furthermore, Malscore was compared with the method of using static and dynamic analysis. The preprocessing and test time represented a reduction of 59.58% and 61.70%, respectively.

Static and dynamic program analysis techniques are important research areas in software security. Static analysis helps us locate vulnerabilities in a software by looking at the source code. Dynamic analysis helps us reason about the behavior of the software from information gathered at run-time. In this thesis, we are focusing on data flow analysis and heap analysis which are key static and dynamic program analysis techniques respectively.
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\nIn the first part of this thesis, we aim at detecting vulnerabilities in Android applications which have capability leaks. The security of the Android platform relies mainly on sandboxing applications and restricting their capabilities such that no application, by default, can perform any operations that would adversely impact other applications, the operating system, or the user. However, a recent research reported that a genuine but vulnerable application may leak its capabilities. When being leveraged, other applications can gain extra capabilities which they are not granted originally. We present DroidChecker, an Android application analyzing tool which searches for the aforementioned vulnerability in Android applications. DroidChecker uses interprocedural control flow graph searching and static taint checking to detect exploitable data paths in an Android application. We analyzed more than 1100 Android applications using DroidChecker and found 6 previously unknown vulnerable applications including the renowned Adobe Photoshop Express application. We also developed a malicious application that exploits the previously unknown vulnerability found in the Adobe Photoshop Express application. We showed that the malicious application, which is not granted any permissions, can access contacts on the phone with just a few lines of code.
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\nIn the second part of this thesis, we explore the use of heap analysis to extract software birthmarks. There are techniques like code obfuscation and watermarking which can make the source code of a program difficult to understand by humans and prove the ownership of the program. However, code obfuscation cannot avoid the source code being copied and a watermark can be defaced. A birthmark is a group of unique characteristics a program possesses that can be used to identify the program. We propose two novel dynamic birthmark systems based on the run-time heap. A dynamic birthmark is one that is extracted when the program is executing. Since it is based on the run-time behavior of the program, semantics-preserving transformations of the code like obfuscation cannot defeat dynamic birthmarks. In this regard, dynamic birthmarks are more robust compared with static birthmarks.
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\nTo the best of our knowledge, these are the first birthmark systems using heap analysis as the underlying technique. The basic idea is to take snapshots of the heap while the program is running. From the snapshots, heap graphs are constructed to model the referencing structure between objects. After going through some filtering and referencing processes, they become the birthmarks. The two birthmark systems have been devised to extract birthmarks for Java programs and JavaScript programs respectively. While the underlying ideas of the two birthmark systems are similar, the differences in nature of the two programming languages led to different implementation designs.

Having about 80 % of the market share, Android is currently the clearly dominating platform for mobile devices. Application theft and repackaging remains a major threat and a cause of significant losses, affecting as much as 97 % of popular paid apps. The ease of decompilation and reverse engineering of high-level bytecode, in contrast to native binary code, is considered one of the main reasons for the high piracy rate. In this paper, we address this problem by proposing four static obfuscation techniques: native opaque predicates, native control flow flattening, native function indirection, and native field access indirection. These techniques provide a simple and yet effective way of reducing the task of bytecode reverse engineering to the much harder task of reverse engineering native code. For this purpose, native function calls are injected into an app’s bytecode, introducing artificial dependencies between the two execution domains. The adversary is forced to analyze the native code in order to be able to comprehend the overall app’s functionality and to successfully launch static and dynamic analyses. Our evaluation results of the proposed protection methods witness an acceptable cost in terms of execution time and application size, while significantly complicating the reverse-engineering process.

The past decade has witnessed an explosive increase in the scale, intensity and sophistication of cyber attacks. While software vendors have significantly increased their efforts on security, they are almost always playing catch up. As a result, security-conscious organizations and individuals have come to expect their system administrators to deploy an array of tools and techniques to stay a step ahead of the hackers. While developer-oriented security tools rely mainly on static analysis, runtime analysis and policy enforcement are the mechanisms of choice in administrator-oriented tools. Runtime techniques offer increased automation and precision over static analysis, thus addressing the needs of administrators who don't have the time or resources needed to acquire extensive knowledge about the internals of a software system. In this talk, I will begin by summarizing some of the significant advances that have been achieved in the past few years in the context of software vulnerability mitigation, including buffer overflow defenses, and more recently, the impressive results that have been achieved using dynamic information-flow analysis for blocking the most popular exploits today, including SQL and command injection and cross-site scripting. I will then proceed to describe dynamic analysis and enforcement techniques aimed at another high-profile security problem faced today, namely, malware defense. Our initial target in this regard has been on dynamic analysis techniques for extracting high-level models of program behavior. These models could be used in a variety of applications such as intrusion detection, vulnerability analysis and security policy verification. More recently, interesting advances have been made in the context of security policy development, where a combination of static and dynamic analysis techniques have been developed to synthesize low-level, enforceable policies that achieve a high-level goal such as protecting system integrity. Finally, I will conclude the talk with a discussion of some research opportunities and challenges in software security.

Device (DA) capabilities for mobile devices, e.g., remote locking/wiping, or enforcing password strength, were originally introduced to help organizations manage phone fleets or enable parental control. However, DA capabilities have been subverted and abused: malicious apps have used DA to create ransomware or lock users out, while benign apps have used DA to prevent or hinder uninstallation; in certain cases the only remedy is to factory-reset the phone. We call these apps Deathless Device Administrator (DDA), i.e., apps that cannot be uninstalled. We provide the first systematic study of Android DA capabilities, DDA apps, DDA-attack resistance across Android versions, and DDA-induced families in malicious apps. To enable scalable studies of questionable DA behavior, we developed DAAX, a static analyzer which exposes potential DA abuse effectively and efficiently. In a corpus of 39,459 apps (20,467 malicious and 18,992 benign) DAAX has found 4,135 DA apps and 691 potential DDA apps. The static analysis results on the 4,135 apps were cross-checked via dynamic analysis on at least 3 phones, confirming 578 true DDAs, including apps currently on Google Play. The study has shown that DAAX is effective (84.8% F-measure) and efficient (analysis typically takes 205 seconds per app).

Android access control granularity based on its permission mechanism is relatively coarse, which cannot effectively protect the user privacy. Many Android applications do not strictly abide by the principle of least privilege (PLP). Both benign and malicious apps may request more permissions than those they really use. We rethink previous permission over-claim problem of Android applications, and extend it to three kinds of problems: Explicit Permission Over-claim, Implicit Permission Over-claim and Ad Library Permission Over-claim. The latter two problems are new that have not been raised by any previous work. Static analysis is to decompile the applications to generate intermediate code and then analyze the usage of permissions. Our static analysis on 10710 applications shows that 76.08% of them may have Explicit Permission Over-claim problem, among those there are 424 applications that have sensitive permissions, which are only used in the advertisement library’s code of the applications rather than developer’s own code. They have Ad Library Permission Over-claim problem. The main idea of our semantic analysis is to calculate the semantic similarity between apps’ descriptions and function phrases. If the similarity exceeds a certain threshold, the app is considered relevant to the corresponding function. We compare the results of the semantic analysis with those of manual reading of 102 Android application descriptions. The F-measures of the three chosen functions are 80.82%, 70.48% and 89.62%, respectively. The evaluation results show our method can efficiently detect the above three kinds of permission over claim problems which indicates that our method would be helpful for normal users to have a clear understanding of permission usage of Android applications.

A secure software demands effective techniques for vulnerability detection during its development cycle. The practice of detecting security flaws before the deployment phase eliminates the risks that vulnerabilities may impose for the company. Static analysis and dynamic analysis techniques offer two complimentary approaches for checking vulnerabilities. Static analysis involves the scanning of source code or binary eliminating the need of executing it. This approach is fast and has no run time overhead. However, static analysis are quite imprecise and generate huge false positives and false negatives. On the other hand, dynamic analysis involves the running of the software. The problem of false positives and negatives is less in case of dynamic analysis because they analyze by running the test cases. But this approach requires large number of test cases to ensure a certain confidence level in detecting security bugs. This paper describes a methodology which integrates the two approaches in a complimentary manner. It adopts the strengths of the two and eliminates their weaknesses. We are currently dealing with buffer overflow vulnerability with pointer aliasing. However the idea can be extended to other vulnerabilities also for e.g memory related errors, race conditions( Time of Check to Time to Use vulnerability), dangling pointer vulnerability, integer errors etc.

Programmers using complex libraries and frameworks are faced with the difficult task of ensuring that their implementations comply with complex and informally described rules for proper sequencing of API calls. Recent advances in static and dynamic techniques for checking explicit specifications of program typestate properties have shown promise in addressing this challenge. Unfortunately, static typestate analyses are limited in their scalability and dynamic analyses can suffer from significant run-time overhead. In this paper, we present an approach that exploits information calculated by flow-sensitive static typestate analyses to reformulate the original analysis problem as a residual dynamic typestate analysis. We demonstrate that residual analyses retain the error reporting of unoptimized dynamic analysis while offering the potential for significantly reducing analysis cost

The increasing popularity of dynamically-typed programming languages, such as JavaScript or Python, requires specific support methods for developers to avoid pitfalls arising from the dynamic nature of these languages. Static analyses are frequently used but the dynamic type systems limit their applicability. Dynamic analyses, in contrast, depend on the execution of the code under analysis, and thus depend on the quality of existing tests. This quality of the test suite can be improved by the use of automated test generation but automated test generation for dynamically-typed programming languages itself is hard due to the lack of type information in the programs. The limitations of each of these approaches will be overcome by iteratively combining test generation with static and dynamic analysis techniques for dynamically-typed programs.

With the continuous growth in the usage of Android apps, ensuring their security has become critically important. An increasing number of malicious apps adopt anti-analysis techniques to evade security measures. Although some research has started to consider anti-runtime analysis (ARA), it is unfortunate that they have not systematically examined ARA techniques. Furthermore, the rapid evolution of ARA technology exacerbates the issue, leading to increasingly inaccurate analysis results. To effectively analyze Android apps, understanding their adopted ARA techniques is necessary. However, no systematic investigation has been conducted thus far. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In this paper, we conduct the first systematic study of the ARA implementations in a wide range of 117,270 Android apps (including both malicious and benign ones) collected between 2016 and 2023. Additionally, we propose a specific investigation tool named <small>ARAP</small> to assist this study by leveraging both static and dynamic analysis. According to the evaluation results, <small>ARAP</small> not only effectively identifies the ARA implementations in Android apps but also reveals many important findings. For instance, almost all apps have implemented at least one category of ARA technology (99.6% for benign apps and 97.0% for malicious apps).

Dynamic program analysis is extremely successful both in code debugging and in malicious code attacks. Fuzzing, concolic, and monkey testing are instances of the more general problem of analysing programs by dynamically executing their code with selected inputs. While static program analysis has a beautiful and well established theoretical foundation in abstract interpretation, dynamic analysis still lacks such a foundation. In this paper, we introduce a formal model for understanding the notion of precision in dynamic program analysis. It is known that in sound-by-construction static program analysis the precision amounts to completeness. In dynamic analysis, which is inherently unsound, precision boils down to a notion of coverage of execution traces with respect to what the observer (attacker or debugger) can effectively observe about the computation. We introduce a topological characterisation of the notion of coverage relatively to a given (fixed) observation for dynamic program analysis and we show how this coverage can be changed by semantic preserving code transformations. Once again, as well as in the case of static program analysis and abstract interpretation, also for dynamic analysis we can morph the precision of the analysis by transforming the code. In this context, we validate our model on well established code obfuscation and watermarking techniques. We confirm the efficiency of existing methods for preventing control-flow-graph extraction and data exploit by dynamic analysis, including a validation of the potency of fully homomorphic data encodings in code obfuscation.

Dynamic analysis techniques reason over program executions and deal with data produced at program execution time. Dynamic analysis and static analysis techniques complement each other. Hence, a key focus of the workshop is dynamic analysis of software systems with an emphasis on research that integrates static and dynamic analyses.

Recent research proposed efficient methods for software verification combining static and dynamic analysis, where static analysis reports possible runtime errors (some of which may be false alarms) and test generation confirms or rejects them. However, test generation may time out on real-sized programs before confirming some alarms as real bugs or rejecting some others as unreachable.To overcome this problem, we propose to reduce the source code by program slicing before test generation. This paper presents new optimized and adaptive usages of program slicing, provides underlying theoretical results and the algorithm these usages rely on. The method is implemented in a tool prototype called sante (Static ANalysis and TEsting). Our experiments show that our method with program slicing outperforms previous combinations of static and dynamic analysis. Moreover, simplifying the program makes it easier to analyze detected errors and remaining alarms.

Android based Smartphones are nowadays getting more popular. While using Smartphone, user is always concerned about security and malicious attacks, cryptographic vulnerability of the applications. With increase in the number of Android mobiles, Android malwares are also increasing very rapidly. So the authors have proposed the “Identification of cryptographic vulnerability and malware detection in Android” system. They have designed a user friendly android application, through which user and developer can easily test the application whether it is benign or vulnerable. The application will be tested firstly using static analysis and then the dynamic analysis will be carried out. The authors have implemented static and dynamic analysis of android application for vulnerable and malicious app detection. They have also created a web page. User can either use the application or the web page.

The two broad approaches to discover properties of programs---static and dynamic analyses---have complementary strengths: static techniques perform exhaustive exploration and prove upper bounds on program behaviors, while the dynamic analysis of test cases provides concrete evidence of these behaviors and promise low false alarm rates. In this paper, we present DynaBoost, a system which uses information obtained from test executions to prioritize the alarms of a static analyzer. We instrument the program to dynamically look for dataflow behaviors predicted by the static analyzer, and use these results to bootstrap a probabilistic alarm ranking system, where the user repeatedly inspects the alarm judged most likely to be a real bug, and where the system re-ranks the remaining alarms in response to user feedback. The combined system is able to exploit information that cannot be easily provided by users, and provides significant improvements in the human alarm inspection burden: by 35% compared to the baseline ranking system, and by 89% compared to an unaided programmer triaging alarm reports.