peer reviewed

The rapid evolution of Android malware poses significant challenges to the maintenance and security of mobile applications (apps). Traditional detection techniques often struggle to keep pace with emerging malware variants that employ advanced tactics such as code obfuscation and dynamic behavior triggering. One major limitation of these approaches is their inability to localize malicious payloads at a fine-grained level, hindering precise understanding of malicious behavior. This gap in understanding makes the design of effective and targeted mitigation strategies difficult, leaving mobile apps vulnerable to continuously evolving threats. To address this gap, we propose MalLoc, a novel approach that leverages the code understanding capabilities of large language models (LLMs) to localize malicious payloads at a fine-grained level within Android malware. Our experimental results demonstrate the feasibility and effectiveness of using LLMs for this task, highlighting the potential of MalLoc to enhance precision and interpretability in malware analysis. This work advances beyond traditional detection and classification by enabling deeper insights into behavior-level malicious logic and opens new directions for research, including dynamic modeling of localized threats and targeted countermeasure development.

The incompleteness of 3rd-party app testing is an accepted fact in Software Engineering. This issue makes it impossible to verify the app functionality and to confirm its safety to the end-user. To solve this problem, enterprises developed strict policies. A company, willing to use modern apps, may perform an expensive security analysis, rely on trust or forbid the app. These strategies may lead companies to high direct and indirect spending with no guarantee of safety. In this work, we present a novel approach, called Dynamic Binary Shrinking, that allows a user to review app functionality and leave only tested code. The shrunk app produces 100 % instruction coverage on observed behaviors and in this way guarantees the absence of unexplored, and therefore, potentially malicious code. On our running examples, we demonstrate that apps use less than 20 % of the codebase. We developed an approach and the ACVCut tool to shrink Android apps towards the executed code. Repository - http//github.com/pilgun/acvcut [1].

Cryptojacking applications pose a serious threat to mobile devices. Due to the extensive computations, they deplete the battery fast and can even damage the device. In this work we make a step towards combating this threat. We collected and manually verified a large dataset of Android mining apps. In this paper, we analyze the gathered miners and identify how they work, what are the most popular libraries and APIs used to facilitate their development, and what static features are typical for this class of applications. Further, we analyzed our dataset using VirusTotal. The majority of our samples is considered malicious by at least one VirusTotal scanner, but 16 apps are not detected by any engine; and at least 5 apks were not seen previously by the service. Mining code could be obfuscated or fetched at runtime, and there are many confusing miner-related apps that actually do not mine. Thus, static features alone are not sufficient for miner detection. We have collected a feature set of dynamic metrics both for miners and unrelated benign apps, and built a machine learning-based tool for dynamic detection. Our BrenntDroid tool is able to detect miners with 95% of accuracy on our dataset.

Today, there are millions of third-party Android applications. Some of them are buggy or even malicious. To identify such applications, novel frameworks for automated black-box testing and dynamic analysis are being developed by the Android community. Code coverage is one of the most common metrics for evaluating effectiveness of these frameworks. Furthermore, code coverage is used as a fitness function for guiding evolutionary and fuzzy testing techniques. However, there are no reliable tools for measuring fine-grained code coverage in black-box Android app testing. We present the Android Code coVerage Tool, ACVTool for short, that instruments Android apps and measures code coverage in the black-box setting at class, method and instruction granularity. ACVTool has successfully instrumented 96.9% of apps in our experiments. It introduces a negligible instrumentation time overhead, and its runtime overhead is acceptable for automated testing tools. We demonstrate practical value of ACVTool in a large-scale experiment with Sapienz, a state-of-the-art automated testing tool. Using ACVTool on the same cohort of apps, we have compared different coverage granularities applied by Sapienz in terms of the found amount of crashes. Our results show that none of the applied coverage granularities clearly outperforms others in this aspect.

Code coverage is an important metric that is used by automated Android testing and security analysis tools to guide the exploration of applications and to assess efficacy. Yet, there are many different variants of this metric and there is no agreement within the Android community on which are the best to work with. In this paper, we report on our preliminary study using the state-of-the-art automated test design tool Sapienz. Our results suggest a viable hypothesis that combining different granularities of code coverage metrics can be beneficial for achieving better results in automated testing of Android applications.

The deluge of Android apps from third-party developers calls for sophisticated security testing and analysis techniques to inspect suspicious apps without accessing their source code. Code coverage is an important metric used in these techniques to evaluate their effectiveness, and even as a fitness function to help achieving better results in evolutionary and fuzzy approaches. Yet, so far there are no reliable tools for measuring fine-grained bytecode coverage of Android apps. In this work we present ACVTool that instruments Android apps and measures the smali code coverage at the level of classes, methods, and instructions. Tool repository: https://github.com/pilgun/acvtool