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GNNDroid: Graph-Learning Based Malware Detection for Android Apps With native code

IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2025年第2期22卷 1460-1476页

作者： Xi, Ning Zhang, Yuchen Feng, Pengbin Ma, Siqi Ma, Jianfeng Shen, Yulong Yang, Yale Xidian Univ Sch Cyber Engn Xian 710126 Peoples R China Univ New South Wales Sch Syst & Comp Sydney NSW 2052 Australia Xidian Univ Sch Comp Sci & Technol Xidian 710126 Peoples R China

With the rapid development of mobile apps, developers tend to implement a variety of functionalities to support users' demands. Thus, they involve the usage of native libraries to fulfill the luxuriant functionalities and maintain fast system responses, instead of using a unitary programming language (i.e., Java). Nonetheless, such an inter-language programming framework also introduces more security issues because attackers can conceal malicious behaviors at the native level to evade Android security vetting. Existing state-of-the-art detection tools mainly rely on the information extracted in the Java code to infer the potential malicious behaviors implemented in native code. None of them could simultaneously study the correlated behaviors in Java and native code. Therefore, in this paper, we proposed a static semantic-driven malware detection tool, GNNDroid, to distinguish malware by combining the behaviors implemented in both Java and native code. First, GNNDroid separately analyzes Java and native code to construct Java function call graphs and native function call graphs. It then utilizes a regex-based function recognition approach to explore the correlations between Java code and native code. According to the code correlations, GNNDroid constructs Multi-Relational Directed Graphs (MRDGs) to extract the comprehensive behaviors. Finally, it executes a Gated Graph Neural Network (GGNN) to analyze the MRDGs and distinguish malicious apps. We assessed GNNDroid by analyzing 40,000 Android apps and compared them with state-of-the-art tools. The result demonstrated that GNNDroid not only performs well when analyzing Java+native apps (i.e., apps implemented by both Java and native code), achieving an F1 of 98.57% but also effectively exploits Java only apps (i.e., apps implemented by Java code), achieving an F1 of 96.31%.

关键词： Malware codes Java Feature extraction Semantics Mobile applications Libraries Function call graph gated graph neural network (GGNN) malware detection native code

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Benchmark of a PI Controller Implemented in Java and native code on the Android™ Platform Embedded on ARM

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IEEE LATIN AMERICA TRANSACTIONS 2015年第8期13卷 2488-2494页

作者： Melo, R. R. Caratti Filho, E. Casillo, D. S. S. Villarreal, E. R. L. Univ Fed Rural Semiarido UFERSA Natal RN Brazil

This paper presents a performance analysis of a PI controller implemented on the Android (TM) platform in embedded ARM. To perform the analysis are presented two execution strategies. The first strategy is the execution of PI controller from its implementation in Java. The second strategy is the execution of the PI controller from its implementation in native code. In the analysis of the experiment with two suggested strategies, it was found that the controller obtained better performance and stability while running in native code.

关键词： PI Controller Android ARM microcontroller java code native code

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native code Generation Using the Transterpreter

Native Code Generation Using the Transterpreter

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29th WoTUG Technical Meeting 2006

作者： Jacobsen, Christian L. Dimmich, Damian J. Jadud, Matthew C. Univ Kent Comp Lab Canterbury CT2 7NZ Kent England

ISBN: (纸本)9781586036713

We are interested in languages that provide powerful abstractions for concurrency and parallelism that execute everywhere, efficiently. Currently, the existing runtime environments for the occam-pi programming language provide either one of these features (portability) or some semblance of the other (performance). We believe that both can be achieved through the careful generation of C from occam-pi, and demonstrate that this is possible using the Transterpreter, a portable interpreter for occam-pi, as our starting point.

关键词： Transterpreter native code GCC occam-pi

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Protection of Android Applications from Decompilation Using Class Encryption and native code

Protection of Android Applications from Decompilation Using ...

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Ilic, Stefan Dukic, Slavica RT RK Inst Comp Based Syst Banja Luka Bosnia & Herceg Fac Elect Engn Banja Luka Bosnia & Herceg

ISBN: (纸本)9781509029570

In this paper we analyzed how different methods of protection, namely class encryption and usage of native code, affect decompilation of Android applications. This is important in consumer electronics as it can be used to stop repackaging and spreading of maliciously modified applications. We analyzed two different methods of protection, their benefits and flaws.

关键词： Android decompilation class encryption native code

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Tuplex: Data Science in Python at native code Speed 21

Tuplex: Data Science in Python at Native Code Speed

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ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Spiegelberg, Leonhard Yesantharao, Rahul Schwarzkopf, Malte Kraska, Tim Brown Univ Providence RI 02912 USA MIT CSAIL Cambridge MA USA

ISBN: (纸本)9781450383431

Today's data science pipelines often rely on user-defined functions (UDFs) written in Python. But interpreted Python code is slow, and Python UDFs cannot be compiled to machine code easily. We present Tuplex, a new data analytics framework that just-in-time compiles developers' natural Python UDFs into efficient, end-to-end optimized native code. Tuplex introduces a novel dual-mode execution model that compiles an optimized fast path for the common case, and falls back on slower exception code paths for data that fail to match the fast path's assumptions. Dual-mode execution is crucial to making end-to-end optimizing compilation tractable: by focusing on the common case, Tuplex keeps the code simple enough to apply aggressive optimizations. Thanks to dual-mode execution, Tuplex pipelines always complete even if exceptions occur, and Tuplex's post-facto exception handling simplifies debugging. We evaluate Tuplex with data science pipelines over real-world datasets. Compared to Spark and Dask, Tuplex improves end-to-end pipeline runtime by 5-91x and comes within 1.1-1.7x of a hand-optimized C++ baseline. Tuplex outperforms other Python compilers by 6x and competes with prior, more limited query compilers. Optimizations enabled by dual-mode processing improve runtime by up to 3x, and Tuplex performs well in a distributed setting.

关键词： user defined functions data science python high performance computing big data analytics udf native code jit compiler dataframe query processing query execution compilation

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Identifying Java Calls in native code via Binary Scanning 2020

Identifying Java Calls in Native Code via Binary Scanning

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29th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)

作者： Fourtounis, George Triantafyllou, Leonidas Smaragdakis, Yannis Univ Athens Athens Greece

ISBN: (纸本)9781450380089

Current Java static analyzers, operating either on the source or byte-code level, exhibit unsoundness for programs that contain native code. We show that the Java native Interface ( JNI) specification, which is used by Java programs to interoperate with Java code, is principled enough to permit static reasoning about the effects of native code on program execution when it comes to call-backs. Our approach consists of disassembling native binaries, recovering static symbol information that corresponds to Java method signatures, and producing a model for statically exercising these native call-backs with appropriate mock objects. The approach manages to recover virtually all Java calls in native code, for both Android and Java desktop applicationsD(a) achieving 100% native-to-application call-graph recall on large Android applications (Chrome, Instagram) and (b) capturing the full native call-back behavior of the XCorpus suite programs.

关键词： static analysis Java native code binary

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An Android Malware Detection Method Based on native code and LSTM 24

An Android Malware Detection Method Based on Native Code and...

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6th World Symposium on Software Engineering

作者： Fu, Hao Wu, Shaofei Wuhan Inst Technol Sch Comp Sci & Engn Hubei Prov Key Lab Intelligent Robots Wuhan Hubei Peoples R China

ISBN: (纸本)9798400717086

The ubiquity of mobile Internet has made smartphones an essential component of modern life, yet they remain prime targets for information security threats. Despite a multitude of malware detection systems, skilled developers of malicious software adeptly conceal harmful code within applications, thus eluding standard detection techniques. A significant challenge in Android application reverse engineering is the extraction of critical code from ".so" files, known as native code, which is often viewed as a secure and clandestine strategy in Android development. Addressing this challenge, our research introduces a novel Android malware detection approach that leverages Long Short-Term Memory (LSTM) networks to scrutinize native code opcodes. By decompressing Android Application Packages (APKs) and extracting opcodes from shared libraries, we utilize the tf-idf algorithm for feature selection, facilitating the detection of obscured malicious code in a static analysis context. This methodology, bolstered by LSTM training, constructs a formidable framework for Android malware detection. Our empirical assessments confirm the method's effectiveness and superiority in detecting Android malware.

关键词： Android applications malware detection native code long short-term (LSTM) term frequency-inverse document frequency (TF-IDF)

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A risk estimation study of native code vulnerabilities in Android applications

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JOURNAL OF CYBERSECURITY 2024年第1期10卷

作者： Sanna, Silvia Lucia Soi, Diego Maiorca, Davide Fumera, Giorgio Giacinto, Giorgio Univ Cagliari Dept Elect & Elect Engn I-09123 Piazza Darmi Italy

Android is the most used operating system (OS) worldwide for mobile devices, with hundreds of thousands of apps downloaded daily. Although these apps are primarily written in Java and Kotlin, advanced functionalities such as graphics or cryptography are provided through native C/C++ libraries. These libraries can be affected by common vulnerabilities in C/C++ code (e.g. memory errors such as buffer overflow), through which attackers can read/modify data or execute arbitrary code. The detection and assessment of vulnerabilities in Android native code have only been recently explored by previous research work. In this paper, we propose a fast risk-based approach that provides a risk score related to the native part of an Android application. In this way, before an app is released, the developer can check whether the app may contain vulnerabilities in the native code and, whether present, patch them to publish a more secure application. To this end, we first use fast regular expressions to detect library versions and possible vulnerable functions. Then, we apply scores extracted from a vulnerability database to the analyzed application, thus obtaining a risk score representative of the whole app. We demonstrate the validity of our approach by performing a large-scale analysis on more than 100 000 applications (but only 40% contained native code) and 15 popular libraries carrying known vulnerabilities. The attained results show that many applications contain well-known vulnerabilities that miscreants can potentially exploit, posing serious concerns about the security of the whole Android applications landscape.

关键词： vulnerability detection Android app native code

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Deobfuscating Android native Binary code 19

Deobfuscating Android Native Binary Code

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IEEE/ACM 41st International Conference on Software Engineering - Software Engineering in Practice (ICSE-SEIP)

作者： Kan, Zeliang Wang, Haoyu Wu, Lei Guo, Yao Xu, Guoai Beijing Univ Posts & Telecommun Beijing Peoples R China PeckShield Inc Hangzhou Zhejiang Peoples R China Peking Univ Beijing Peoples R China

ISBN: (纸本)9781728117645

In this paper, we propose an automated approach to facilitate the deobfuscation of Android native binary code. Specifically, given a native binary obfuscated by Obfuscator-LLVM (the most popular native code obfuscator), our deobfuscation system is capable of recovering the original Control Flow Graph. To the best of our knowledge, it is the first work that aims to tackle the problem. We have applied our system in different scenarios, and the experimental results demonstrate the effectiveness of our system based on generic similarity comparison metrics.

关键词： Deobfuscation native code Android Symbolic Execution Taint Analysis

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DronePilot. NET Development: AR. Drone SDK Supporting native and Managed code

DronePilot. NET Development: AR. Drone SDK Supporting Native...

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2nd International Conference on Advanced Computer Science Applications and Technologies (ACSAT)

作者： Saito, Takuya Mase, Kenichi Niigata Univ Res Inst Nat Hazards & Disaster Recovery Niigata Japan Niigata Univ Acad Assembly Niigata Japan

ISBN: (纸本)9781479927586

*** is a remote-controlled quad-rotor helicopter that is operated by computer instruction. Because communications between AR. Drone and a computer are complex, it is difficult to program AR. Drone software. Existing software development kits (SDKs) reduce those complexities;however, SDKs have many limitations. They can be difficult to use because the structure is too complex to understand, or their extensibility is too minimal for making a multifunctional application. To address these problems, we developed the new *** SDK. The *** SDK supports managed code, and it enables creation of user application programs using C# and Visual Basic. Moreover, because the proposed SDK includes code written in C++/CLI, it supports unmanaged code and enables direct access to native libraries. In addition, the *** SDK supports high-definition video data, image analysis by OpenCV, marker recognition by ARToolkit, and 3D object display by OpenGL. We measured the performance of our proposed SDK for processing video images and have verified its satisfactory performance.

关键词： SDK AR. Drone remote-controlled helicopter managed code native code

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