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Understanding Source Code Variability in Cloned Android Families: An Empirical Study on 75 Families

Anas Shatnawi, Tewfik Ziadi, Mohamed Yassin Mohamadi
2019 2019 26th Asia-Pacific Software Engineering Conference (APSEC)  
We perform an empirical study on 75 android families to gain insights about observable phenomena related to the commonality and variability between the source code of PVs of these families.  ...  In particular, we study three research questions to identify the commonality and variability related to the organization of source code files, cloning Java methods, and configuration parameters of AndroidManifest.xml  ...  CONCLUSION AND FUTURE WORKS We performed an empirical study on 75 android families developed using clone-and-own approach in fork-based development.  ... 
doi:10.1109/apsec48747.2019.00047 dblp:conf/apsec/ShatnawiZM19 fatcat:juz3d7cl3zf5riz2zc5hk7orym

A Survey of Android Malware Static Detection Technology Based on Machine Learning

Qing Wu, Xueling Zhu, Bo Liu
2021 Mobile Information Systems  
In this paper, we investigated Android applications' structure, analysed various sources of static features, reviewed the machine learning methods for detecting Android malware, studied the advantages  ...  To detect Android malware, researchers have proposed various techniques, among which the machine learning-based methods with static features of apps as input vectors have apparent advantages in code coverage  ...  malware detection, familial clustering, app clone detection, and app recommendation tasks.  ... 
doi:10.1155/2021/8896013 doaj:9dc548d197fd404fbcd4ee962f374bde fatcat:mbuavifbmzfmjm3shzm4wcbm4a

The MalSource Dataset: Quantifying Complexity and Code Reuse in Malware Development [article]

Alejandro Calleja, Juan Tapiador, Juan Caballero
2018 arXiv   pre-print
study the extent to which code reuse is present in our dataset.  ...  We detect a significant number of code clones across malware families and report which features and functionalities are more commonly shared.  ...  CONCLUSION In this paper, we have presented a study on the evolution of malware source code over the last four decades, as well as a study of source code reuse among malware families.  ... 
arXiv:1811.06888v1 fatcat:3vjjtk2eqngvfgro2btqepvwai

Self-protection of Android systems from inter-component communication attacks

Mahmoud Hammad, Joshua Garcia, Sam Malek
2018 Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering - ASE 2018  
In Chapter 4, we show, via a large-scale empirical study, the effect of code obfuscation on Android apps and anti-malware products.  ...  A Large-Scale Empirical Study on the Effects of Code Obfuscations on Android Apps and Anti-Malware Products. Mahmoud Hammad, Hamid Bagheri, and Sam Malek.  ...  The experimental evaluations show that RevealDroid achieves an accuracy of 98% in detection of malware and an accuracy of 95% in determination of their families.  ... 
doi:10.1145/3238147.3238207 dblp:conf/kbse/HammadGM18 fatcat:qht4e54ehjfltlht6wjuwzsata

Machine Learning-Based Routing and Wavelength Assignment in Software-Defined Optical Networks

Ignacio Martin, Sebastian Troia, Jose Alberto Hernandez, Alberto Rodriguez, Francesco Musumeci, Guido Maier, Rodolfo Alvizu, Oscar Gonzalez de Dios
2019 IEEE Transactions on Network and Service Management  
For this, different parts of the source code of any two applications are compared to look for hints on whether they have been cloned.  ...  While previous work mainly focuses on other characteristics of Android apps, like application code or behavior, our aim is to study meta-data and use it in the detection of malware applications.  ... 
doi:10.1109/tnsm.2019.2927867 fatcat:or3lhqdqbnas3cztwkqr5ykhhq

Identifying Major Research Areas and Minor Research Themes of Android Malware Analysis and Detection Field Using LSA

Deepak Thakur, Jaiteg Singh, Gaurav Dhiman, Mohammad Shabaz, Tanya Gera, Long Wang
2021 Complexity  
An empirical overview of contemporary machine learning methods, which have the potential to expedite evidence synthesis within research literature, has been explained.  ...  TRENDMINER uncovered the intellectual structure of a corpus of 444 abstracts of research articles (published during 2010–2019) on Android malware analysis and detection.  ...  on an Android device.  ... 
doi:10.1155/2021/4551067 fatcat:nb5q2ult5fg55fs4c5sasz6fii

A Comprehensive Review of Android Security: Threats, Vulnerabilities, Malware Detection, and Analysis

Saket Acharya, Umashankar Rawat, Roheet Bhatnagar, Bharat Bhushan
2022 Security and Communication Networks  
The popularity and open-source nature of Android devices have resulted in a dramatic growth of Android malware.  ...  Most of the researchers have focused on Android system security.  ...  An attacker can embed malicious code in the original source code and repack the APK file. Figure 21(a) shows control flow graph of malicious source code.  ... 
doi:10.1155/2022/7775917 fatcat:ux2eun5y4bbfxlqkc36glj2fxq

Rebooting Research on Detecting Repackaged Android Apps: Literature Review and Benchmark [article]

Li Li, Tegawendé Bissyandé, Jacques Klein
2018 arXiv   pre-print
Repackaging is a serious threat to the Android ecosystem as it deprives app developers of their benefits, contributes to spreading malware on users' devices, and increases the workload of market maintainers  ...  In the space of six years, the research around this specific issue has produced 57 approaches which do not readily scale to millions of apps or are only evaluated on private datasets without, in general  ...  The authors would like to thank the anonymous reviewers who have provided insightful and constructive comments that have led to important improvements in several parts of the manuscript.  ... 
arXiv:1811.08520v1 fatcat:zjpauswnn5fprdqdnetp3zbhfu

A Survey on Deep Learning for Software Engineering [article]

Yanming Yang, Xin Xia, David Lo, John Grundy
2020 arXiv   pre-print
To fill this gap, we performed a survey to analyse the relevant studies published since 2006. We first provide an example to illustrate how deep learning techniques are used in SE.  ...  There are many factors, e.g., deep learning model selection, internal structure differences, and model optimization techniques, that may have an impact on the performance of DNNs applied in SE.  ...  [36, 42, 75, 80, 103] . 7.6.1 Code clone detection.  ... 
arXiv:2011.14597v1 fatcat:pcyg6zbnm5bc3g4yhjomcnye3y

An Empirical Study on the Usage of Transformer Models for Code Completion [article]

Matteo Ciniselli, Nathan Cooper, Luca Pascarella, Antonio Mastropaolo, Emad Aghajani, Denys Poshyvanyk, Massimiliano Di Penta, Gabriele Bavota
2021 arXiv   pre-print
We present a large-scale study exploring the capabilities of state-of-the-art Transformer-based models in supporting code completion at different granularity levels, including single tokens, one or multiple  ...  Thus, little is known about the performance of state-of-the-art code completion approaches in more challenging scenarios in which, for example, an entire code block must be generated.  ...  To build the Android dataset we adopted a similar procedure. We cloned the set of 8,431 open-source Android apps from GitHub available in the AndroidTimeMachine dataset [28] .  ... 
arXiv:2108.01585v1 fatcat:le2gets3rzctxd5wlobbiooium

Android Mobile Malware Detection Using Machine Learning: A Systematic Review

Janaka Senanayake, Harsha Kalutarage, Mhd Omar Al-Kadri
2021 Electronics  
With the increasing use of mobile devices, malware attacks are rising, especially on Android phones, which account for 72.2% of the total market share.  ...  Finally, the ML-based methods for detecting source code vulnerabilities are discussed, because it might be more difficult to add security after the app is deployed.  ...  The empirical analysis conducted in [112] identified the static software metrics' correlation and the most informative metrics which can be used to find code vulnerability related to Android source codes  ... 
doi:10.3390/electronics10131606 fatcat:jpbsq7j6p5dtrpzkincfi2obpe

Predictive Models in Software Engineering: Challenges and Opportunities [article]

Yanming Yang, Xin Xia, David Lo, Tingting Bi, John Grundy, Xiaohu Yang
2020 arXiv   pre-print
There have been a large number of primary studies that apply predictive models and that present well-preformed studies and well-desigeworks in various research domains, including software requirements,  ...  Predictive models are one of the most important techniques that are widely applied in many areas of software engineering.  ...  [139] conducted an empirical study on six open-source Java systems to investigate the life expectancy of clones.  ... 
arXiv:2008.03656v1 fatcat:fe7ylphujfbobeo3g5yevniiei

Research on Third-Party Libraries in AndroidApps: A Taxonomy and Systematic LiteratureReview [article]

Xian Zhan, Tianming Liu, Lingling Fan, Li Li, Sen Chen, Xiapu Luo, Yang Liu
2021 arXiv   pre-print
Third-party libraries (TPLs) have been widely used in mobile apps, which play an essential part in the entire Android ecosystem. However, TPL is a double-edged sword.  ...  To this end, we conduct the first systematic literature review on Android TPL-related research.  ...  [82] conducted an empirical study regarding the ad library updates in Android apps.  ... 
arXiv:2108.03787v1 fatcat:jnj4kvlkuzg3hbgy4pl5wpvle4

Usage and Attribution of Stack Overflow Code Snippets in GitHub Projects [article]

Sebastian Baltes, Stephan Diehl
2018 arXiv   pre-print
We present results of a large-scale empirical study analyzing the usage and attribution of non-trivial Java code snippets from SO answers in public GitHub (GH) projects.  ...  We found that at most 1.8% of all analyzed repositories containing code from SO used the code in a way compatible with CC BY-SA 3.0.  ...  et al, 2014) , the code clone detector we used in the second phase of our study.  ... 
arXiv:1802.02938v4 fatcat:55nhx2mjd5hm5j65okgnmofvhy

A Survey on Machine Learning Techniques for Source Code Analysis [article]

Tushar Sharma, Maria Kechagia, Stefanos Georgiou, Rohit Tiwari, Federica Sarro
2021 arXiv   pre-print
Objective: We aim to summarize the current knowledge in the area of applied machine learning for source code analysis.  ...  A large number of studies poses challenges to the community to understand the current landscape.  ...  Studies in this category prepare a dataset containing source code samples classified as clones or non-clones.  ... 
arXiv:2110.09610v1 fatcat:jc6c3jnxcbekfbssyy7hn3zwxa
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