Android app repackaging detection: A comprehensive survey

Android devices are popular nowadays due to their rich functionality and good performance. Android provides a permission model that supports user privacy by protecting restricted data access and actions on the Android operating system. Based on the app's defined functionality, any app must get users' permission to access different types of restricted data or resources. However, some developers request additional permissions other than the app's functionality, known as over-claim permissions. These over-claim permissions may steal sensitive information from users, Which is one of the leading privacy issues. Looking towards its criticality, many researchers have started working on detecting over-claim permission. This paper conducts a comprehensive survey of various approaches to identify and handle over-claim permissions. It also discusses the limitations of existing techniques and further research scope in this direction, which may help researchers define a path for designing new methods or optimizing existing strategies to resolve over-claim permission problems satisfactorily.

In the last decade, we have witnessed an unprecedented increase in the adoption of mobile devices. A substantial number of these devices run on the Android operating system. Android is an open-source operating system based on Linux, which provides a permission-based security model that demands each application to request explicit permissions (approved by the user) before it can be installed to run. However, end users cannot estimate application risk, so the user's decision is almost completely unrelated to the application risk level. Moreover, due to the platform openness and the plethora of available software, dangerous apps (even if not necessarily malware) are now also very common for Android devices. In this paper we propose a new approach and a tool to evaluate the potential risk of Android application packages to help end user security awareness. The tool exploits both static and dynamic analysis techniques. It examines the correlations between app required permissions and the invoked APIs, as well as the contents in the package, and subsequently it uses a dynamic analysis module to confirm the suspicions proposed by static modules. The risk activities detected by analysis modules are then mapped into finer-grained risk categories and further evaluated using the fuzzy logic algorithm. Fuzzy logic aims to deal with uncertainty which arises from the nature of automatic analysis, as not all detected activities intend to cause harm. For the sake of both tech-uninterested and tech-savvy users, the results contain a simple numerical value showing the risk level plus a detailed report of detected activities and their mappings to the risk categories. Finally, we tested our software on a large set of real-world samples, demonstrating its efficiency and showing a reasonable capacity to identify and evaluate the potential risk of application packages, both the benign and the malicious ones.

Help Without Borders: How the Google Android Case Threatens to Derail the Limited Scope of the Obligation to Assist Competitors

The packaging model of Android apps requires the entire code necessary for the execution of an app to be shipped into one single apk file. Thus, an analysis of Android apps often visits code which is not part of the functionality delivered by the app. Such code is often contributed by the common libraries which are used pervasively by all apps. Unfortunately, Android analyses, e.g., for piggybacking detection and malware detection, can produce inaccurate results if they do not take into account the case of library code, which constitute noise in app features. Despite some efforts on investigating Android libraries, the momentum of Android research has not yet produced a complete set of common libraries to further support in-depth analysis of Android apps. In this paper, we leverage a dataset of about 1.5 million apps from Google Play to harvest potential common libraries, including advertisement libraries. With several steps of refinements, we finally collect by far the largest set of 1,113 libraries supporting common functionality and 240 libraries for advertisement. We use the dataset to investigates several aspects of Android libraries, including their popularity and their proportion in Android app code. Based on these datasets, we have further performed several empirical investigations to confirm the motivations behind our work.

The advancement of neural-based models has driven significant progress in modern code intelligence, accelerating the development of intelligent programming tools such as code assistants and automated software engineering systems. This study presents a comprehensive and systematic survey of neural methods for programming tasks within the broader context of software development. Guided by six research questions, this study synthesizes insights from more than 250 scientific papers, the majority of which were published between 2015 and 2025, with earlier foundational works (dating back to the late 1990s) included for historical context. The analysis spans 18 major programming tasks, including code generation, code translation, code clone detection, code classification, and vulnerability detection. The survey methodologically examines the development and evolution of neural approaches, the datasets employed, and the performance evaluation metrics adopted in this field. It traces the progress in neural techniques from early code modeling approaches to advanced Code-specific Large Language Models (Code LLMs), emphasizing their advantages over traditional rule-based and statistical methods. A taxonomy of evaluation metrics and a categorized summary of datasets and benchmarks reveal both progress and persistent limitations in data coverage and evaluation practices. The review further distinguishes neural models designed for natural language processing and programming languages, highlighting the structural and functional characteristics that influence model performance. Finally, the study discusses emerging trends, unresolved challenges, and potential research directions, underscoring the transformative role of neural-based architectures, particularly Code LLMs, in enhancing programming and software design activities and shaping the future of AI-driven software development.

Diving deeper into underwater image enhancement: A survey

Introduction: In order to assess how well conventional and hybrid pitch detection techniques perform in speech processing applications, a comparative analysis of the two types of methods is conducted. Method: A proposed hybrid approach, Proposed PEF+CEP, is examined alongside five traditional algorithms, namely Normalized Correlation Function (NCF), Pitch Estimation Filter (PEF), Log- Harmonic Summation (LHS), Summation of Residual Harmonics (SRH) and Cepstrum Pitch Determination (CEP). The effectiveness is evaluated using performance metrics like accuracy, specificity, sensitivity, and Gross Pitch Error (GPE). Results and Discussion: Our findings show that the accuracy and specificity of the traditional methods are impressive; the accuracy and sensitivity of the suggested hybrid method surpass their performance, with an astounding 98.8% accuracy and 99.2% sensitivity. Conclusion: Furthermore, the Proposed PEF+CEP method is a promising solution for accurate and dependable pitch detection in speech processing applications because it strikes a strong balance between computational efficiency and robustness. These results open up new avenues for research in the field of speech processing and demonstrate the potential of hybrid approaches.

The app economy is largely reliant on data collection as its primary revenue model. To comply with legal requirements, app developers are often obligated to notify users of their privacy practices in privacy policies. However, prior research has suggested that many developers are not accurately disclosing their apps’ privacy practices. Evaluating discrepancies between apps’ code and privacy policies enables the identification of potential compliance issues. In this study, we introduce the Mobile App Privacy System (MAPS) for conducting an extensive privacy census of Android apps. We designed a pipeline for retrieving and analyzing large app populations based on code analysis and machine learning techniques. In its first application, we conduct a privacy evaluation for a set of 1,035,853 Android apps from the Google Play Store. We find broad evidence of potential non-compliance. Many apps do not have a privacy policy to begin with. Policies that do exist are often silent on the practices performed by apps. For example, 12.1% of apps have at least one location-related potential compliance issue. We hope that our extensive analysis will motivate app stores, government regulators, and app developers to more effectively review apps for potential compliance issues.

Malware family classification is grouping malware samples that have the same or similar characteristics into the same family. It plays a crucial role in understanding notable malicious patterns and recovering from malware infections. Although many machine learning approaches have been devised for this problem, there are still several open questions including, “Which features, classifiers, and evaluation metrics are better for malware familial classification”? In this paper, we propose a machine learning approach to Android malware family classification using built-in and custom permissions. Each Android app must declare proper permissions to access restricted resources or to perform restricted actions. Permission declaration is an efficient and obfuscation-resilient feature for malware analysis. We developed a malware family classification technique using permissions and conducted extensive experiments with several classifiers on a well-known dataset, DREBIN. We then evaluated the classifiers in terms of four metrics: macrolevel F1-score, accuracy, balanced accuracy (BAC), and the Matthews correlation coefficient (MCC). BAC and the MCC are known to be appropriate for evaluating imbalanced data classification. Our experimental results showed that: (i) custom permissions had a positive impact on classification performance; (ii) even when the same classifier and the same feature information were used, there was a difference up to 3.67% between accuracy and BAC; (iii) LightGBM and AdaBoost performed better than other classifiers we considered.

Dynamic privacy leakage analysis of Android third-party libraries

Personal, commercial, and economic impacts of financial fraud are significant. Traditional rule-based fraud detection systems often generate false positives and struggle to adapt to new fraud methods. ML algorithms are examined as an alternative to traditional financial fraud detection methods in this chapter. It explores how supervised, unsupervised, and hybrid models may identify financial data abnormalities and fraud patterns. Money laundering, payments fraud, and other financial crimes are covered in this chapter. Fraud detection is complicated by data imbalance, privacy concerns, and model scalability. It also shows how AI, blockchain, and predictive analytics will fight fraud and secure financial institutions. This chapter reviews how new technology is changing financial crime prevention, using examples from credit card and online retail fraud.

This paper presents a human machine interface (HMI) system to monitor and analyze heart issues and physiological activities by extracting and processing photoplethysmography (PPG) signal from a pulse sensor. This noninvasive framework estimates heart rate in beats per minute (BPM) and heart rate variability (HRV) of the autonomic sinus activity of a human body. The ace promising feature is to introduce a customized android application (app) named Impulse in order to observe the cardiac activities in a consistent manner. The data transformation from the sensor to the android app takes place via a wireless communication protocol (bluetooth interface). This paper documents MATLAB simulations for filtering raw PPG signals. Physiology of PPG, developed PPG extracting device, features, user manual and performance evaluation of the android app and MATLAB simulations are articulated in this paper.

X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learning-based object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.

The visual recognition of Android malicious applications (Apps) is mainly focused on the binary classification using grayscale images, while the multiclassification of malicious App families is rarely studied. If we can visualize the Android malicious Apps as color images, we will get more features than using grayscale images. In this paper, a method of color visualization for Android Apps is proposed and implemented. Based on this, combined with deep learning models, a multiclassifier for the Android malicious App families is implemented, which can classify 10 common malicious App families. In order to better understand the behavioral characteristics of malicious Apps, we conduct a comprehensive manual analysis for a large number of malicious Apps and summarize 1695 malicious behavior characteristics as customized features. Compared with the App classifier based on the grayscale visualization method, it is verified that the classifier using the color visualization method can achieve better classification results. We use four types of Android App features: classes.dex file, sets of class names, APIs, and customized features as input for App visualization. According to the experimental results, we find out that using the customized features as the color visualization input features can achieve the highest detection accuracy rate, which is 96% in the ten malicious families.

Migrating to Android for iOS Developers gives youas an experienced native iOS app developerthe skills to learn native Android apps development from scratch. Starting with preparing your Android integrated development environment and introducing just enough Android application framework fundamentals, youll understand how to create a simple but meaningful HelloAndroid project immediately. This book provides the guidelines and tutorial projects to show you how to translate your existing iOS app to the Android platform. Youll use your mobile app knowledge to structure your Android apps in a similar way to how you would structure your iOS apps. To implement use cases with detailed screens, the most common mobile topics are discussed, including user interfaces, managing data, and networking with remote services. As you move through the book, youll create Android apps with rich UI components to handle common CRUD operations locally and remotely. There are many Android goodies described in the book. Instead of relying on routine text descriptions, youll discover the uniqueness of Android and appreciate the many features that are unique to the platform. This book also explores more powerful mobile UX patterns that are commonly used on the iOS and Android platforms. When you finish reading Migrating to Android for iOS Developers, youll be an Android developer as well as an iOS developer. And, you will be fully convinced you can do everything in Android that you can do in iOS. What youll learn How to maximize your existing iOS mobile knowledge to learn Android programming skills How to use the Android integrated development environment with the Eclipse ADT plugin How to translate your existing iOS code to Android with the following common mobile topics: Common mobile screen navigation patterns User interface components and UI animations Storing data Networking and using remote services Using system apps Maps and location awareness Mobile search frameworks Mobile analyticsWho this book is for This book is for iOS app developerslike youwho want to port their native iOS app to become an Android app. Also, if you are not an iOS developer, but already familiar with mobile apps, then this book can also help you understand Android development with step-by-step instructions and tutorial projects.