Unintentional bugs to vulnerability mapping in Android applications

Android is the most widely used mobile operating system (OS). A large number of third-party Android application (app) markets have emerged. The absence of third-party market regulation has prompted research institutions to propose different malware detection techniques. However, due to improvements of malware itself and Android system, it is difficult to design a detection method that can efficiently and effectively detect malicious apps for a long time. Meanwhile, adopting more features will increase the complexity of the model and the computational cost of the system. Permissions play a vital role in the security of the Android apps. Term Frequency—Inverse Document Frequency (TF-IDF) is used to assess the importance of a word for a file set in a corpus. The static analysis method does not need to run the app. It can efficiently and accurately extract the permissions from an app. Based on this cognition and perspective, in this paper, a new static detection method based on TF-IDF and Machine Learning is proposed. The system permissions are extracted in Android application package’s (Apk’s) manifest file. TF-IDF algorithm is used to calculate the permission value (PV) of each permission and the sensitivity value of apk (SVOA) of each app. The SVOA and the number of the used permissions are learned and tested by machine learning. 6070 benign apps and 9419 malware are used to evaluate the proposed approach. The experiment results show that only use dangerous permissions or the number of used permissions can’t accurately distinguish whether an app is malicious or benign. For malware detection, the proposed approach achieve up to 99.5% accuracy and the learning and training time only needs 0.05s. For malware families detection, the accuracy is 99.6%. The results indicate that the method for unknown/new sample’s detection accuracy is 92.71%. Compared against other state-of-the-art approaches, the proposed approach is more effective by detecting malware and malware families.

Bugs in software are costly and difficult to find and fix. In recent years, many tools and techniques have been developed for automatically finding bugs by analyzing source code or intermediate code statically (at compile time). Different tools and techniques have different tradeoffs, but the practical impact of these tradeoffs is not well understood. In this paper, we apply five bug finding tools, specifically Bandera, ESC/Java 2, FindBugs, JLint, and PMD, to a variety of Java programs. By using a variety of tools, we are able to cross-check their bug reports and warnings. Our experimental results show that none of the tools strictly subsumes another, and indeed the tools often find nonoverlapping bugs. We discuss the techniques each of the tools is based on, and we suggest how particular techniques affect the output of the tools. Finally, we propose a meta-tool that combines the output of the tools together, looking for particular lines of code, methods, and classes that many tools warn about.

In computer science education, the analysis of source code with errors is of interest as programming errors may give a hint to misconceptions. The analysis of misconceptions can help teachers to improve their exercises and lessons.The semantic analysis of texts or video sequences could lead to different, subjective interpretation. This problem also effects source code errors, which could contain semantic errors.In different projects, we were confronted with a lot of incorrect source codes, which were written by students of universities and secondary schools. A first analysis of these errors led to a categorization of errors regarding missing competencies.To avoid mainly subjective interpretation of source code errors a standardized method for categorizing errors, which could also be applied by a practitioner, has to be developed and justified. Categorizing texts or source code errors is a matter of semantics, because text or code elements have to be interpreted. Thus, a qualitative content analysis is most suitable. In this paper we explain the difference between errors and misconceptions and present our adaption of the qualitative content analysis of Mayring to source code errors.

Recently, with the purpose of helping developers reduce the needed effort to build highly secure software, researchers have proposed a number of vulnerable source code prediction models that are built on different kinds of features. Identifying security vulnerabilities along with differentiating non-vulnerable from a vulnerable code is not an easy task. Commonly, security vulnerabilities remain dormant until they are exploited. Software metrics have been widely used to predict and indicate several quality characteristics about software, but the question at hand is whether they can recognize vulnerable code from non-vulnerable ones. In this work, we conduct a study on static code metrics, their interdependency, and their relationship with security vulnerabilities in Android applications. The aim of the study is to understand: (i) the correlation between static software metrics; (ii) the ability of these metrics to predict security vulnerabilities, and (iii) which are the most informative and discriminative metrics that allow identifying vulnerable units of code.

Android applications (APPS) are in widespread use and have enriched our life. To ensure the quality and security of the apps, many approaches have been proposed in recent years for detecting bugs and defects in the apps, of which program analysis is a major one. This paper mainly makes an investigation of existing works on the analysis of Android apps. We summarize the purposes and proposed techniques of existing approaches, and make a taxonomy of these works, based on which we point out the trends and challenges of research in this field. From our survey, we sum up four main findings: (1) program analysis in Android security field has gained particular attention in the past years, the fields of functionality and performance should also gain proper attention; the infrastructure that supports detection of various defects should be enriched to meet the industry’s need; (2) many kinds of defects result from developers’ misunderstanding or misuse of the characteristics and mechanisms in Android system, thus the works that can systematically collect and formalize Android recommendations are in demand; (3) various program analysis approaches with techniques in other fields are applied in analyzing Android apps; however, they can be improved with more precise techniques to be more applicable; (4) The fragmentation and evolution of Android system blocks the usability of existing tools, which should be taken into consideration when developing new approaches.

Development without any defect is unsubstantial. Timely detection of software defects favors the proper resource utilization saving time, effort and money. With the increasing size and complexity of software, demand for accurate and efficient prediction models is increasing. Recently, search-based techniques (SBTs) have fascinated many researchers for Software Defect Prediction (SDP). The goal of this study is to conduct an empirical evaluation to assess the applicability of SBTs for predicting software defects in object-oriented (OO) softwares. In this study, 16 SBTs are exploited to build defect prediction models for 13 OO software projects. Stable performance measures — GMean, Balance and Receiver Operating Characteristic-Area Under Curve (ROC-AUC) are employed to probe into the predictive capability of developed models, taking into consideration the imbalanced nature of software datasets. Proper measures are taken to handle the stochastic behavior of SBTs. The significance of results is statistically validated using the Friedman test complied with Wilcoxon post hoc analysis. The results confirm that software defects can be detected in the early phases of software development with help of SBTs. This paper identifies the effective subset of SBTs that will aid software practitioners to timely detect the probable software defects, therefore, saving resources and bringing up good quality softwares. Eight SBTs — sUpervised Classification System (UCS), Bioinformatics-oriented hierarchical evolutionary learning (BIOHEL), CHC, Genetic Algorithm-based Classifier System with Adaptive Discretization Intervals (GA_ADI), Genetic Algorithm-based Classifier System with Intervalar Rule (GA_INT), Memetic Pittsburgh Learning Classifier System (MPLCS), Population-Based Incremental Learning (PBIL) and Steady-State Genetic Algorithm for Instance Selection (SGA) are found to be statistically good defect predictors.

SummaryThis paper investigates the failures exposed in mobile apps by the mobile‐specific event of changing the screen orientation. We focus on GUI failures resulting in unexpected GUI states that should be avoided to improve the apps quality and to ensure better user experience. We propose a classification framework that distinguishes 3 main classes of GUI failures due to orientation changes and exploit it in 2 studies that investigate the impact of such failures in Android apps. The studies involved both open‐source and apps from Google Play that were specifically tested exposing them to orientation change events. The results showed that more than 88% of these apps were affected by GUI failures, some classes of GUI failures were more common than others, and some GUI objects were more frequently involved. The app source code analysis allowed us to identify 6 classes of common faults causing specific GUI failures.

The presence of malicious software (malware), for example, in Android applications (apps), has harmful or irreparable consequences to the user and/or the device. Despite the protections app stores provide to avoid malware, it keeps growing in sophistication and diffusion. In this paper, we explore the use of machine learning (ML) techniques to detect malware in Android apps. The focus is on the study of different data pre-processing, dimensionality reduction, and classification techniques, assessing the generalization ability of the learned models using public domain datasets and specifically developed apps. We find that the classifiers that achieve better performance for this task are support vector machines (SVM) and random forests (RF). We emphasize the use of feature selection (FS) techniques to reduce the data dimensionality and to identify the most relevant features in Android malware classification, leading to explainability on this task. Our approach can identify the most relevant features to classify an app as malware. Namely, we conclude that permissions play a prominent role in Android malware detection. The proposed approach reduces the data dimensionality while achieving high accuracy in identifying malware in Android apps.

As the number of android applications (apps) available in the market are increasing rapidly, various types of security attacks using the android apps are also increasing with the same pace. The ransomware attack is one of these kind of security attacks in which the attackers locks the user's phone, encrypts user's data or blocks the user's access to their own data and threatens the user to pay a ransom to gain the access back. This cyber-threat is terrorizing the world from many years as it performs mimicry attacks i. e. combination of encryption & locking attacks. Android devices are more prone to these ransomware attacks compared to Windows and IOS devices. RansomwareElite is an android application which detects the presence of ransomware in the apps installed on an android device by checking the presence of any threatening text in app code or by verifying the permissions requested by the app from the user. In this paper, we focused on improving the performance of RansomwareElite app by extending its features. Now, the RansomwareElite app also searches the presence of any threatening image or file containing threatening text by analyzing the Android Package Kit(APK) file of android app. Moreover, it also detects some specific methods and classes in the code of the APK which could be used for locking the device and checks some specific permissions requested, for uninstalled apps now. Further, it maintains a database on the online server for the records of all the suspicious and ransomware apps detected by RansomwareElite. We have tested RansomwareElite with 9 Test Apps which are manually created based on the features of ransomware family and on 48 android devices. After analyzing the test results, we have found that the performance of RansomwareElite is improved after incorporating the new features and RansomwareElite app detects the presence of ransomware in installed as well as uninstalled apps present on an android's device in an efficient manner.

With the increasing popularity of the Android platform, we have seen the rapid growth of malicious Android applications recently. Considering that the heavy use of applications on mobile phones such as games, emails, and social network services has become a crucial part of our daily life, we have become more vulnerable to malicious applications running on mobile devices. To alleviate this hostile environment of Android mobile applications, we propose a malware detection approach that (1) extracts both built-in permissions and custom permissions requested by Android apps from their Manifest.xml and (2) applies the permissions and a Random Forest classifier to Android applications for classifying them into benign and malicious. The Random Forest classifier learns a model using the permissions to classify the input dataset of 45,311 Android applications. In the learned model, an optimal subset of permissions has been identified and then using the subset of permissions we could achieve 94.23% accuracy in detecting malware.

Stroke is currently one of the leading causes of mortality worldwide. Extensive research has identified several risk factors associated with stroke, which have been extensively studied to enhance prediction and categorization of the disease. Machine learning (ML) has proven to be a powerful tool for analyzing vast amounts of data, enabling accurate predictions and informed decision-making. Researchers are actively working to develop automated ML models for stroke prediction with the aim of facilitating early interventions and saving lives. As the global population ages and the number of individuals at risk for stroke continues to rise, the need for accurate prediction algorithms has become increasingly critical. The widespread adoption of Android applications provides an opportunity to make predictive tools accessible to a broader audience. This study investigates the application of ML algorithms in stroke risk prediction and demonstrates their integration into a functional Android application. The dataset used for model development was sourced from Kaggle, where a significant imbalance was observed with a ratio of 19:1 between instances of no stroke and stroke. To address this imbalance, Synthetic Minority Oversampling Technique (SMOTE) analysis was employed, ensuring a balanced dataset for training the models. Eight robust ML algorithms were utilized to develop predictive models. Among these, the Ensemble method leveraging a voting classifier achieved the best performance, attaining an accuracy of 95% and a recall of 95%. TensorFlow was used to integrate the machine learning model into an Android application, enabling real-time predictions. The Android app, developed in Java and built using the Android Studio platform, is designed to offer recommendations for stroke prevention and management. This user-friendly application aims to enhance accessibility to stroke risk predictions, empowering users to take proactive measures to safeguard their health..

Cardiovascular disease (CVD) patients need continuous monitoring of bio-signals and regular hospital visit to improve health conditions. A portable health monitoring device can provide continuous monitoring of chronic diseases. In this paper, a low-cost health monitoring system is introduced to observe Electrocardiogram (ECG), body temperature and heart-beat. This research illustrates the use of android smartphone and Android applications to process and visualize the ECG signal, heart rate, and temperature. An android application is developed to monitor, store and share this biomedical signal data with an expert to get the fastest treatment. The system can also store the previous data to observe patient's history easily whenever necessary. The design strategy, experimental data with android apps, smartphone synchronization, real-time monitoring have been presented in this paper. The prototype can be utilized to control heart diseases for the people in the underdeveloped area.

Responsiveness is one of the most important properties of Android applications to both developers and users. Recent survey on automated improvement of non-functional properties of Android applications shows there is a gap in the application of search-based techniques to improve responsiveness. Therefore, we explore the use of genetic improvement (GI) to achieve this task. We extend Gin, an open source GI framework, to work with Android applications. Next, we apply GI to four open source Android applications, measuring frame rate as proxy for responsiveness. We find that while there are improvements to be found in UI-implementing code (up to 43%), often applications’ test suites are not strong enough to safely perform GI, leading to generation of many invalid patches. We also apply GI to areas of code which have highest test-suite coverage, but find no patches leading to consistent frame rate reductions. This shows that although GI could be successful in improvement of Android apps’ responsiveness, any such test-based technique is currently hindered by availability of test suites covering UI elements.

Third-party payment libraries (TPLs) are widely used in Android applications to facilitate in-app transactions, yet their security implications remain largely underexplored. In this paper, we present a novel approach for automated detection and security analysis of payment libraries in Android applications. Our tool, PayScan, employs byte-pattern analysis and heuristic scanning techniques to identify TPLs and then it assesses their security posture. Additionally, the tool integrates three independent security scanners. We analyzed a dataset of 10,553 Android applications, detecting 18 payment libraries and evaluating their security and privacy risks. Our findings indicate that 71.7% of applications use outdated payment libraries, with some SDK versions being over four years old. Additionally, we identified 397 private key leaks across 212 applications. The security scanners detected over 20,000 vulnerabilities, including critical issues such as SSL misconfigurations, WebView XSS, and weak cryptographic implementations. We compare our detection approach against LibScout and LibRadar, demonstrating its practical performance in detecting payment libraries, including in obfuscated applications. This study reveals important security risks in mobile payment ecosystems and emphasizes the value of continued monitoring of third-party payment libraries. The proposed tool offers a scalable solution for detection and analysis, providing practical utility for researchers, developers, and auditors focused on financial application security.

Ageing is often associated with sleep disorders. This study aimed to describe the effect of using an Android application on family support in the sleep patterns of the elderly. The study used a cross-sectional survey design conducted among the elderly to assess the use of android-based applications in identifying the sleep pattern experienced and the perceived-family support. The research sample was 98 people selected by convenience sampling and snowball sampling techniques. Researchers enhanced family support through an Android app. The mean value of sleep patterns before using the app was 4.25 and after using the app was 8.35. The correlation test showed that perceived-family support had a significantly strong correlation with sleep patterns with a positive correlation (correlation coefficient 0.779, p-value 0.001; Table 5). The higher the family support perceived by the elderly, the better their sleep pattern. The study concluded that the elderly at risk of dementia often experience disturbed sleep patterns. Families play an important role in helping the improvement of elderly sleep patterns. Android applications can be a technological innovation in increasing family support for the elderly.
 Keywords: android-based application, sleep patterns, family support