Comparative analysis of detecting over-claim permissions from android apps

Android introduces a new permission model that allows apps to request permissions at runtime rather than at the installation time since 6.0 (Marshmallow, API level 23). While this runtime permission model provides users with greater flexibility in controlling an app's access to sensitive data and system features, it brings new challenges to app development. First, as users may grant or revoke permissions at any time while they are using an app, developers need to ensure that the app properly checks and requests required permissions before invoking any permission-protected APIs. Second, Android's permission mechanism keeps evolving and getting customized by device manufacturers. Developers are expected to comprehensively test their apps on different Android versions and device models to make sure permissions are properly requested in all situations. Unfortunately, these requirements are often impractical for developers. In practice, many Android apps suffer from various runtime permission issues (ARP issues). While existing studies have explored ARP issues, the understanding of such issues is still preliminary. To better characterize ARP issues, we performed an empirical study using 135 Stack Overflow posts that discuss ARP issues and 199 real ARP issues archived in popular open-source Android projects on GitHub. Via analyzing the data, we observed 11 types of ARP issues that commonly occur in Android apps. For each type of issues, we systematically studied: (1) how they can be manifested, (2) how pervasive and serious they are in real-world apps, and (3) how they can be fixed. We also analyzed the evolution trend of different types of issues from 2015 to 2020 to understand their impact on the Android ecosystem. Furthermore, we conducted a field survey and in-depth interviews among the practitioners from open-source community and industry, to gain insights from practitioners’ practices and learn their requirements of tools that can help combat ARP issues. Finally, to understand the strengths and weaknesses of the existing tools that can detect ARP issues, we built <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ARPBench</small> , an open benchmark consisting of 94 real ARP issues, and evaluated the performance of three available tools. The experimental results indicate that the existing tools have very limited supports for detecting our observed issue types and report a large number of false alarms. We further analyzed the tools’ limitations and summarized the challenges of designing an effective ARP issue detection technique. We hope that our findings can shed light on future research and provide useful guidance to practitioners.

Android security implements a permission model to protect a user’s most sensitive data. These permissions regulate an app’s access to different aspects of the device, however, a fatal flaw of Android’s permission model is that it relies on the discretion of the user to determine which apps are granted permissions and which are not with limited assistance in their choice from the device. As a result, a specialized type of malware known as a logic bomb has affected Android devices. These logic bombs are designed to execute malicious code when activated by triggers, and can be designed to take advantage of users who poorly vet their applications or even hide themselves inside applications that appear to be benign. On Android, logic bombs usually carry out malicious intent by violating permissions, using a permission for some activity the user never intended. We have found 18 different permissions that applications can violate to carry out some form of malicious intent, and have developed an app, called HyenaDroid, to violate each of these permissions and create logic bombs. This provides evidence that the current Android security revolving around permissions is in need of either an update to the permissions model, or an additional system to assist the user with navigating the Android permissions model. Our research also proposes such a system, PRAST. PRAST is designed as a modular system, combining a level of efficiency that can be run during the download on an Android device, along with the effectiveness and accuracy of external analysis systems.

Comparing Phoneme Speech Recordings and Acoustic App Data Capture Experience for Android and iOS Mobile Device Users in the Large Decentralized AcRIS Study.

A Wireless Health Monitoring System based on Android Operating System

The Android operating system, dominating over 85% of the mobile market through open-source flexibility, suffers from intrinsic vulnerabilities. The APK(Android Package Kit) parsability and Smali code modifiability enable attackers to decompile applications via tools. This facilitates widespread repackaging-malicious actors inject payloads or tamper with functionality, redistributing counterfeit applications(apps) through third-party markets. These practices cause dual damage. Developers face code theft and revenue diversion, while users endure privacy leaks, financial fraud, and device compromise. Consequently, accurate repackaging detection has become critical. This paper reviews recent progress in repackaging detection techniques for Android applications. We first outline the fundamental characteristics of Android apps and then examine detection methods based on code analysis and resource similarity. Frequently used Android app datasets and evaluation metrics for measuring the effectiveness of repackaging detection methods are also summarized. Finally, we discuss the development trends of repackaging detection techniques and identify future research directions, with the aim of providing meaningful insights and guidance for researchers in this domain.

Objective: Smartphone industry is growing day by day.According to studies, maximum smartphone users watch videos on their smartphones.With the time, android operating system is becoming popular due to the applications which are based on android SDKsthat attract the attention of the mobile phone users. Methods: However, with the increase of its popularity video editing in android devices is becoming a complex task for many reasons as well like poor video quality after editing, slow editing process, editing tool crashes while editing etc. There is a need of an attractivetool and an efficient environmentto deal with such complex tasks. Findings: This study discusses the novel and easy-to-use interface of video editing in android devices. The developed tool can perform video encoding which generates number of frames per second. We have applied image processing techniques to add images or text to any frame of video and can apply different effects like covers, sepia, gamma, rotation, snow. Then the tool can regenerate video from the frames produced after editing and application of various effects. Application:The resultant video will be quality conscious and can be generated in multiple formats like MP4, 3GP, and MOV reliably. Keywords: Android Phones, Digital Video, Image Processing, Smartphones, Video Processing, Video Editing

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.

Millions of Naira are being laundered by criminals through internet fraud, which deceives the victim with a false presentation, or hacking, which involves breaking information system security to steal financial information or data. Cybercrime, more specifically internet fraud, has become a common problem facing the world. In any case, this only occurred if the attacker was able to obtain the victim's account privileges (bank account, credit card information, or login credentials). This work presented an authentication technique that can be inexpensively implemented and used to create a secure architecture that is hard to breach. It can also reduce the amount of overhead that is placed on the authentication server during an attempt at authentication. The possibility of breaking the authentication technique was calculated using probability theory, and the mobile-based authentication algorithm was implemented using Android Studio. The result of this research shows the implementations of RatHole algorithm using android operating system which is file management android app that is being protected with RatHole algorithm and the probability of breaking down the algorithm for 1 to 10 authentication method keeps reducing by averagely 0.12. The implemented Rat-Hole on android devices also consumed 20 to 30 megabytes of data very less than memory consumption of recent research based on block chain solution which consumed 1 gigabyte to 50 gigabyte of memory.

In this digital era, integrating Internet of Things (IoT) technology with mobile applications has revolutionised various industries, including weather monitoring. This project presents the development of a weather monitoring app that harnesses the power of IoT and Kotlin programming language. The app utilizes a weather API to obtain real-time weather data and seamlessly communicates with an IoT device, enabling users to access and display weather information conveniently. project begins with a detailed analysis of requirements, outlining the desired features and functionalities of the app. The weather API selection process is discussed, and the chosen API is integrated into the Kotlin app to fetch weather data. Additionally, location services are implemented to offer location-based weather updates. An IoT device, such as a Raspberry Pi or Arduino, is selected for displaying the weather data. The IoT device is connected to the internet and programmed to fetch weather data from the app. Communication protocols between the Android app and the IoT device are established, ensuring smooth data exchange. The app's user interface is designed to provide an intuitive experience for users, with weather data and location information prominently displayed. Error handling mechanisms are implemented to deal with potential API downtimes or communication failures between the app and the IoT device. Through comprehensive testing, the app's functionality, performance, and reliability are assessed. Security measures are implemented to safeguard user data and privacy during data transmission. The result is a robust and user-friendly weather-monitoring app that seamlessly integrates IoT technology with Kotlin's efficiency and flexibility. The app empowers users to access real-time weather updates, enhancing their preparedness for weather changes and enabling informed decision-making for various outdoor activities. With the proliferation of IoT and its impact on various sectors, this project serves as a valuable example of leveraging IoT and Kotlin to develop innovative mobile applications for everyday use. Keywords – Weather Monitoring App, Kotlin, IoT, Real- time Weather Data, Weather API

Modern smartphone operating systems (e.g., Android 6.0 and later versions) employ an ask-on-first-use policy to regulate app permissions. To assist users in policy decisions, relevant efforts have been focusing on leveraging contexts to capture users' privacy preferences. However, these techniques have various limitations, such as heavily relying on users' historical decisions on granting permissions, ignoring the fact that users are not experts on privacy protection, and hard to determine whether a permission shall be granted. To address this problem, we propose an autonomous permission recommendation system, AutoPer+, to automatically recommend users the permission decisions at runtime. The main insight of our proposed system is that the natural language description of an app reflects its functionality and its similarity to other apps, and thus can be used to analyze whether a permission is needed indeed by it, and the apps similar to it. First, we introduce a multi-topic model into app functionality mining, and design a topic-permission mapper for the proposed recommendation system. Then we propose a deep semi-supervised machine using Long Short-Term Memory (LSTM) neural networks to identify similar apps, by which we can explore privacy permission usage in a cluster of apps. Finally, we capture a trade-off between privacy and utility to present a systematic recommendation. In addition to the permission decision (“Allow” or “Deny”), the permission explanations are also provided for users to make decisions (called “Ask”). We evaluate the proposed system via extensive comparison experiments on 31,023 Android apps. The results show that our approach achieves an accuracy of 84.1%. Moreover, we conduct user studies via installing AutoPer+ in the participants' own Android devices. We receive positive responses from the participants, which implies AutoPer+ is potentially for real-world deployment for enhancing current permission recommendation.

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.

Modern car infotainment systems allow users to connect an Android device to the vehicle. The device then interacts with the hardware of the car, hence providing new interaction mechanisms to the driver. However, this can be misused and become a major security breach into the car, with subsequent security concerns: the Android device can both read sensitive data (speed, model, airbag status) and send dangerous commands (brake, lock, airbag explosion). Moreover, this scenario is unsettling since Android devices connect to the cloud, opening the door to remote attacks by malicious users or the cyberspace. The OpenXC platform is an open-source API that allows Android apps to interact with the car’s hardware. This article studies this library and shows how it can be used to create injection attacks. Moreover, it introduces a novel static analysis that identifies such attacks before they actually occur. It has been implemented in the Julia static analyzer and finds injection vulnerabilities in actual apps from the Google Play marketplace.

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

As android devices have increased in number in the past few years, the android operating system has started dominating the smartphone market. The vast spread of android across all the devices has made security an important issue as the android users continue to grow exponentially. The security of android platform has become the need of the hour in view of increase in the number of malicious apps and thus several studies have emerged to present the detection approaches. In this paper, we review the android components to propose a threat model that illustrates the possible threats that are present in the android. We also present the attack taxonomy to illustrate the possible attacks at various layers of the android architecture. Experiments demonstrating the feature extraction and classification using machine earning algorithms have also been performed.

Mobile health or better known as m-health is the use of mobile devices for medical practices. M-health technology involving mobile telecommunications infrastructure in providing medical services to patients. Mobile technology has developed very rapidly, where the cost of mobile devices getting cheaper, so that mobile devices become an essential tool of communication and information technology. Android devices is a major preoccupation in the development of the general computing world. Android is a comprehensive device, because Android based Linux operating system that has the ability to regulate the use of the device, memory and process. The device is designed to measure oximetry and blood pressure. Communication between the device and a mobile phone using Bluetooth communication is embedded in a mobile phone that uses the Android operating system 2.3.3. The function of this mobile phone to display the data after the measurement, the transmit data that has been measured by SMS, e-mail and a local database. The device is designed to be used personally or to support the use of the hospital.