Role mining algorithm evaluation and improvement in large volume android applications

Mobile devices have become widespread computing technology, people prefer to use than desktop devices. These connected devices and their features like an exchange of data, video calling etc. have made our lives simple but, this has also increased data security concerns. At present, Google's Android is dominating the market share of mobile devices; as a result, it has become a big target for malware writers. Android users can download applications from official or third-party stores. Google implements various security policies to ensure secure distribution of applications but third-party application stores have less efficient or no such policies. This makes such markets more attractive for malware writers. This paper investigates Android application security by analysing 1,946 free most downloaded Android applications in the year 2016: 1,300 from Google Play Store and 646 from third-party Android applications. 100 samples from 33 different malware families (with variants) prominent in the Android market during January 2016 to April 2016 were also collected that acted as a template for malware detection. Further, based on detected malware samples, an evaluation-based study on ten anti-malware applications is performed to identify how well they protect users from malware.

The Android Mobiles constitute a large portion of mobile market which also attracts the malware developer for malicious gains. Every year hundreds of malwares are detected in the Android market. Unofficial and Official Android market such as Google Play Store are infested with fake and malicious apps which is a warning alarm for naive user. Guided by this insight, this paper presents the malicious application detection and classification system using machine learning techniques by extracting and analyzing the Android Permission Feature of the Android applications. For the feature extraction, the authors of this work have developed the AndroData tool written in shell script and analyzed the extracted features of 1060 Android applications with machine learning algorithms. They have achieved the malicious application detection and classification accuracy of 98.2% and 87.3%, respectively with machine learning techniques.

With the emergence of Role Based Access Control (RBAC) as the de facto access control model, organizations can now implement and manage many high level security policies. As a means of migration from traditional access control systems to RBAC, different role mining algorithms have been proposed in recent years for finding a minimal set of roles from existing user-permission assignments. While determining such roles, it is often required that certain security objectives are satisfied. A common goal is to enforce the role-usage cardinality constraint, which limits the maximum number of roles any user can have. In this paper, we propose two alternative approaches for role mining with an upper bound on the number of roles that can be assigned to each user, and validate their performance with benchmark data sets.

The rapid growing of the Android application and malware has increased the usage of the application category in Android malware detection and application searching. However, the defects of management of Android Market lead to a great deal of applications miscategorization. Therefore, it's helpful for both organizing the Android Market and Android malware detection to give an approach that can automatically distinguish different categories of the applications. In this paper, we present an effective approach for automatically categorizing Android applications based on Bayesian classification. Considering the category of the application is determined by its function, we extracted the used permissions and strings that can reflect the application function from the application itself and Android Market as classification features. Finally, we conduct experiments with 13005 applications that are composed of 18 categories with Naive Bayes. The evaluation results show that our approach can achieve better accuracy and performance than previous coarse-grained feature extraction methods.

According to International Data Corporation (IDC), Android accounted for 81% of all smartphone shipments in the third quarter of 2013. With the increase in smartphones, mobile application installs from Google Play have reached more than 48 billion app installs as revealed by Sundar Pichai, senior vice president, Chrome and Apps at Google I/O 2013. Thus, Android application development has become increasingly popular with the need of an effective software solution for application development and testing. While most Android applications are developed in Java, the Android framework is quite different fromm typical Java frameworks. Thus, while development or testing of Android applications, the user needs to be aware of all the terms and concepts specific to the Android framework such as activity, views and the Android SDK in general.

While many role mining algorithms have been proposed in recent years, there lacks a comprehensive study to compare these algorithms. These role mining algorithms have been evaluated when they were proposed, but the evaluations were using different datasets and evaluation criteria. In this paper, we introduce a comprehensive framework for evaluating role mining algorithms. We categorize role mining algorithms into two classes based on their outputs; Class 1 algorithms output a sequence of prioritized roles while Class 2 algorithms output complete RBAC states. We then develop techniques that enable us to compare these algorithms directly. We also introduce a new role mining algorithm and two new ways for algorithmically generating datasets for evaluation. Using synthetic as well as real datasets, we compared nine role mining algorithms. Our results illustrate the strengths and weaknesses of these algorithms.

IT services delivery is a complex ecosystem that engages 100000s of system administrators in service delivery centers globally managing 1000s of IT systems on behalf of customers. Such large-scale hosting environments require a flexible identity management system to provision necessary access rights, in order to ensure compliance posture of an organization. A popular and effective access control scheme is Role Based Access Control (RBAC). Ideally, a role should correspond to a business function performed within an enterprise. Several role mining algorithms have been proposed which attempt to automate the process of role discovery. In this paper, we represent the user-permission assignments as a bi-partite graph with users/permissions as vertices and user-permission assignments as edges. Given a user-permission bi-partite graph, most role mining algorithms focus on discovering roles that cover all the user-permission assignments. We show that by relaxing the coverage requirement, one can improve the accuracy of role detection. We propose a parameterized definition of a role based on graph theoretical properties, and demonstrate that the role parameters can be controlled to balance the accuracy and coverage of the roles detected. Finally, we propose a heuristic to illustrate the efficacy of our approach and validate it on real and artificial organizational access control data.

Android is the most popular mobile platform today, and it is also the mobile operating system that is most heavily targeted by malware. Existing static analyses are effective in detecting the presence of most malicious code and unwanted information flows. However, certain types of malice are very difficult to capture explicitly by modeling permission sets, suspicious API calls, or unwanted information flows. One important type of such malice is malicious application logic, where a program (often subtly) modifies its outputs or performs actions that violate the expectations of the user. Malicious application logic is very hard to identify without a specification of the normal, expected functionality of the application. We refer to malicious application logic that is executed, or triggered, only under certain (often narrow) circumstances as a logic bomb. This is a powerful mechanism that is commonly employed by targeted malware, often used as part of APTs and state-sponsored attacks: in fact, in this scenario, the malware is designed to target specific victims and to only activate under certain circumstances. In this paper, we make a first step towards detecting logic bombs. In particular, we propose trigger analysis, a new static analysis technique that seeks to automatically identify triggers in Android applications. Our analysis combines symbolic execution, path predicate reconstruction and minimization, and inter-procedural control-dependency analysis to enable the precise detection and characterization of triggers, and it overcomes several limitations of existing approaches. We implemented a prototype of our analysis, called TriggerScope, and we evaluated it over a large corpus of 9,582 benign apps from the Google Play Store and a set of trigger-based malware, including the recently-discovered HackingTeam's RCSAndroid advanced malware. Our system is capable of automatically identify several interesting time-, location-, and SMS-related triggers, is affected by a low false positive rate (0.38%), and it achieves 100% detection rate on the malware set. We also show how existing approaches, specifically when tasked to detect logic bombs, are affected by either a very high false positive rate or false negative rate. Finally, we discuss the logic bombs identified by our analysis, including two previously-unknown backdoors in benign apps.

Uniform-scale assessment of role minimization in bipartite networks and its application to access control

Constraint is an essential aspect of role-based access control RBAC and is sometimes argued to be the principle motivation for RBAC. However, most of role mining algorithms do not consider the constraint. Furthermore, they just compare the least cost of the authorization process but do not consider how to assess the accuracy of the derived role state, thus, providing the motivation for this work. In this paper, we first define a wide variety of constraints, especially the permission cardinality constraint and user cardinality constraint. We further propose a role mining algorithm to generate roles based on these two kinds of cardinality constraints that consider the similarity between roles in the process of merging roles in order to improve the accuracy of the role state at the same time. Finally, we carry out the experiments to evaluate our approach. The experimental results demonstrate the effectiveness of our proposed algorithm. Copyright © 2014 John Wiley & Sons, Ltd.

PurposeThe objective of this paper was to review the functionalities and effectiveness of the free mobile health applications available in the Google Play and App stores used in Saudi Arabia, Italy, Singapore, the United Kingdom, USA, and India during the COVID-19 outbreak.MethodsThis study adopted a systematic search strategy to identify the free mobile applications available in the App and Google Play stores related to the COVID-19 outbreak. According to the PRISMA flowchart of the search, only 12 applications met the inclusion criterion.ResultsThe 12 mobile applications that met the inclusion criterion were: Mawid, Tabaud, Tawakkalna, Sehha, Aarogya setu, TraceTogether, COVID safe, Immuni, COVID symptom study, COVID watch, NHS COVID-19, and PathCheck. The following features and functionalities of the apps were described: app overview (price, ratings, android, iOS, developer/owner, country, status), health tools (user status-risk assessment, self-assessment, E-pass integration, test results reporting, online consultation, contact tracing), learning options (personalized notes, educational resources, COVID-19 information), communication tools (query resolution, appointments, social network, notifications), app design (data visualization, program plan), networking tools (location mapping – GPS, connectivity with other devices), and safety and security options (alerts, data protection). Also, the effectiveness of the apps was analyzed.ConclusionThe analysis revealed that various applications have been developed for different functions like contact tracing, awareness building, appointment booking, online consultation, etc. However, only a few applications have integrated various functions and features such as self-assessment, consultation, support and access to information. Also, most of the apps are focused on contact tracing, while very few are dedicated to raising awareness and sharing information about the COVID-19 pandemic. Likewise, the majority of applications rely on GPS and Bluetooth technologies for relevant functions. No apps were identified that had built-in social media features. It is suggested to design and develop an integrated mobile health application with most of the features and functionalities analyzed in this study.

Background The use of mobile apps for health and well-being has grown exponentially in the last decade, as such apps were reported to be ideal platforms for behavioral change and symptoms monitoring and management. Objective This study aimed to systematically review period tracking applications available at Google Play and Apple App Stores and determine the presence, features, and quality of these smartphone apps. In addition, behavioral changes associated with the top 5 rated apps were assessed. Methods This study used the Systematic Search Criteria through Google Play Store and iTunes Apple Store, using terms related to period tracking. Apps were scanned for matching the inclusion criteria and the included apps were assessed by two reviewers using the Mobile Application Rating Scale (MARS), a tool that was developed for classifying and assessing the quality of mHealth apps. Results Forty-nine apps met the inclusion criteria. Most of the apps enabled setting user goals, motivations, and interactivity, tracking multiple symptoms or mood changes, allowed notifications, and used graphs to illustrate the tracking result over a specific period of time. The majority of features and functions within these apps were offered for free, while some apps included limited in-app purchases or needed Internet connection to function. Certain apps were reported by participants to promote behavioral change and increase knowledge and awareness regarding monthly periods. Conclusions Period tracking apps were easy to use and navigate and can hence be readily adopted into routine tracking and management of periods. However, most apps were not based on significant evidence and may need further development to support period-related symptom management.

Study question Does mobile applications (apps) developed for infertile couples have a holistic and utility characteristic? Summary answer Many mobil apps developed in the field of fertility have limited features and very few relevant, understandable, and holistic apps were present for infertile individuals. What is known already In an increasingly technological world, mobile apps offer promising alternatives to support couples’ fertility treatment. Infertile couples can use these apps with aim to manage lifestyle, learn about fertility, follow clinical appointments, receive psychological support, or to seek peer support. For increasing efficiency of this applications, holistic approach should be considered. Human is a whole with biological, physiological, psychological, social and spiritual dimensions and in fertility treatment process all dimensions are highly crucial. Thus, the purpose of this study was to evaluate the utility and holistic perspectives of apps developed for infertile couples by using “APPLICATIONS” mobile app scoring system. Study design, size, duration The study was a systematic evaluation of apps related to fertility and it was conducted via mobile applications platform. To determine relevant apps search was conducted on Google Play and Apple App Store by using “infertility”, “fertility”, “in vitro fertilization”, “IVF”, “sterility” and “fecundity” key words.The search was conducted twice in 1-year intervals, in February 2021 and March 2022. In total from 1917 reached apps; 1841 apps were excluded, and 76 eligible app were evaluated. Participants/materials, setting, methods Mobile apps were independently evaluated by two authors via using APPLICATIONS scoring system. The APPLICATIONS scoring system consists objective criteria such as comprehensiveness (holistic approach), fee, subscription, use of evidence-based information, purchasing, connection, advertising, text search area, interplatform suitability, use of images/shapes or videos, and app-specific features are objective scoring criteria and subjective criteria for instance subjective presentation and ease of navigation in the app. Consequently the maximum score of APPLICATIONS scoring system is 17. Main results and the role of chance The study reached 1917 applications from the search with its keywords. Seventy-six mobile applications meeting the inclusion criteria were analyzed according to the “APPLICATIONS” scoring system. Of the 76 applications in the analysis, 18 were available only in the Apple App Store (23%), 28 were available only in the Google Play Store (36%), and 30 were available on both platforms (41%). Among analyzed app, we determined that one app received 16 points out of maximum 17 points, and two apps received 15 points each. Also, the lowest scoring app earned 4 points. According to overall composite scores, evaluable apps in our study showed scores in the middle range of possible scores with an average score of 9 out of 17, indicating insufficient quality in mobile apps. Besides that, only six apps (%7.8) meet all holistic comprehensiveness criteria and received full scores. Of 76 mobile applications, 31.5% were developed by a healthcare professional, 31.5% by a software company, and 38% by an unknown entity. In addition, 48% of the applications evaluated were specific to infertile couples, 45% were specific to infertile women, and 7% were specific to infertile men. Limitations, reasons for caution Because of the nature of the digital environment, mobile apps are in a continuously changing and rapidly developing internet environment, and currently, newly developed mobile apps were not present in the analysis. This was the limitation of our study. Wider implications of the findings Using mobile health apps can help infertile couples to improve treatment outcomes. Healthcare professionals should be aware of the widespread use of apps and correct the faulty practice recommendations that patients may encounter, and couples should be guided to the correct practices. Trial registration number not applicable

We developed an architecture that automatically searches for and downloads Android applications from the Android Market. Furthermore, we created a detailed mapping of Android application programming interface (API) calls to the required permission(s), if any, for each call. We then performed an analysis of 141,372 Android applications to determine if they have the appropriate set of permissions based on the static analysis of the APK bytecode of each application. Our findings indicate that the majority of mobile software developers are notusing the correct permission set and that they either over-specify or under-specify their security requirements.

To ensure the security of current information systems, role-based access control (RBAC) is a widely used concept. For this purpose, based on an initial assignment of permissions to users, permissions are grouped to roles, which are then assigned to users. The corresponding (NP-complete) optimization problem, the so-called role mining problem (RMP), aims at finding a minimal set of roles and a corresponding assignment of those roles to users. Previously, the RMP has been considered as a static optimization problem. However, the application of RBAC in real business use cases requires the inclusion of dynamically occurring events that reflect changes in the business environment of companies, as well as events that result from direct user interaction with the role mining process. Therefore, in this paper, we provide a comprehensive overview and classification of the most relevant events for role mining and present methods for integrating them into the framework of an evolutionary role mining algorithm. The functional performance of these methods as well as the overall performance gain of dynamic role mining compared to the static approach is then examined in a series of experiments.