Improving Android app security and privacy with developers

Existing research has uncovered many security vulnerabilities in Android applications (apps) caused by inexperienced, and unmotivated developers. Especially, the lack of tool support makes it hard for developers to avoid common security and privacy problems in Android apps. As a result, this leads to apps with security vulnerability that exposes end users to a multitude of attacks. This thesis presents a line of work that studies and supports Android developers in writing more secure code. We first studied to which extent tool support can help developers in creating more secure applications. To this end, we developed and evaluated an Android Studio extension that identifies common security problems of Android apps, and provides developers suggestions to more secure alternatives. Subsequently, we focused on the issue of outdated third-party libraries in apps which also is the root cause for a variety of security vulnerabilities. Therefore, we analyzed all popular 3rd party libraries in the Android ecosystem, and provided developers feedback and guidance in the form of tool support in their development environment to fix such security problems. In the second part of this thesis, we empirically studied and measured the impact of user reviews on app security and privacy evolution. Thus, we built a review classifier to identify security and privacy related reviews and performed regression analysis to measure their impact on the evolution of security and privacy in Android apps. Based on our results we proposed several suggestions to improve the security and privacy of Android apps by leveraging user feedbacks to create incentives for developers to improve their apps toward better versions.Die bisherige Forschung zeigt eine Vielzahl von Sicherheitslücken in Android-Applikationen auf, welche sich auf unerfahrene und unmotivierte Entwickler zurückführen lassen. Insbesondere ein Mangel an Unterstützung durch Tools erschwert es den Entwicklern, häufig auftretende Sicherheits- und Datenschutzprobleme in Android Apps zu vermeiden. Als Folge führt dies zu Apps mit Sicherheitsschwachstellen, die Benutzer einer Vielzahl von Angriffen aussetzen. Diese Dissertation präsentiert eine Reihe von Forschungsarbeiten, die Android-Entwickler bei der Entwicklung von sichereren Apps untersucht und unterstützt. In einem ersten Schritt untersuchten wir, inwieweit die Tool-Unterstützung Entwicklern beim Schreiben von sicherem Code helfen kann. Zu diesem Zweck entwickelten und evaluierten wir eine Android Studio-Erweiterung, die gängige Sicherheitsprobleme von Android-Apps identifiziert und Entwicklern Vorschläge für sicherere Alternativen bietet. Daran anknüpfend, konzentrierten wir uns auf das Problem veralteter Bibliotheken von Drittanbietern in Apps, die ebenfalls häufig die Ursache von Sicherheitslücken sein können. Hierzu analysierten wir alle gängigen 3rd-Party-Bibliotheken im Android-Ökosystem und gaben den Entwicklern Feedback und Anleitung in Form von Tool-Unterstützung in ihrer Entwicklungsumgebung, um solche Sicherheitsprobleme zu beheben. Im zweiten Teil dieser Dissertation untersuchten wir empirisch die Auswirkungen von Benutzer-Reviews im Android Appstore auf die Entwicklung der Sicherheit und des Datenschutzes von Apps. Zu diesem Zweck entwickelten wir einen Review-Klassifikator, welcher in der Lage ist sicherheits- und datenschutzbezogene Reviews zu identifizieren. Nachfolgend untersuchten wir den Einfluss solcher Reviews auf die Entwicklung der Sicherheit und des Datenschutzes in Android-Apps mithilfe einer Regressionsanalyse. Basierend auf unseren Ergebnissen präsentieren wir verschiedene Vorschläge zur Verbesserung der Sicherheit und des Datenschutzes von Android-Apps, welche die Reviews der Benutzer zur Schaffung von Anreizen für Entwickler nutzen

The Influence of Investor's Characteristics on the Perception About the Selling Price of Luxury Apartments in Hanoi

This research was conducted to investigate the impact of investor's characteristics on the perception about the selling price of luxury apartments in Hanoi, Vietnam. Data were collected through a survey with 458 real estate employees from Hanoi. With this data, we have used descriptive statistics, Cronbach's Alpha to determine the level of impact of the independent variable on the dependent variable, i.e. the perception about the selling price of luxury apartments. The results showed that the determinant is the investor's characteristics which have positive relationships with the perception about the selling price of luxury apartments in Hanoi. Based on this finding, this paper gives several recommendations for improvement the perception about the selling price of luxury apartments in Hanoi. Keywords: the perception about the selling price of luxury apartme, investor's characteristics, real estate JEL codes: G12, G13, G14, L80, L85 DOI: 10.7176/EJBM/12-9-06 Publication date:March 31st 202

The optimal control system of the ship based on the linear quadratic regular algorithm

In this paper, the authors propose an optimal controller for the ship motion. Firstly, the model and dynamic equations of the ship motion are presented. Based on the model of the ship motion, the authors build the linear quadratic regular algorithm-based control system of ship motion to minimize the error between the desired trajectory and the response trajectory. The task of the controller is to control the trajectory of the ship to coincide with the desired trajectory. The ship model and controller are built to investigate the system quality through Matlab-Simulink software. The results show that the quality of the hold control system is very high. The trajectory of a ship always follows the desired trajectory with very small errors

Flexible information management strategies in machine learning and data mining

In recent times, a number of data rnining and machine learning techniques have been applied successfully to discover useful knowledge from data. Of the available techniques, rule induction and data clustering are two of the most useful and popular. Knowledge discovered from rule induction techniques in the form of If-Then rules is easy for users to understand and verify, and can be employed as classification or prediction models. Data clustering techniques are used to explore irregularities in the data distribution. Although rule induction and data clustering techniques are applied successfully in several applications, assumptions and constraints in their approaches have limited their capabilities. The main aim of this work is to develop flexible management strategies for these techniques to improve their performance. The first part of the thesis introduces a new covering algorithm, called Rule Extraction System with Adaptivity, which forms the whole rule set simultaneously instead of a single rule at a time. The rule set in the proposed algorithm is managed flexibly during the learning phase. Rules can be added to or omitted from the rule set depending on knowledge at the time. In addition, facilities to process continuous attributes directly and to prune the rule set automatically are implemented in the Rule Extraction System with Adaptivity algorithm The second part introduces improvements to the K-means algorithm in data clustering. Flexible management of clusters is applied during the learning process to help the algorithm to find the optimal solution. Another flexible management strategy is used to facilitate the processing of very large data sets. Finally, an effective method to determine the most suitable number of clusters for the K-means algorithm is proposed. The method has overcome all deficiencies of K-means

Testing Proxy Means Tests in the Field: Evidence from Vietnam

During 2005-2015, the poor households in Vietnam were identified by Ministry of Labor, Invalid and Social Affairs (MOLISA) using an approach that combined proxy means tests (PMT) and quick collection of income data. A set of indicators were used to identify the surely poor and surely non-poor households. Then, income data were collected using simple questionnaires for the remaining households to identify the poor households. However, measuring income using simple questionnaires can result in a large measurement error. In attempt to improve the poverty targeting, with the technical supports from the World Bank and General Statistics Office of Vietnam, MOLISA has improved the PMT method and used it to identify the poor households since 2015. Income data are no longer collected. This report documents the current poverty identification approach, and the process of movement from the income-PMT approach to the PMT approach in Vietnam

EnSolver: Uncertainty-Aware CAPTCHA Solver Using Deep Ensembles

The popularity of text-based CAPTCHA as a security mechanism to protect websites from automated bots has prompted researches in CAPTCHA solvers, with the aim of understanding its failure cases and subsequently making CAPTCHAs more secure. Recently proposed solvers, built on advances in deep learning, are able to crack even the very challenging CAPTCHAs with high accuracy. However, these solvers often perform poorly on out-of-distribution samples that contain visual features different from those in the training set. Furthermore, they lack the ability to detect and avoid such samples, making them susceptible to being locked out by defense systems after a certain number of failed attempts. In this paper, we propose EnSolver, a novel CAPTCHA solver that utilizes deep ensemble uncertainty estimation to detect and skip out-of-distribution CAPTCHAs, making it harder to be detected. We demonstrate the use of our solver with object detection models and show empirically that it performs well on both in-distribution and out-of-distribution data, achieving up to 98.1% accuracy when detecting out-of-distribution data and up to 93% success rate when solving in-distribution CAPTCHAs.Comment: Epistemic Uncertainty - E-pi UAI 2023 Worksho

Innovations in Water Management for Sustainable Development of Higher Education Institutions: Experience from Ton Duc Thang University, Vietnam

Ton Duc Thang University (TDTU), which was established in 1997, is a fully autonomous public university in Vietnam. After over 22 years of development, TDTU is now the number one university in Vietnam for all aspects. In 2019, TDTU was ranked the 1st in Vietnam and ranked 165th in the world on sustainable development by UI GreenMetric World University Rankings (UI GreenMetric). Among six categories of UI GreenMetric, including: setting and infrastructure, energy and climate change, waste, water, transport, and education, the water category of TDTU achieved 725 points out of 1000 maximum points (72.50%). This paper presents water management of TDTU, focusing on water conservation, water recycling, the use of water efficient appliances and piped water consumption. Specifically, the paper highlights innovations in water management that TDTU has implemented during the past few years. The paper concludes that sustainable water management makes great contribution to sustainable development of a higher education institutio

Simulation of suspended sediment and black carbon transport in surface water layer of Ha Long bay

Delft3D model employed to simulate the distribution and transport of suspended sediment and black carbon in Ha Long bay shows outcomes meeting with results from previous experiment studies. In the rainy season, suspended matter in surface layer is mainly in waters of western and southwestern Cat Ba island regions, and from Cua Luc toward the south nearshore areas with concentration of 50–130 g/m3. The concentration of suspended setdiment in the waters from Cua Luc to the north nearshore area is from 20 g/m3 to 50 g/m3 and that of offshore areas is 2–20 g/m3. In the dry season, the average concentrations of suspended matter are lower, approximately 110–150 g/m3 compared to the rainy season. In the rainy season, the total particulate carbon in surface layer is 0.0016–0.0028 kg/m3 and in the dry season, it ranges from 0.0001–0.005 kg/m3