This thesis deals with the creation of a system for managing and monitoring the use of mobile devices in a selected company. Firstly, it deals with the analysis of the current state and then, based on the identified requirements, describes the system design and implementation using selected technologies. The system is implemented in the form of an Android application in Java and a web application in the JavaScript framework Vue.js.

This paper introduces a system which enables visually impaired users to detect objects and landmarks within the line of sight. The system works in two modes: landmark mode, which detects predefined landmarks, and object mode, which detects objects for everyday use. Users can get audio announcement for the name of the detected object or landmark as well as its estimated distances. Landmark detection helps visually impaired users explore an unfamiliar environment and build a mental map. The proposed system utilizes a deep learning system for detection, which is deployed on the mobile phone and optimized to run in real-time. Unlike many other existing deep-learning systems that require an Internet connection or specific accessories. Our system works offline and only requires a smart phone with camera, which gives the advantage to avoid the cost for data services, reduce delay to access the cloud server, and increase the system reliability in all environments.

The popularity of smart phones has risen throughout the years since first introduced. With the popularity of the devices growing so too has the number of malicious applications flooding the devices’ marketplaces. With more usage there becomes a larger target for malware and exploitation creation. As threats to these devices continue to grow there is a constant need for security to safeguard against these threats. Some attempts to protect smart phones involve building software to analyze applications running on the devices. This attempt has cut back on the amount of malicious software on the marketplace. These attempts however only catch malicious applications after they have been running. This dissertation presents the Secure Android Development Model. The goal of this model is to contribute to security of these devices by having a development model that implicitly builds security into applications. The model ensures a minimal amount of open permissions thus limiting the number of attack vectors that malicious software can make on the devices. By following the model, developers will have all information available during development to make appropriate security decisions in their applications.

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Android is the most popular operating system and occupies close to 70% of the market share. With the growth in the usage of Android OS, the number of games also increased and the Android play store has over 500,000 games. Testing of Android games is done either manually or through some of the existing tools which automate some parts of this testing. Manual testing requires a great deal of effort and can be expensive to afford. The existing tools which automate testing do not make use of any domain knowledge. This can cause the testing to be ineffective as the game may involve complex strategies, intricate details, widgets, etc. Existing tools like Android Monkey and Time Machine generate random Android events, including gestures like touch, swipe, clicks, and other system-level events across the application. Some deep learning methods like Wuji were only created for combat-type games. These limitations make it imperative to create a testing paradigm that uses domain knowledge as well as is easy to use by a developer who doesn't have any machine or deep learning knowledge. In this work, we develop a tool called DRAG- Deep Reinforcement learning based Android Gamer - which leverages Reinforcement Learning to learn the requisite domain knowledge and play the game in a fashion like a human would. DRAG uses a unified Reinforcement Learning agent and a Unified Reinforcement Learning environment. It only customizes the action space for each game. This generalization is done in the following ways- 1) Record an 8-minute demo video of the game and capture the underlying Android action log. 2) Analyze the recorded video and the action log to generate an action space for the Reinforcement Learning Agent. The unified RL agent is trained by providing it the score and coverage as a reward and screenshots of the game as observed states. We chose a set of 19 different open-sourced games for evaluation of the created tool. These games differ in the action set required by each of them - some require tapping icons, some require swiping in random directions, and some require more complex actions which are a combination of different gestures. The evaluation of our tool outperformed state-of-the-art TimeMachine for all 19 games and outperformed Monkey in 16 of the 19 games. This strengthens the fact that Deep Reinforcement Learning can be used to test Android games and can provide better results than tools that make no use of any domain knowledge. / Master of Science / The popularity of the Android operating system has led to a significant increase in the number of available Android games, with over 500,000 games on the Android Play Store alone. However, ensuring the quality and functionality of these games has become a challenge. Traditional testing methods involve either time-consuming manual testing or the use of existing tools that lack the necessary domain knowledge to handle complex game mechanics effectively. To overcome these limitations, we propose a solution called DRAG: the Deep Reinforcement Learning-based Android Gamer. Our tool utilizes Reinforcement Learning (RL) to acquire the domain knowledge needed to play Android games in a manner similar to human players. Unlike other tools, DRAG incorporates a unified RL agent and environment that can be customized for each specific game.The process of customizing the action space involves two main steps. First, we record an 8-minute demonstration video of the game while capturing the underlying Android action log. Then, we analyze the video and action log to generate a tailored action space for the game. The unified RL agent is trained using rewards based on the game's score and coverage, while observed screenshots of the game serve as input states.\\We evaluated DRAG using a diverse set of 19 open-source games, each requiring different actions such as tapping icons, swiping in random directions, or complex combinations of gestures. Our results demonstrate that DRAG outperforms state-of-the-art tools like TimeMachine in all 19 games and outperforms Monkey in 16 of the 19 games. These findings highlight the effectiveness of Deep Reinforcement Learning for testing Android games and its ability to deliver better results compared to tools lacking domain knowledge.Our work introduces a new testing approach that combines RL and domain knowledge, providing a user-friendly solution for developers without extensive machine or deep learning expertise. By automating game testing to replicate human gameplay, DRAG offers the potential for more efficient and effective quality assurance in the Android gaming ecosystem.

Construction projects require large amounts of cyber-information, such as 3D models, in order to achieve success. Unfortunately, this information is typically difficult for construction field personnel to access and use on-site, due to the highly mobile nature of the job and hazardous work environments. Field personnel rely on carrying around large stacks of construction drawings, diagrams, and specifications, or traveling to a trailer to look up information electronically, reducing potential project efficiency. This thesis details my work on Hybrid 4-Dimensional Augmented Reality, known as HD4AR, a mobile augmented reality system for construction projects that provides high-precision visualization of semantically-rich 3D cyber-information over real-world imagery. The thesis examines the challenges related to augmenting reality on a construction site, describes how HD4AR overcomes these challenges, and empirically evaluates the capabilities of HD4AR. / Master of Science

Android malware collusion is a new threat model that occurs when multiple Android apps communicate in order to execute an attack. This threat model threatens all Android users' private information and system resource security. Although recent research has made advances in collusion detection and classification, security analysts still do not have robust tools which allow them to definitively identify colluding Android applications. Specifically, in order to determine whether an alert produced by a tool scanning for Android collusion is a true-positive or a false-positive, the analyst must perform manual analysis of the suspected apps, which is both time consuming and prone to human errors. In this thesis, we present a new approach to definitive Android collusion detection and confirmation by rendering inter-component communications between a set of potentially collusive Android applications. Inter-component communications (abbreviated to ICCs), are a feature of the Android framework that allows components from different applications to communicate with one another. Our approach allows Android security analysts to inspect all ICCs within a set of suspicious Android applications and subsequently identify collusive attacks which utilize ICCs. Furthermore, our approach also visualizes all potentially collusive data-flows within each component within a set of apps. This allows analysts to inspect, step-by-step, the the data-flows that are currently used by collusive attacks, or the data-flows that could be used for future collusive attacks. Our tool effectively visualizes the malicious and benign ICCs in sets of proof-of-concept and real-world colluding applications. We conducted a user study which revealed that our approach allows for accurate and efficient identification of true- and false-positive collusive ICCs while still maintaining usability. / Master of Science