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Campus driver assistant on an Android platform
Title
Campus driver assistant on an Android platform.
Creator
Zankina, Iana., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
College campuses can be large, confusing, and intimidating for new students and visitors. Finding the campus may be easy using a GPS unit or Google Maps directions, but this is not the case when you are actually on the campus. There is no service that provides directional assistance for the campus itself. This thesis proposes a driver assistant application running on an Android platform that can direct drivers to different buildings and parking lots in the campus. The application's user...
Show moreCollege campuses can be large, confusing, and intimidating for new students and visitors. Finding the campus may be easy using a GPS unit or Google Maps directions, but this is not the case when you are actually on the campus. There is no service that provides directional assistance for the campus itself. This thesis proposes a driver assistant application running on an Android platform that can direct drivers to different buildings and parking lots in the campus. The application's user interface lets the user select a user type, a campus, and a destination through use of drop down menus and buttons. Once the user submits the needed information, then the next portion of the application runs in the background. The app retrieves the Campus Map XML created by the mapping tool that was constructed for this project. The XML data containing all the map elements is then parsed and stored in a hierarchal data structure. The resulting objects are then used to construct a campus graph, on which an altered version of Dijkstra's Shortest Path algorithm is executed. When the path to the destination has been discovered, the campus map with the computed path overlaid is displayed on the user's device, showing the route to the desired destination.
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Date Issued
2012
PURL
http://purl.flvc.org/FAU/3359159
Subject Headings
Mobile computing, Software engineering, Application software, Development
Format
Document (PDF)
Android approach to web services resource framework
Title
An Android approach to web services resource framework.
Creator
Garcia-Kunzel, Adriana., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Web services have become increasingly important over the past decades. Versatility and platform independence are just some of their advantages. On the other hand, grid computing enables the efficient distribution of computing resources. Together, they provide a great source of computing power that can be particularly leveraged by mobile devices. Mobile computing enables information creation, processing, storage and communication without location constraints [63], not only improving business'...
Show moreWeb services have become increasingly important over the past decades. Versatility and platform independence are just some of their advantages. On the other hand, grid computing enables the efficient distribution of computing resources. Together, they provide a great source of computing power that can be particularly leveraged by mobile devices. Mobile computing enables information creation, processing, storage and communication without location constraints [63], not only improving business' operational efficiency [63] but actually changing a way of life. However, the convenience of anytime and anywhere communication is counterbalanced by small screens, limited computing power and battery life. Despite these limitations, mobile devices can extend grid functionality by bringing to the mix not only mobile access but sensing capabilities as well, gathering information from their surroundings through built in mechanisms, such as microphone, camera, GPS and even accelerometers. Prior work has already demonstrated the possibility of enabling Web Services Resource Framework (WSRF) access to grid resources from mobile device clients in the WSRF-ME project [39], where a representative Nokia S60 Smartphone application was created on a framework, which extends the JSR-172 functionality to achieve WSRF compliance. In light of today's mobile phone market diversity, this thesis extends the solution proposed by WSRF-ME to non-Java ME phones and to Android devices in particular. Android-based device numbers have grown considerably over the past couple of years despite its recent creation and reduced availability of mature software tools., Therefore, Android's web service capabilities are studied and the original framework is analyzed in order to propose a modified framework version that achieves and documents WSRF compliant communication form Android for the first time. As a case study, an illustrative mobile File Explorer application is developed to match the mod framework' functionality to the original WSRF-ME's use case. An additional case study, the LIGO Monitor application, shows the viability of mobile web services for monitoring purposes in the Laser Interferometer Gravitational Observatory (LIGO) grid environment for the first time. The context that an actual application implementation such as LIGO provides, allows some of the challenges of real mobile grid clients to surface. As a result, the observations made during this development give way to the drafting of a preliminary set of guidelines for Globus service implementation suitable for Android consumption that still remain open for proof in future works.
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Date Issued
2010
PURL
http://purl.flvc.org/FAU/2684891
Subject Headings
Application software, Development, Mobile communication systems, User interfaces (Computer systems), Computational grids (Computer systems), Data structures (Computer science)
Format
Document (PDF)
Deep Learning for Android Application Ransomware Detection
Title
Deep Learning for Android Application Ransomware Detection.
Creator
Wongsupa, Panupong, Zhu, Xingquan, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Smartphones and mobile tablets are rapidly growing, and very important nowadays. The most popular mobile operating system since 2012 has been Android. Android is an open source platform that allows developers to take full advantage of both the operating system and the applications itself. However, due to the open source community of an Android platform, some Android developers took advantage of this and created countless malicious applications such as Trojan, Malware, and Ransomware. All...
Show moreSmartphones and mobile tablets are rapidly growing, and very important nowadays. The most popular mobile operating system since 2012 has been Android. Android is an open source platform that allows developers to take full advantage of both the operating system and the applications itself. However, due to the open source community of an Android platform, some Android developers took advantage of this and created countless malicious applications such as Trojan, Malware, and Ransomware. All which are currently hidden in a large number of benign apps in official Android markets, such as Google PlayStore, and Amazon. Ransomware is a malware that once infected the victim’s device. It will encrypt files, unlock device system, and display a popup message which asks the victim to pay ransom in order to unlock their device or system which may include medical devices that connect through the internet. In this research, we propose to combine permission and API calls, then use Deep Learning techniques to detect ransomware apps from the Android market. Permissions setting and API calls are extracted from each app file by using a python library called AndroGuard. We are using Permissions and API call features to characterize each application, which can identify which application has potential to be ransomware or is benign. We implement our Android Ransomware Detection framework based on Keras, which uses MLP with back-propagation and a supervised algorithm. We used our method with experiments based on real-world applications with over 2000 benign applications and 1000 ransomware applications. The dataset came from ARGUS’s lab [1] which validated algorithm performance and selected the best architecture for the multi-layer perceptron (MLP) by trained our dataset with 6 various of MLP structures. Our experiments and validations show that the MLPs have over 3 hidden layers with medium sized of neurons achieved good results on both accuracy and AUC score of 98%. The worst score is approximately 45% to 60% and are from MLPs that have 2 hidden layers with large number of neurons.
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Date Issued
2018
PURL
http://purl.flvc.org/fau/fd/FA00013151
Subject Headings
Deep learning, Android (Electronic resource)--Security measures, Malware (Computer software)--Prevention
Format
Document (PDF)