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Attacking Smartphone Security and Privacy
Published in Georgios Kambourakis, Asaf Shabtai, Constantinos Kolias, Dimitrios Damopoulos, Intrusion Detection and Prevention for Mobile Ecosystems, 2017
Vincent F. Taylor, Ivan Martinovic
iOS is a mobile operating system developed by Apple Inc. It has a healthy app ecosystem that surrounds it with over 1.4 million iOS applications available for download. The operating system itself is proprietary, closed source, and written in C, C++, Objective-C, and Swift. It is a Unix-like operating system and features a hybrid kernel that runs on 64/32-bit ARM processors. Before iOS apps are made available to the public in the Apple App Store, they must undergo a thorough vetting process by Apple. Apps must pass reliability testing and other analysis to ensure that they are not malicious or otherwise unsavory. Apple's vetting process includes manual testing and static analysis to determine whether an app tries to perform actions outside of what it claims to do [6]. This vetting process is not always perfect and indeed security researchers have uncovered ways of circumventing the protections put in place by Apple [7]. In the case of Jekyll [8], the malicious app passed the vetting process by rearranging its code to add new, malicious functionality, after passing the approval process. The iOS kernel uses code signing to ensure that all apps running on a device come from an approved source and have not been tampered with [9]. Additionally, all third-party apps are sandboxed by iOS to prevent them from accessing data stored by other apps and modifying the system. However, Han et al. described how to “break out” of the iOS sandbox by leveraging dynamically loaded, private APIs in malicious apps [10]. Finally, iOS enforces a secure boot chain and file encryption using a per-file key.
Here Is One I Made Earlier: Machine Learning Deployment
Published in Jesús Rogel-Salazar, Advanced Data Science and Analytics with Python, 2020
We will first talk about data products, the requirement to build pipelines and processes and the need for creating machine learning models able to perform scoring on device. We will then provide an example for deploying a simple machine learning model in a mobile device such as an Apple iPhone. To that end we will use some of the capabilities that the ecosystem for these devices offers, including the use of XCode and the Swift programming language. We will build an iOS app!
Mobile Device Security
Published in Kutub Thakur, Al-Sakib Khan Pathan, Cybersecurity Fundamentals, 2020
Kutub Thakur, Al-Sakib Khan Pathan
The entire OS is written with different programming languages such as Objective-C, C++, C, and Swift. The first version of iOS was released on June 29, 2007. The latest stable version released by Apple Inc., is known as iOS 12.1.4, which was released in the first week of February 2019. The beta version of iOS 12.2 has also been released on February 19, 2019.
Game.UP: Gamified Urban Planning Participation Enhancing Exploration, Motivation, and Interactions
Published in International Journal of Human–Computer Interaction, 2023
Sarah L. Muehlhaus, Chloe Eghtebas, Nils Seifert, Gerhard Schubert, Frank Petzold, Gudrun Klinker
The prototype was built in Swift for iOS 13.1 using the Xcode development environment. All user studies were conducted on an iPad 11 as it provided a larger screen for accessibility and visibility. An initial selection screen at the start of the application allowed the switching between application versions (gamification, gamified-a, control). The application prompts the surveys throughout the beginning, middle, and end of the user study through a web-view controller accessing instances of the survey on SurveyMonkey, so a reliable data connection was required to record the user session on location. GPS and Camera access were required for the Apple map, in which the AR view (see Figure 1) is accessed. There were some technical difficulties with the on-site AR concerning scene detection due to the high number of moving variables (pedestrians, cyclists, cars), as well as environmental issues hindering the reliable placement of a QR code. As a result, the AR view in the application, implemented using the ARkit framework, only displayed a scene of the bridge upon manually tapping the screen.
Validity and reliability of a computer-vision-based smartphone app for measuring barbell trajectory during the snatch
Published in Journal of Sports Sciences, 2020
Carlos Balsalobre-Fernández, Gretchen Geiser, John Krzyszkowski, Kristof Kipp
The vertical and horizontal positions recorded with the motion capture system and were exported as .csv files for further processing. For the app, an update to the previously validated My Lift app (Balsalobre-Fernández et al., 2018) was specifically developed for this study using Xcode 10 for macOS High Sierra 10.14 and the Swift 4 programming language with iOS 12 SDK (Apple Inc., USA). The update included a set of custom computer-vision algorithms using Apple’s Vision framework (Apple Inc., USA) designed to automatically detect barbell trajectory during weightlifting movements. To calibrate the app, a scalable circle was drawn around the barbell plate that was closest to the camera. Then, computer-vision algorithms automatically tracked the motion of the selected plate during the whole movement. A video-tutorial showing how to use the app to measure the trajectory of the barbell in the snatch exercise can be found in the following URL: https://youtu.be/WGU4VR8efzQ, and as a supplementary file. Once the barbell trajectory was tracked in the app, the vertical and horizontal positions were exported as .csv files for further processing.
The validity and reliability of a novel app for the measurement of change of direction performance
Published in Journal of Sports Sciences, 2019
Carlos Balsalobre-Fernández, Chris Bishop, José Vicente Beltrán-Garrido, Pau Cecilia-Gallego, Aleix Cuenca-Amigó, Daniel Romero-Rodríguez, Marc Madruga-Parera
The CODTimer app was specifically developed for this study using Xcode 10.2.1 for macOS High Sierra 10.14.4 and the Swift 5 programming language with iOS 12 SDK (Apple Inc., USA). The AVFoundation and AVKit frameworks (Apple Inc., USA) were used for capturing, importing and manipulating high-speed videos. Then, the app (version 1.0) was installed on an iPhone X running iOS 12.2 (Apple Inc., USA) which has a recording frequency of 240 frames per second (fps) at a quality of FullHD (1920 × 1080 pixels). The app’s user interface was designed to record and high-speed videos and to allow a frame-by-frame inspection of them. Then, the app calculates the total time in the 5 + 5 change of direction test (5 + 5) as the difference between two time events which were manually selected by an independent user as follows: the beginning of the 5 + 5 was considered as the first frame in which the participant crossed the timing gate in the starting/end line of the test, and the end was considered as the first frame in which the participant crossed that gate again. A video-tutorial showing the complete procedure can be found in the following URL: https://youtu.be/_Y2xZjMA7fc