Biblio

We use mobile apps on a daily basis and there is an app for everything. We trust these applications with our most personal data. It is therefore important that these apps are as secure and well usable as possible. So far most studies on the maintenance and security of mobile applications have been done on Android applications. We do, however, not know how well these results translate to iOS.This research project aims to close this gap by analysing iOS applications with regards to maintainability and security. Regarding maintainability, we analyse code smells in iOS applications, the evolution of code smells in iOS applications and compare code smell distributions in iOS and Android applications. Regarding security, we analyse the evolution of the third-party library dependency network for the iOS ecosystem. Additionally, we analyse how publicly reported vulnerabilities spread in the library dependency network.Regarding maintainability, we found that the distributions of code smells in iOS and Android applications differ. Code smells in iOS applications tend to correspond to smaller classes, such as Lazy Class. Regarding security, we found that the library dependency network of the iOS ecosystem is not growing as fast as in some other ecosystems. There are less dependencies on average than for example in the npm ecosystem and, therefore, vulnerabilities do not spread as far.

Mobile devices are an inseparable part of our lives. They have made it possible to access all the information and services anywhere at any time. Almost all of the organizations try to provide a mobile device-based solution to its users. However, this convenience has arisen the risk of losing personal information and has increased the threat to security. It has been observed recently that some of the mobile device manufacturers and mobile apps developers have lost the private information of their users to hackers. It has risen a great concern among mobile device users about their personal information. Android and iOS are the major operating systems for mobile devices and share over 99% of the mobile device market. This research aims to conduct a comparative analysis of the security of the components in the Android and iOS operating systems. It analyses the security from several perspectives such as memory randomization, application sandboxing, isolation, encryption, built-in antivirus, and data storage. From the analysis, it is evident that iOS is more secure than Android operating system. However, this security comes with a cost of losing the freedom.

In this paper, two of the main Blockchain development platforms, Ethereum and EOS.IO are compared. The objective is to help developers select the most appropriate platform as the back-end Blockchain for their apps. A decentralized application was implemented on each of the platforms triggering basic operations and timing them. The simulations were performed on Microsoft’s Azure cloud, running up to 150 Blockchain nodes while recording the user response time, the CPU utilization, and the totally used memory in Mbytes. The results in this study show that although recognized as a major competitor to Ethereum, EOS.IO fails to outperform the Ethereum platform in this experiment, recording a very high response time in comparison to Ethereum.

Fitbit Versa is the most popular of its predecessors and successors in the Fitbit faction. Increasingly data stored on these smart fitness devices, their linked applications and cloud datacenters are being used for criminal convictions. There is limited research for investigators on wearable devices and specifically exploring evidence identification and methods of extraction. In this paper we present our analysis of Fitbit Versa using Cellebrite UFED and MSAB XRY. We present a clear scope for investigation and data significance based on the findings from our experiments. The data recovery will include logical and physical extractions using devices running Android 9 and iOS 12, comparing between Cellebrite and XRY capabilities. This paper discusses databases and datatypes that can be recovered using different extraction and analysis techniques, providing a robust outlook of data availability. We also discuss the accuracy of recorded data compared to planned test instances, verifying the accuracy of individual data types. The verifiable accuracy of some datatypes could prove useful if such data was required during the evidentiary processes of a forensic investigation.

Haptic communications and 5G networks in conjunction with AI and robotics will augment the human user experience by enabling real-time task performance via the control of objects remotely. This represents a paradigm shift from content delivery-based networks to task-oriented networks for remote skill set delivery. The transmission of user skill sets in remote task performance marks the advent of a haptic-enabled Internet of Skills (IoS), through which the transmission of touch and actuation sensations will be possible. In this proposed research, a conceptual Task-Technology Fit (TTF) model of a haptic-enabled IoS is developed to link human users and haptic-enabled technologies to technology use and task performance between master (control) and remote (controlled) domains to provide a Quality of Experience (QoE) and Quality of Task (QoT) oriented perspective of a Haptic Internet. Future 5G-enabled applications promise the high availability, security, fast reaction speeds, and reliability characteristics required for the transmission of human user skills over large geographical distances. The 5G network and haptic-enabled IoS considered in this research will support a number of critical applications. One such novel scenario in which a TTF of a Haptic Internet can be modelled is the use case of remote-controlled Unmanned Aerial Vehicles (UAVs). This paper is a contribution towards the realization of a 5G network and haptic-enabled QoE-QoT-centric IoS for augmented user task performance. Future empirical results of this research will be useful to understanding the role that varying degrees of a fit between context-specific task and technology characteristics play in influencing the impact of haptic-enabled technology use for real-time immersive remote UAV (drone) control task performance.

Embedded systems in physically insecure environments are subject to additional security risk via capture by an adversary. A captured microchip device can be reverse engineered to recover internal buffer data that would otherwise be inaccessible through standard IO mechanisms. We consider an adversary who has sufficient ability to gain all internal bits and logic from a device at the time of capture as an unsolved threat. In this paper we present a novel sensing architecture that enhances embedded system security by randomly encoding sensed values. We randomly encode data at the time of sensing to minimize the amount of plaintext data present on a device in buffer memory. We encode using techniques that are unintelligible to an adversary even with full internal bit knowledge. The encoding is decipherable by a trusted home server, and we have provided an architecture to perform this decoding. Our experimental results show the proposed architecture meets timing requirements needed to perform communications with a satellite utilizing short-burst data, such as in remote sensing telemetry and tracking applications.

The paper proposes ways to solve the problem of secure remote access to telecommunications’ equipment. The purpose of the study is to develop a set of procedures to ensure secure interaction while working remotely with Cisco equipment using the SSH protocol. This set of measures is a complete list of measures which ensures security of remote connection to a corporate computer network using modern methods of cryptography and network administration technologies. It has been tested on the GNS3 software emulator and Cisco telecommunications equipment and provides a high level of confidentiality and integrity of remote connection to a corporate computer network. In addition, the study detects vulnerabilities in the IOS operating system while running SSH service and suggests methods for their elimination.

In the context of the Industry 4.0 initiative, Cyber-Physical Production Systems (CPPS) or Cyber Manufacturing Systems (CMS) can be characterized as advanced networked mechatronic production systems gaining their added value by interaction with different systems using advanced communication technologies. Appropriate wired and wireless communication technologies and standards need to add timing in combination with security concepts to realize the potential improvements in the production process. One of these standards is IO-Link Wireless, which is used for sensor/actuator network operation. In this paper cryptographic performance and energy efficiency of an IO-Link Wireless Device are analyzed. The power consumption and the influence of the cryptographic operations on the trans-mission timing of the IO-Link Wireless protocol are exemplary measured employing a Phytec module based on a CC2650 system-on-chip (SoC) radio transceiver [2]. Confidentiality is considered in combination with the cryptographic performance as well as the energy efficiency. Different cryptographic algorithms are evaluated using the on chip hardware accelerator compared to a cryptographic software implementation.

Smart phones and mobile apps have become an essential part of our daily lives. It is necessary to ensure the quality of these apps. Two important aspects of code quality are maintainability and security. The goals of my PhD project are (1) to study code smells, security issues and their evolution in iOS apps and frameworks, (2) to enhance training and teaching using visualisation support, and (3) to support developers in automatically detecting dependencies to vulnerable library elements in their apps. For each of the three tools, dedicated tool support will be provided, i.e., GraphifyEvolution, VisualiseEvolution, and DependencyEvolution respectively. The tool GraphifyEvolution exists and has been applied to analyse code smells in iOS apps written in Swift. The tool has a modular architecture and can be extended to add support for additional languages and external analysis tools. In the remaining two years of my PhD studies, I will complete the other two tools and apply them in case studies with developers in industry as well as in university teaching.

Mobile security remains a concern for multiple stakeholders. Safe user behavior is crucial key to avoid and mitigate mobile threats. The research used a survey design to capture key constructs of mobile user threat avoidance behavior. Analysis revealed that there is no significant difference between the two key drivers of secure behavior, threat appraisal and coping appraisal, for Android and iOS users. However, statistically significant differences in avoidance motivation and avoidance behavior of users of the two operating systems were displayed. This indicates that existing threat avoidance models may be insufficient to comprehensively deal with factors that affect mobile user behavior. A newly introduced variable, perceived security, shows a difference in the perceptions of their level of protection among the users of the two operating systems, providing a new direction for research into mobile security.

The recent analysis indicates more than 250,000 people in the United States of America (USA) die every year because of medical errors. World Health Organisation (WHO) reports states that 2.6 million deaths occur due to medical and its prescription errors. Many of the errors related to the wrong drug/dosage administration by caregivers to patients due to indecipherable handwritings, drug interactions, confusing drug names, etc. The espousal of Mobile-based speech recognition applications will eliminate the errors. This allows physicians to narrate the prescription instead of writing. The application can be accessed through smartphones and can be used easily by everyone. An application program interface has been created for handling requests. Natural language processing is used to read text, interpret and determine the important words for generating prescriptions. The patient data is stored and used according to the Health Insurance Portability and Accountability Act of 1996 (HIPAA) guidelines. The SMS4-BSK encryption scheme is used to provide the data transmission securely over Wireless LAN.

The Security available in personal computers and laptops are not possible in mobile communication, since there is no controlling software such as an operating system. The European Union General Data Protection Regulation (GDPR) will require many organisations throughout the European Union to comply with new requirements that are intended to protect their user's personal data. The responsibilities of the organizations and the penalties related to the protection of personal data of the users are proved to be both organisationally and technically challenging. Under the GDPR's 'privacy by design' and 'privacy by default' requirements, organizations need to prove that they are in control of user data and have taken steps to protect it. There are a large number of organizations that makes use of mobile devices to process personal data of their customers. GDPR mandates that the organization shall be able to manage all devices that handles sensitive data so that the company can implement group updates, restrict apps and networks, and enforce security measures. In this work, we propose a Mobile Device Management solution using the built-in frameworks of Android and iOS mobile platforms which is compatible and incorporates GDPR articles relevant to a small to medium sized organization.

This article describes the development of two mobile applications for learning Digital Electronics. The first application is an interactive app for iOS where you can study the different digital circuits, and which will serve as the basis for the second: a game of questions in augmented reality.

The massive adoption of mobile devices by both individuals and companies is raising many security concerns. The fact that such devices are handling sensitive data makes them a target for attackers. Many attack prevention mechanisms are deployed with a last line of defense that focuses on the containment principle. Currently, iOS treats each 3rd party application alike which may lead to security flaws. We propose a framework in which each application has a custom sandboxed environment. We investigated the current confinement architecture used by Apple and built a solution on top of it.

While enjoying the convenience brought by mobile phones, users have been exposed to high risk of private information leakage. It is known that many applications on mobile devices read private data and send them to remote servers. However how, when and in what scale the private data are leaked are not investigated systematically in the real-world scenario. In this paper, a framework is proposed to analyze the usage data from mobile devices and the traffic data from the mobile network and make a comprehensive privacy leakage detection and privacy inference mining on a large scale of realworld mobile data. Firstly, this paper sets up a training dataset and trains a privacy detection model on mobile traffic data. Then classical machine learning tools are used to discover private usage patterns. Based on our experiments and data analysis, it is found that i) a large number of private information is transmitted in plaintext, and even passwords are transmitted in plaintext by some applications, ii) more privacy types are leaked in Android than iOS, while GPS location is the most leaked privacy in both Android and iOS system, iii) the usage pattern is related to mobile device price. Through our experiments and analysis, it can be concluded that mobile privacy leakage is pervasive and serious.

In this paper, we propose the first traffic monitoring and labeling system for iOS devices, named iMonitor, which not just captures mobile network traffic in .pcap files, but also provides comprehensive APP-related and user-related information of captured packets. Through further analysis, one can obtain the exact APP or device where each packet comes from. The labeled traffic can be used in many research areas for mobile security, such as privacy leakage detection and user profiling. Given the implementation methodology of NetworkExtension framework of iOS 9+, APP labels of iMonitor are reliable enough so that labeled traffic can be regarded as training data for any traffic classification methods. Evaluations on real iPhones demonstrate that iMonitor has no notable impact upon user experience even with slight packet latency. Also, the experiment result supports our motivation that mobile traffic monitoring for iOS is absolutely necessary, as traffic generated by different OSes like Android and iOS are different and unreplaceable in researches.

People increasingly rely on mobile devices for banking transactions or two-factor authentication (2FA) and thus trust in the security provided by the underlying operating system. Simultaneously, jailbreaks gain tremendous popularity among regular users for customizing their devices. In this paper, we show that both do not go well together: Jailbreaks remove vital security mechanisms, which are necessary to ensure a trusted environment that allows to protect sensitive data, such as login credentials and transaction numbers (TANs). We find that all but one banking app, available in the iOS App Store, can be fully compromised by trivial means without reverse-engineering, manipulating the app, or other sophisticated attacks. Even worse, 44% of the banking apps do not even try to detect jailbreaks, revealing the prevalent, errant trust in the operating system's security. This study assesses the current state of security of banking apps and pleads for more advanced defensive measures for protecting user data.

As the accessibility of high-resolution smartphone camera has increased and an improved computational speed, it is now convenient to build Business Card Readers on mobile phones. The project aims to design and develop a Business Card Reader (BCR) Application for iOS devices, using an open-source OCR Engine - Tesseract. The system accuracy was tested and evaluated using a dataset of 55 digital business cards obtained from an online repository. The accuracy result of the system was up to 74% in terms of both text recognition and data detection. A comparative analysis was carried out against a commercial business card reader application and our application performed vastly reasonable.

Increased use of secure chat and voice/ video apps has transformed the social life. While the benefits and facilitations are seemingly limitless, so are the asscoiacted vulnerabilities and threats. Besides ensuring confidentiality requirements for common users, known facts of non-readable contents over the network make these apps more attractive for criminals. Though access to contents of cryptograhically secure sessions is not possible, network forensics of secure apps can provide interesting information which can be of great help during criminal invetigations. In this paper, we presented a novel framework of profiling the secure chat and voice/ video calling apps which can be employed to extract hidden patterns about the app, information of involved parties, activities of chatting, voice/ video calls, status indications and notifications while having no information of communication protocol of the app and its security architecture. Signatures of any secure app can be developed though our framework and can become base of a large scale solution. Our methodology is considered very important for different cases of criminal investigations and bussiness intelligence solutions for service provider networks. Our results are applicable to any mobile platform of iOS, android and windows.

Software Engineering is a field of new challenges every day. With every passing day, new technologies emerge. There was an era of web Applications, but the time has changed and most of the web Applications are available as Mobile Applications as well. The Mobile Applications are either android based or iOS based. To deliver error free, secure and reliable Application, it is necessary to test the Applications properly. Software testing is a phase of software development life cycle, where we test an Application in all aspects. Nowadays different type of tools are available for testing an Application automatically but still we have too many challenges for applying test cases on a given Application. In this paper the authors will discuss the challenges of automation of test cases for a Mobile based payment Application.

This paper presents a detailed analysis of some relevant security features of iOS and Android -the two most popular operating systems for mobile devices- from the perspective of user privacy. In particular, permissions that can be modified at run time on these platforms are analyzed. Additionally, a framework is introduced for permission analysis, a hybrid mobile application that can run on both iOS and Android. The framework, which can be extended, places special emphasis on the relationship between the user's privacy and the permission system.

The growing use of smart phones has also given opportunity to the intruders to create malicious apps thereby the security and privacy concerns of a novice user has also grown. This research focuses on the privacy concerns of a user who unknowingly installs a malicious apps created by the programmer. In this paper we created an attack scenario and created an app capable of compromising the privacy of the users. After accepting all the permissions by the user while installing the app, the app allows us to track the live location of the Android device and continuously sends the GPS coordinates to the server. This spying app is also capable of sending the call log details of the user. This paper evaluates two leading smart phone operating systems- Android and IOS to find out the flexibility provided by the two operating systems to their programmers to create the malicious apps.

In this paper we propose a solution to support iOS developers in creating better applications, to use static analysis to investigate source code and detect secure coding issues while simultaneously pointing out good practices and/or secure APIs they should use.

Modern operating systems, such as iOS, use multiple access control policies to define an overall protection system. However, the complexity of these policies and their interactions can hide policy flaws that compromise the security of the protection system. We propose iOracle, a framework that logically models the iOS protection system such that queries can be made to automatically detect policy flaws. iOracle models policies and runtime context extracted from iOS firmware images, developer resources, and jailbroken devices, and iOracle significantly reduces the complexity of queries by modeling policy semantics. We evaluate iOracle by using it to successfully triage executables likely to have policy flaws and comparing our results to the executables exploited in four recent jailbreaks. When applied to iOS 10, iOracle identifies previously unknown policy flaws that allow attackers to modify or bypass access control policies. For compromised system processes, consequences of these policy flaws include sandbox escapes (with respect to read/write file access) and changing the ownership of arbitrary files. By automating the evaluation of iOS access control policies, iOracle provides a practical approach to hardening iOS security by identifying policy flaws before they are exploited.

Robotics and the Internet of Things (IoT) are enveloping our society at an exponential rate due to lessening costs and better availability of hardware and software. Additionally, Cloud Robotics and Robot Operating System (ROS) can offset onboard processing power. However, strong and fundamental security practices have not been applied to fully protect these systems., partially negating the benefits of IoT. Researchers are therefore tasked with finding ways of securing communications and systems. Since security and convenience are oftentimes at odds, securing many heterogeneous components without compromising performance can be daunting. Protecting systems from attacks and ensuring that connections and instructions are from approved devices, all while maintaining the performance is imperative. This paper focuses on the development of security best practices and a mesh framework with an open-source, multipoint-to-multipoint virtual private network (VPN) that can tie Linux, Windows, IOS., and Android devices into one secure fabric, with heterogeneous mobile robotic platforms running ROSPY in a secure cloud robotics infrastructure.