Biblio
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.
This paper describes a realisation of a ResNet face recognition method through Zigbee-based wireless protocol. The system uses a CC2530 Zigbee-based radio frequency chip with connected VC0706 camera on it. The Arduino Nano had been used for organisation of data compression and effective division of Zigbee packets. The proposed solution also simplifies a data transmission within a strict bandwidth of Zigbee protocol and reliable packet forwarding in case of frequency distortion. The following investigation model uses Raspberry Pi 3 with connected Zigbee End Device (ZED) for successful receiving of important images and acceleration of deep learning interfaces. The model is integrated into a smart security system based on Zigbee modules, MySQL database, Android application and works in the background by using daemons procedures. To protect data, all wireless connections had been encrypted by the 128-bit Advanced Encryption Standard (AES-128) algorithm. Experimental results show a possibility to implement complex systems under restricted requirements of available transmission protocols.
Traffic identification becomes more important yet more challenging as related encryption techniques are rapidly developing nowadays. In difference to recent deep learning methods that apply image processing to solve such encrypted traffic problems, in this paper, we propose a method named Payload Encoding Representation from Transformer (PERT) to perform automatic traffic feature extraction using a state-of-the-art dynamic word embedding technique. Based on this, we further provide a traffic classification framework in which unlabeled traffic is utilized to pre-train an encoding network that learns the contextual distribution of traffic payload bytes. Then, the downward classification reuses the pre-trained network to obtain an enhanced classification result. By implementing experiments on a public encrypted traffic data set and our captured Android HTTPS traffic, we prove the proposed method can achieve an obvious better effectiveness than other compared baselines. To the best of our knowledge, this is the first time the encrypted traffic classification with the dynamic word embedding alone with its pre-training strategy has been addressed.
This paper deals with the design and development of a Li-Fi (light fidelity) simplex communication system for data exchange between Android mobile devices. Li-Fi is an up-to-date technology in the modern world, since it uses visible light for data exchange, allowing for high-speed communication. The paper includes a brief review of Li-Fi technology, a review of the literature used, and a study of technological methods for implementing such systems, based on scientific sources. We propose the algorithms for data exchange, packet formation, and encryption-decryption. The paper presents the developed mobile application and the transceiver device, the development results, as well as experiments with the developed prototype. The results show that Li-Fi technology is workable and is a good alternative to existing communication methods.
In the northern gas fields, most data are transmitted via wireless networks, which requires special transmission security measures. Herewith, the gas field infrastructure dictates cybersecurity modules to not only meet standard requirements but also ensure reduced energy consumption. The paper discusses the issue of building such a module for a process control system based on the RTP-04M recorder operating in conjunction with an Android-based mobile device. The software options used for the RSA and Diffie-Hellman data encryption and decryption algorithms on both the RTP-04M and the Android-based mobile device sides in the Keil μVision4 and Android Studio software environments, respectively, have shown that the Diffie-Hellman algorithm is preferable. It provides significant savings in RAM and CPU resources and power consumption of the recorder. In terms of energy efficiency, the implemented programs have been analyzed in the Android Studio (Android Profiler) and Simplicity Studio (Advanced Energy Monitor) environments. The integration of this module into the existing software will improve the field's PCS cybersecurity level due to protecting data transmitted from third-party attacks.
The purpose of the General Data Protection Regulation (GDPR) is to provide improved privacy protection. If an app controls personal data from users, it needs to be compliant with GDPR. However, GDPR lists general rules rather than exact step-by-step guidelines about how to develop an app that fulfills the requirements. Therefore, there may exist GDPR compliance violations in existing apps, which would pose severe privacy threats to app users. In this paper, we take mobile health applications (mHealth apps) as a peephole to examine the status quo of GDPR compliance in Android apps. We first propose an automated system, named HPDROID, to bridge the semantic gap between the general rules of GDPR and the app implementations by identifying the data practices declared in the app privacy policy and the data relevant behaviors in the app code. Then, based on HPDROID, we detect three kinds of GDPR compliance violations, including the incompleteness of privacy policy, the inconsistency of data collections, and the insecurity of data transmission. We perform an empirical evaluation of 796 mHealth apps. The results reveal that 189 (23.7%) of them do not provide complete privacy policies. Moreover, 59 apps collect sensitive data through different measures, but 46 (77.9%) of them contain at least one inconsistent collection behavior. Even worse, among the 59 apps, only 8 apps try to ensure the transmission security of collected data. However, all of them contain at least one encryption or SSL misuse. Our work exposes severe privacy issues to raise awareness of privacy protection for app users and developers.
The development in the web technologies given growth to the new application that will make the voting process very easy and proficient. The E-voting helps in providing convenient, capture and count the votes in an election. This project provides the description about e-voting using an Android platform. The proposed e-voting system helps the user to cast the vote without visiting the polling booth. The application provides authentication measures in order to avoid fraud voters using the OTP. Once the voting process is finished the results will be available within a fraction of seconds. All the casted vote count is encrypted using AES256 algorithm and stored in the database in order to avoid any outbreaks and revelation of results by third person other than the administrator.
The development of mobile internet has brought convenience to people, but the openness and diversity of mobile Internet make it face the security threat of communication privacy data disclosure. In this paper, a trusted android device security communication method based on TrustZone is proposed. Firstly, Elliptic Curve Diffie-Hellman (ECDH) key agreement algorithm is used to make both parties negotiate the session key in the Trusted Execution Environment (TEE), and then, we stored the key safely in the TEE. Finally, TEE completes the encryption and decryption of the transmitted data. This paper constructs a secure communication between mobile devices without a trusted third party and analyzes the feasibility of the method from time efficiency and security. The experimental results show that the method can resist malicious application monitoring in the process of data encryption and ensures the security of the session key. Compared with the traditional scheme, it is found that the performance of the scheme is not significantly reduced.
Increased availability of mobile cameras has led to more opportunities for people to record videos of significantly more of their lives. Many times people want to share these videos, but only to certain people who were co-present. Since the videos may be of a large event where the attendees are not necessarily known, we need a method for proving co-presence without revealing information before co-presence is proven. In this demonstration, we present a privacy-preserving method for comparing the similarity of two videos without revealing the contents of either video. This technique leverages the Similarity of Simultaneous Observation technique for detecting hidden webcams and modifies the existing algorithms so that they are computationally feasible to run under fully homomorphic encryption scheme on modern mobile devices. The demonstration will consist of a variety of devices preloaded with our software. We will demonstrate the video sharing software performing comparisons in real time. We will also make the software available to Android devices via a QR code so that participants can record and exchange their own videos.
The evolving of context-aware applications are becoming more readily available as a major driver of the growth of future connected smart, autonomous environments. However, with the increasing of security risks in critical shared massive data capabilities and the increasing regulation requirements on privacy, there is a significant need for new paradigms to manage security and privacy compliances. These challenges call for context-aware and fine-grained security policies to be enforced in such dynamic environments in order to achieve efficient real-time authorization between applications and connected devices. We propose in this work a novel solution that aims to provide context-aware security model for Android applications. Specifically, our proposition provides automated context-aware access control model and leverages Attribute-Based Encryption (ABE) to secure data communications. Thorough experiments have been performed and the evaluation results demonstrate that the proposed solution provides an effective lightweight adaptable context-aware encryption model.
Instant messaging is an application that is widely used to communicate. Based on the wearesocial.com report, three of the five most used social media platforms are chat or instant messaging. Instant messaging was chosen for communication because it has security features in log in using a One Time Password (OTP) code, end-to-end encryption, and even two-factor authentication. However, instant messaging applications still have a vulnerability to account theft. This account theft occurs when the user loses his cellphone. Account theft can happen when a cellphone is locked or not. As a result of this account theft, thieves can read confidential messages and send fake news on behalf of the victim. In this research, instant messaging application security will be applied using hybrid encryption and two-factor authentication, which are made interrelated. Both methods will be implemented in 2 implementation designs. The implementation design is securing login and securing sending and receiving messages. For login security, QR Code implementation is sent via email. In sending and receiving messages, the message decryption process will be carried out when the user is authenticated using a fingerprint. Hybrid encryption as message security uses RSA 2048 and AES 128. Of the ten attempts to steal accounts that have been conducted, it is shown that the implementation design is proven to reduce the impact of account theft.
Enterprises round the globe have been searching for a way to securely empower AndroidTM devices for work but have spurned away from the Android platform due to ongoing fragmentation and security concerns. Discrepant vulnerabilities have been reported in Android smartphones since Android Lollipop release. Smartphones can be easily hacked by installing a malicious application, visiting an infectious browser, receiving a crafted MMS, interplaying with plug-ins, certificate forging, checksum collisions, inter-process communication (IPC) abuse and much more. To highlight this issue a manual analysis of Android vulnerabilities is performed, by using data available in National Vulnerability Database NVD and Android Vulnerability website. This paper includes the vulnerabilities that risked the dual persona support in Android 5 and above, till Dec 2017. In our security threat analysis, we have identified a comprehensive list of Android vulnerabilities, vulnerable Android versions, manufacturers, and information regarding complete and partial patches released. So far, there is no published research work that systematically presents all the vulnerabilities and vulnerability assessment for dual persona feature of Android's smartphone. The data provided in this paper will open ways to future research and present a better Android security model for dual persona.