Biometric fingerprint architecture for home security system

Home security system is an emerging technology that gained much attention recently by homeowners. The conventional hardwired system is easy to install in newly developed homes; however, the existing homes require complex configuration of such systems which involves substantial cost. Hence, a wireless home security system has been an alternative to the hardwired.This paper describes a simple home security system that is implemented using fingerprint biometrics technology.The system is known as BIOmetrics FIngerprint for Home Security (BIOFIHS). BIOFIHS is demonstrated using a prototype that consists of hardware and software components.The hardware includes fingerprint sensors, a microcontroller, a wireless network router, an application server, and a smartphone. For the software, a program is developed to record the fingerprint data and to verify the data on the remote server. All of the components are connected to the home network wirelessly that makes the system easier to implement with cheaper costs

Biometric face authentication system for secure smart office environments

The use of the smart office concept in security has increased significantly recently. One of the areas of concern is the use of facial finger biometric technology for authentication systems, for example, authentication to enter a particular room in an office. This paper aims to describe a new prototype for office door automation and security that combines facial biometric technology and NodeMCU. Hopefully, this system will help increase the safety and comfort of office employees with easy installation and low cost. This system automatically controls (opens or closes) the door based on the biometrics of the user’s face registered in the database on the NodeMCU microcontroller. The main system comprises a NodeMCU microcontroller, face sensors, and a door lock system

Architecture and Applications of IoT Devices in Socially Relevant Fields

Number of IoT enabled devices are being tried and introduced every year and there is a healthy competition among researched and businesses to capitalize the space created by IoT, as these devices have a great market potential. Depending on the type of task involved and sensitive nature of data that the device handles, various IoT architectures, communication protocols and components are chosen and their performance is evaluated. This paper reviews such IoT enabled devices based on their architecture, communication protocols and functions in few key socially relevant fields like health care, farming, firefighting, women/individual safety/call for help/harm alert, home surveillance and mapping as these fields involve majority of the general public. It can be seen, to one's amazement, that already significant number of devices are being reported on these fields and their performance is promising. This paper also outlines the challenges involved in each of these fields that require solutions to make these devices reliableComment: 1

Enhancing Usability, Security, and Performance in Mobile Computing

We have witnessed the prevalence of smart devices in every aspect of human life. However, the ever-growing smart devices present significant challenges in terms of usability, security, and performance. First, we need to design new interfaces to improve the device usability which has been neglected during the rapid shift from hand-held mobile devices to wearables. Second, we need to protect smart devices with abundant private data against unauthorized users. Last, new applications with compute-intensive tasks demand the integration of emerging mobile backend infrastructure. This dissertation focuses on addressing these challenges. First, we present GlassGesture, a system that improves the usability of Google Glass through a head gesture user interface with gesture recognition and authentication. We accelerate the recognition by employing a novel similarity search scheme, and improve the authentication performance by applying new features of head movements in an ensemble learning method. as a result, GlassGesture achieves 96% gesture recognition accuracy. Furthermore, GlassGesture accepts authorized users in nearly 92% of trials, and rejects attackers in nearly 99% of trials. Next, we investigate the authentication between a smartphone and a paired smartwatch. We design and implement WearLock, a system that utilizes one\u27s smartwatch to unlock one\u27s smartphone via acoustic tones. We build an acoustic modem with sub-channel selection and adaptive modulation, which generates modulated acoustic signals to maximize the unlocking success rate against ambient noise. We leverage the motion similarities of the devices to eliminate unnecessary unlocking. We also offload heavy computation tasks from the smartwatch to the smartphone to shorten response time and save energy. The acoustic modem achieves a low bit error rate (BER) of 8%. Compared to traditional manual personal identification numbers (PINs) entry, WearLock not only automates the unlocking but also speeds it up by at least 18%. Last, we consider low-latency video analytics on mobile devices, leveraging emerging mobile backend infrastructure. We design and implement LAVEA, a system which offloads computation from mobile clients to edge nodes, to accomplish tasks with intensive computation at places closer to users in a timely manner. We formulate an optimization problem for offloading task selection and prioritize offloading requests received at the edge node to minimize the response time. We design and compare various task placement schemes for inter-edge collaboration to further improve the overall response time. Our results show that the client-edge configuration has a speedup ranging from 1.3x to 4x against running solely by the client and 1.2x to 1.7x against the client-cloud configuration

AI-assisted patent prior art searching - feasibility study

This study seeks to understand the feasibility, technical complexities and effectiveness of using artificial intelligence (AI) solutions to improve operational processes of registering IP rights. The Intellectual Property Office commissioned Cardiff University to undertake this research. The research was funded through the BEIS Regulators’ Pioneer Fund (RPF). The RPF fund was set up to help address barriers to innovation in the UK economy

The Proceedings of 15th Australian Information Security Management Conference, 5-6 December, 2017, Edith Cowan University, Perth, Australia

Conference Foreword The annual Security Congress, run by the Security Research Institute at Edith Cowan University, includes the Australian Information Security and Management Conference. Now in its fifteenth year, the conference remains popular for its diverse content and mixture of technical research and discussion papers. The area of information security and management continues to be varied, as is reflected by the wide variety of subject matter covered by the papers this year. The papers cover topics from vulnerabilities in “Internet of Things” protocols through to improvements in biometric identification algorithms and surveillance camera weaknesses. The conference has drawn interest and papers from within Australia and internationally. All submitted papers were subject to a double blind peer review process. Twenty two papers were submitted from Australia and overseas, of which eighteen were accepted for final presentation and publication. We wish to thank the reviewers for kindly volunteering their time and expertise in support of this event. We would also like to thank the conference committee who have organised yet another successful congress. Events such as this are impossible without the tireless efforts of such people in reviewing and editing the conference papers, and assisting with the planning, organisation and execution of the conference. To our sponsors, also a vote of thanks for both the financial and moral support provided to the conference. Finally, thank you to the administrative and technical staff, and students of the ECU Security Research Institute for their contributions to the running of the conference

Towards Authentication of IoMT Devices via RF Signal Classification

The increasing reliance on the Internet of Medical Things (IoMT) raises great concern in terms of cybersecurity, either at the device’s physical level or at the communication and transmission level. This is particularly important as these systems process very sensitive and private data, including personal health data from multiple patients such as real-time body measurements. Due to these concerns, cybersecurity mechanisms and strategies must be in place to protect these medical systems, defending them from compromising cyberattacks. Authentication is an essential cybersecurity technique for trustworthy IoMT communications. However, current authentication methods rely on upper-layer identity verification or key-based cryptography which can be inadequate to the heterogeneous Internet of Things (IoT) environments. This thesis proposes the development of a Machine Learning (ML) method that serves as a foundation for Radio Frequency Fingerprinting (RFF) in the authentication of IoMT devices in medical applications to improve the flexibility of such mechanisms. This technique allows the authentication of medical devices by their physical layer characteristics, i.e. of their emitted signal. The development of ML models serves as the foundation for RFF, allowing it to evaluate and categorise the released signal and enable RFF authentication. Multiple feature take part of the proposed decision making process of classifying the device, which then is implemented in a medical gateway, resulting in a novel IoMT technology.A confiança crescente na IoMT suscita grande preocupação em termos de cibersegurança, quer ao nível físico do dispositivo quer ao nível da comunicação e ao nível de transmissão. Isto é particularmente importante, uma vez que estes sistemas processam dados muito sensíveis e dados, incluindo dados pessoais de saúde de diversos pacientes, tais como dados em tempo real de medidas do corpo. Devido a estas preocupações, os mecanismos e estratégias de ciber-segurança devem estar em vigor para proteger estes sistemas médicos, defendendo-os de ciberataques comprometedores. A autenticação é uma técnica essencial de ciber-segurança para garantir as comunicações em sistemas IoMT de confiança. No entanto, os métodos de autenticação atuais focam-se na verificação de identidade na camada superior ou criptografia baseada em chaves que podem ser inadequadas para a ambientes IoMT heterogéneos. Esta tese propõe o desenvolvimento de um método de ML que serve como base para o RFF na autenticação de dispositivos IoMT para melhorar a flexibilidade de tais mecanismos. Isto permite a autenticação dos dispositivos médicos pelas suas características de camada física, ou seja, a partir do seu sinal emitido. O desenvolvimento de modelos de ML serve de base para o RFF, permitindo-lhe avaliar e categorizar o sinal libertado e permitir a autenticação do RFF. Múltiplas features fazem parte do processo de tomada de decisão proposto para classificar o dispositivo, que é implementada num gateway médico, resultando numa nova tecnologia IoMT