22,368 research outputs found

    Security and Privacy Problems in Voice Assistant Applications: A Survey

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    Voice assistant applications have become omniscient nowadays. Two models that provide the two most important functions for real-life applications (i.e., Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR) models and Speaker Identification (SI) models. According to recent studies, security and privacy threats have also emerged with the rapid development of the Internet of Things (IoT). The security issues researched include attack techniques toward machine learning models and other hardware components widely used in voice assistant applications. The privacy issues include technical-wise information stealing and policy-wise privacy breaches. The voice assistant application takes a steadily growing market share every year, but their privacy and security issues never stopped causing huge economic losses and endangering users' personal sensitive information. Thus, it is important to have a comprehensive survey to outline the categorization of the current research regarding the security and privacy problems of voice assistant applications. This paper concludes and assesses five kinds of security attacks and three types of privacy threats in the papers published in the top-tier conferences of cyber security and voice domain.Comment: 5 figure

    The Viability and Potential Consequences of IoT-Based Ransomware

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    With the increased threat of ransomware and the substantial growth of the Internet of Things (IoT) market, there is significant motivation for attackers to carry out IoT-based ransomware campaigns. In this thesis, the viability of such malware is tested. As part of this work, various techniques that could be used by ransomware developers to attack commercial IoT devices were explored. First, methods that attackers could use to communicate with the victim were examined, such that a ransom note was able to be reliably sent to a victim. Next, the viability of using "bricking" as a method of ransom was evaluated, such that devices could be remotely disabled unless the victim makes a payment to the attacker. Research was then performed to ascertain whether it was possible to remotely gain persistence on IoT devices, which would improve the efficacy of existing ransomware methods, and provide opportunities for more advanced ransomware to be created. Finally, after successfully identifying a number of persistence techniques, the viability of privacy-invasion based ransomware was analysed. For each assessed technique, proofs of concept were developed. A range of devices -- with various intended purposes, such as routers, cameras and phones -- were used to test the viability of these proofs of concept. To test communication hijacking, devices' "channels of communication" -- such as web services and embedded screens -- were identified, then hijacked to display custom ransom notes. During the analysis of bricking-based ransomware, a working proof of concept was created, which was then able to remotely brick five IoT devices. After analysing the storage design of an assortment of IoT devices, six different persistence techniques were identified, which were then successfully tested on four devices, such that malicious filesystem modifications would be retained after the device was rebooted. When researching privacy-invasion based ransomware, several methods were created to extract information from data sources that can be commonly found on IoT devices, such as nearby WiFi signals, images from cameras, or audio from microphones. These were successfully implemented in a test environment such that ransomable data could be extracted, processed, and stored for later use to blackmail the victim. Overall, IoT-based ransomware has not only been shown to be viable but also highly damaging to both IoT devices and their users. While the use of IoT-ransomware is still very uncommon "in the wild", the techniques demonstrated within this work highlight an urgent need to improve the security of IoT devices to avoid the risk of IoT-based ransomware causing havoc in our society. Finally, during the development of these proofs of concept, a number of potential countermeasures were identified, which can be used to limit the effectiveness of the attacking techniques discovered in this PhD research

    In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning

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    Cracks and keyhole pores are detrimental defects in alloys produced by laser directed energy deposition (LDED). Laser-material interaction sound may hold information about underlying complex physical events such as crack propagation and pores formation. However, due to the noisy environment and intricate signal content, acoustic-based monitoring in LDED has received little attention. This paper proposes a novel acoustic-based in-situ defect detection strategy in LDED. The key contribution of this study is to develop an in-situ acoustic signal denoising, feature extraction, and sound classification pipeline that incorporates convolutional neural networks (CNN) for online defect prediction. Microscope images are used to identify locations of the cracks and keyhole pores within a part. The defect locations are spatiotemporally registered with acoustic signal. Various acoustic features corresponding to defect-free regions, cracks, and keyhole pores are extracted and analysed in time-domain, frequency-domain, and time-frequency representations. The CNN model is trained to predict defect occurrences using the Mel-Frequency Cepstral Coefficients (MFCCs) of the lasermaterial interaction sound. The CNN model is compared to various classic machine learning models trained on the denoised acoustic dataset and raw acoustic dataset. The validation results shows that the CNN model trained on the denoised dataset outperforms others with the highest overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC score (98%). Furthermore, the trained CNN model can be deployed into an in-house developed software platform for online quality monitoring. The proposed strategy is the first study to use acoustic signals with deep learning for insitu defect detection in LDED process.Comment: 36 Pages, 16 Figures, accepted at journal Additive Manufacturin

    Using Contactless Mobile Payment in the Vietnamese Restaurant Industry

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    This study develops a critical understanding of Contactless Mobile Payment (CMP) in the context of consumer behaviour and explores its use in the Vietnamese restaurant industry. An online survey was used to collect the data (n=153) from Vietnamese consumers. Data analysis was conducted with the use of SPSS and AMOS software. A Confirmatory Factor Analysis (CFA) in conjunction with Structural Equation Modelling (SEM) were employed to explore consumer perceptions regarding the use of CMP. The findings indicate that consumers find CMP a fast and convenient way to make transactions in Vietnamese restaurants. The findings also indicate the importance of ease of use and security. The study contributes to the understanding of consumer behaviour in regard to technology in the service industries context

    The Gradual Disappearance Of Financial Literacy In Today\u27s World. What Is Financial Literacy And Why Is It So Important? My Own Story Of Acquisition

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    There is a growing concern in this country that the middle class is disappearing and not in the way one would hope. Instead of families moving into a higher socio-economic class and being able to provide richer life experiences for themselves and their children, vast numbers are shifting to a lower socio-economic status level. The gap between the affluent and those barely eking out an existence is increasing at an alarming rate. This trend will directly affect who can successfully attend college and who will be available and capable to perform the blue-collar jobs that are vital to the continuation of our economy. Many of these jobs are becoming increasingly complex and sophisticated. While they may not require a college degree, they do require additional post-secondary training and expertise. Longer reaching concerns are that a dwindling middle class equates to a smaller tax base and contributes to a larger segment of the population that needs financial assistance. The productive management of money is part of a concept known as financial literacy. People that have money take this knowledge for granted. Somewhere along the line, whether it was at home, in school, or from personal experiences, successful people learned the value of earning money and using it thoughtfully and intentionally in order to achieve a future goal. No one disputes the fact that personal choices and discretion are parts of the picture. Imagine, however, that the environment in which you grew up did not contain earning possibilities. Perhaps you had to work without pay caring for your siblings, leaving no time to go out and earn your own money. Maybe your family was in the situation where everything that each family member earned was required to try to make ends meet. The result can be a feeling of ignorance and powerlessness around financial literacy and a lack of understanding the difference it could make in your life. This dissertation examines these issues. As a Scholarly Personal Narrative, it will also relate the story of my own journey of acquiring financial literacy and how that knowledge has affected my life. It concludes with a proposal that I created for teaching the concepts of financial literacy to underserved members of our society living at the lower socio-economic level. This education is important because understanding financial literacy can build self-confidence, empowerment, and purpose. This knowledge can also set an example that parents can pass on to their children and future generations. I believe this is one possible route toward breaking the cycle of poverty

    Développement d’un système intelligent de reconnaissance automatisée pour la caractérisation des états de surface de la chaussée en temps réel par une approche multicapteurs

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    Le rôle d’un service dédié à l’analyse de la météo routière est d’émettre des prévisions et des avertissements aux usagers quant à l’état de la chaussée, permettant ainsi d’anticiper les conditions de circulations dangereuses, notamment en période hivernale. Il est donc important de définir l’état de chaussée en tout temps. L’objectif de ce projet est donc de développer un système de détection multicapteurs automatisée pour la caractérisation en temps réel des états de surface de la chaussée (neige, glace, humide, sec). Ce mémoire se focalise donc sur le développement d’une méthode de fusion de données images et sons par apprentissage profond basée sur la théorie de Dempster-Shafer. Les mesures directes pour l’acquisition des données qui ont servi à l’entrainement du modèle de fusion ont été effectuées à l’aide de deux capteurs à faible coût disponibles dans le commerce. Le premier capteur est une caméra pour enregistrer des vidéos de la surface de la route. Le second capteur est un microphone pour enregistrer le bruit de l’interaction pneu-chaussée qui caractérise chaque état de surface. La finalité de ce système est de pouvoir fonctionner sur un nano-ordinateur pour l’acquisition, le traitement et la diffusion de l’information en temps réel afin d’avertir les services d’entretien routier ainsi que les usagers de la route. De façon précise, le système se présente comme suit :1) une architecture d’apprentissage profond classifiant chaque état de surface à partir des images issues de la vidéo sous forme de probabilités ; 2) une architecture d’apprentissage profond classifiant chaque état de surface à partir du son sous forme de probabilités ; 3) les probabilités issues de chaque architecture ont été ensuite introduites dans le modèle de fusion pour obtenir la décision finale. Afin que le système soit léger et moins coûteux, il a été développé à partir d’architectures alliant légèreté et précision à savoir Squeeznet pour les images et M5 pour le son. Lors de la validation, le système a démontré une bonne performance pour la détection des états surface avec notamment 87,9 % pour la glace noire et 97 % pour la neige fondante

    Message Journal, Issue 5: COVID-19 SPECIAL ISSUE Capturing visual insights, thoughts and reflections on 2020/21 and beyond...

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    If there is a theme running through the Message Covid-19 special issue, it is one of caring. Of our own and others’ resilience and wellbeing, of friendship and community, of students, practitioners and their futures, of social justice, equality and of doing the right thing. The veins of designing with care run through the edition, wide and deep. It captures, not designers as heroes, but those with humble views, exposing the need to understand a diversity of perspectives when trying to comprehend the complexity that Covid-19 continues to generate. As graphic designers, illustrators and visual communicators, contributors have created, documented, written, visualised, reflected, shared, connected and co-created, designed for good causes and re-defined what it is to be a student, an academic and a designer during the pandemic. This poignant period in time has driven us, through isolation, towards new rules of living, and new ways of working; to see and map the world in a different light. A light that is uncertain, disjointed, and constantly being redefined. This Message issue captures responses from the graphic communication design community in their raw state, to allow contributors to communicate their experiences through both their written and visual voice. Thus, the reader can discern as much from the words as the design and visualisations. Through this issue a substantial number of contributions have focused on personal reflection, isolation, fear, anxiety and wellbeing, as well as reaching out to community, making connections and collaborating. This was not surprising in a world in which connection with others has often been remote, and where ‘normal’ social structures of support and care have been broken down. We also gain insight into those who are using graphic communication design to inspire and capture new ways of teaching and learning, developing themselves as designers, educators, and activists, responding to social justice and to do good; gaining greater insight into society, government actions and conspiracy. Introduction: Victoria Squire - Coping with Covid: Community, connection and collaboration: James Alexander & Carole Evans, Meg Davies, Matthew Frame, Chae Ho Lee, Alma Hoffmann, Holly K. Kaufman-Hill, Joshua Korenblat, Warren Lehrer, Christine Lhowe, Sara Nesteruk, Cat Normoyle & Jessica Teague, Kyuha Shim. - Coping with Covid: Isolation, wellbeing and hope: Sadia Abdisalam, Tom Ayling, Jessica Barness, Megan Culliford, Stephanie Cunningham, Sofija Gvozdeva, Hedzlynn Kamaruzzaman, Merle Karp, Erica V. P. Lewis, Kelly Salchow Macarthur, Steven McCarthy, Shelly Mayers, Elizabeth Shefrin, Angelica Sibrian, David Smart, Ane Thon Knutsen, Isobel Thomas, Darryl Westley. - Coping with Covid: Pedagogy, teaching and learning: Bernard J Canniffe, Subir Dey, Aaron Ganci, Elizabeth Herrmann, John Kilburn, Paul Nini, Emily Osborne, Gianni Sinni & Irene Sgarro, Dave Wood, Helena Gregory, Colin Raeburn & Jackie Malcolm. - Coping with Covid: Social justice, activism and doing good: Class Action Collective, Xinyi Li, Matt Soar, Junie Tang, Lisa Winstanley. - Coping with Covid: Society, control and conspiracy: Diana Bîrhală, Maria Borțoi, Patti Capaldi, Tânia A. Cardoso, Peter Gibbons, Bianca Milea, Rebecca Tegtmeyer, Danne Wo
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