Breathing-based authentication on resource-constrained IoT devices using recurrent neural networks

Recurrent neural networks (RNNs) have shown promising results in audio and speech-processing applications. The increasing popularity of Internet of Things (IoT) devices makes a strong case for implementing RNN-based inferences for applications such as acoustics-based authentication and voice commands for smart homes. However, the feasibility and performance of these inferences on resource-constrained devices remain largely unexplored. The authors compare traditional machine-learning models with deep-learning RNN models for an end-to-end authentication system based on breathing acoustics

Secure and Usable Behavioural User Authentication for Resource-Constrained Devices

Robust user authentication on small form-factor and resource-constrained smart devices, such as smartphones, wearables and IoT remains an important problem, especially as such devices are increasingly becoming stores of sensitive personal data, such as daily digital payment traces, health/wellness records and contact e-mails. Hence, a secure, usable and practical authentication mechanism to restrict access to unauthorized users is a basic requirement for such devices. Existing user authentication methods based on passwords pose a mental demand on the user's part and are not secure. Behavioural biometric based authentication provides an attractive means, which can replace passwords and provide high security and usability. To this end, we devise and study novel schemes and modalities and investigate how behaviour based user authentication can be practically realized on resource-constrained devices. In the first part of the thesis, we implemented and evaluated the performance of touch based behavioural biometric on wearables and smartphones. Our results show that touch based behavioural authentication can yield very high accuracy and a small inference time without imposing huge resource requirements on the wearable devices. The second part of the thesis focus on designing a novel hybrid scheme named BehavioCog. The hybrid scheme combined touch gestures (behavioural biometric) with challenge-response based cognitive authentication. Touch based behavioural authentication is highly usable but is prone to observation attacks. While cognitive authentication schemes are highly resistant to observation attacks but not highly usable. The hybrid scheme improves the usability of cognitive authentication and improves the security of touch based behavioural biometric at the same time. Next, we introduce and evaluate a novel behavioural biometric modality named BreathPrint based on an acoustics obtained from individual's breathing gestures. Breathing based authentication is highly usable and secure as it only requires a person to breathe and low observability makes it secure against spoofing and replay attacks. Our investigation with BreathPrint showed that it could be used for efficient real-time authentication on multiple standalone smart devices especially using deep learning models

Wearable Wireless Devices

No abstract available

Wearable Wireless Devices

No abstract available

How Physicality Enables Trust: A New Era of Trust-Centered Cyberphysical Systems

Multi-agent cyberphysical systems enable new capabilities in efficiency, resilience, and security. The unique characteristics of these systems prompt a reevaluation of their security concepts, including their vulnerabilities, and mechanisms to mitigate these vulnerabilities. This survey paper examines how advancement in wireless networking, coupled with the sensing and computing in cyberphysical systems, can foster novel security capabilities. This study delves into three main themes related to securing multi-agent cyberphysical systems. First, we discuss the threats that are particularly relevant to multi-agent cyberphysical systems given the potential lack of trust between agents. Second, we present prospects for sensing, contextual awareness, and authentication, enabling the inference and measurement of ``inter-agent trust" for these systems. Third, we elaborate on the application of quantifiable trust notions to enable ``resilient coordination," where ``resilient" signifies sustained functionality amid attacks on multiagent cyberphysical systems. We refer to the capability of cyberphysical systems to self-organize, and coordinate to achieve a task as autonomy. This survey unveils the cyberphysical character of future interconnected systems as a pivotal catalyst for realizing robust, trust-centered autonomy in tomorrow's world

Secure Data Collection and Analysis in Smart Health Monitoring

Smart health monitoring uses real-time monitored data to support diagnosis, treatment, and health decision-making in modern smart healthcare systems and benefit our daily life. The accurate health monitoring and prompt transmission of health data are facilitated by the ever-evolving on-body sensors, wireless communication technologies, and wireless sensing techniques. Although the users have witnessed the convenience of smart health monitoring, severe privacy and security concerns on the valuable and sensitive collected data come along with the merit. The data collection, transmission, and analysis are vulnerable to various attacks, e.g., eavesdropping, due to the open nature of wireless media, the resource constraints of sensing devices, and the lack of security protocols. These deficiencies not only make conventional cryptographic methods not applicable in smart health monitoring but also put many obstacles in the path of designing privacy protection mechanisms. In this dissertation, we design dedicated schemes to achieve secure data collection and analysis in smart health monitoring. The first two works propose two robust and secure authentication schemes based on Electrocardiogram (ECG), which outperform traditional user identity authentication schemes in health monitoring, to restrict the access to collected data to legitimate users. To improve the practicality of ECG-based authentication, we address the nonuniformity and sensitivity of ECG signals, as well as the noise contamination issue. The next work investigates an extended authentication goal, denoted as wearable-user pair authentication. It simultaneously authenticates the user identity and device identity to provide further protection. We exploit the uniqueness of the interference between different wireless protocols, which is common in health monitoring due to devices\u27 varying sensing and transmission demands, and design a wearable-user pair authentication scheme based on the interference. However, the harm of this interference is also outstanding. Thus, in the fourth work, we use wireless human activity recognition in health monitoring as an example and analyze how this interference may jeopardize it. We identify a new attack that can produce false recognition result and discuss potential countermeasures against this attack. In the end, we move to a broader scenario and protect the statistics of distributed data reported in mobile crowd sensing, a common practice used in public health monitoring for data collection. We deploy differential privacy to enable the indistinguishability of workers\u27 locations and sensing data without the help of a trusted entity while meeting the accuracy demands of crowd sensing tasks