Authenticated Digital Avatars on Metaverse by Cyber Security Procedures

Metaverse is the  next generation Internet, aims to build a fully immersive, hyper spatiotemporal and self sustaining virtual shared space for humans to play, work, shop and socialize. In metaverse, users are  represented as digital avatars and using identity, user can shuttle across various virtual worlds (i.e., sub-metaverses) to experience a digital life, as well as make digital creations and economic interactions supported by physical infrastructures and the metaverse engine. Virtual reality headsets are the main devices used to access the Metaverse. Privacy and security concerns of the metaverse. The users need to verify their identity to log into the metaverse platforms, and the security of this phase becomes vital. In this paper, the user authentication methods such as Information-based authentication, biometric based authentication, and multi-model methods are reviewed and compared in terms of users security but in some cases these methods are failed to secure from cyber attacks. In this paper, we proposed,Token-based authentication method to enhance the security for the users to access and work on  the virtual environment

Enabling Privacy and Trust in Edge AI Systems

Recent advances in mobile computing and the Internet of Things (IoT) enable the global integration of heterogeneous smart devices via wireless networks. A common characteristic across these modern day systems is their ability to collect and communicate streaming data, making machine learning (ML) appealing for processing, reasoning, and predicting about the environment. More recently, low network latency requirements have made offloading intelligence to the cloud undesirable. These novel requirements have led to the emergence of edge computing, an approach that brings computation closer to the device with low latency, high throughput, and enhanced reliability. Together, they enable ML-powered information processing and control pipelines spanning end devices, edge computing, and cloud environments. However, continuous collaboration between cloud, edge and device is susceptible to information leakage and loss, leading to insecure and unreliable operation. This raises an important question: how can we design, develop, and evaluate high-performing ML systems that are trustworthy and privacy-preserving in resource-constrained edge environments? In this thesis, I address this question by designing and implementing privacy-preserving and trustworthy ML systems for distributed applications. I first introduce a system that establishes trust in the explanations generated from a popular visualization technique, saliency maps, using counterfactual reasoning. Through the proposed evaluation system, I assess the degree to which hypothesized explanations correspond to the semantics of edge-based reinforcement learning environments. Second, I examine the privacy implications of personalized models in distributed mobile services by proposing time-series based model inversion attacks. To thwart such attacks, I present a distributed framework, Pelican, that learns and deploys transfer learning-based personalized ML models in a privacy preserving manner on resource-constrained mobile devices. Third, I investigate ML models that are deployed on local devices for inference and highlight the ease with which proprietary information embedded in these models can be exposed. For mitigating such attacks, I present a secure on-device application framework, SODA, which is supported by real-time adversarial detection. Finally, I present an end-to-end privacy-aware system for a real-world application to model group interaction behavior via mobility sensing. The proposed system, W4-Groups, distributes computation across device, edge, and cloud resources to strengthen its privacy and trustworthiness guarantees

A patient agent controlled customized blockchain based framework for internet of things

Although Blockchain implementations have emerged as revolutionary technologies for various industrial applications including cryptocurrencies, they have not been widely deployed to store data streaming from sensors to remote servers in architectures known as Internet of Things. New Blockchain for the Internet of Things models promise secure solutions for eHealth, smart cities, and other applications. These models pave the way for continuous monitoring of patient’s physiological signs with wearable sensors to augment traditional medical practice without recourse to storing data with a trusted authority. However, existing Blockchain algorithms cannot accommodate the huge volumes, security, and privacy requirements of health data. In this thesis, our first contribution is an End-to-End secure eHealth architecture that introduces an intelligent Patient Centric Agent. The Patient Centric Agent executing on dedicated hardware manages the storage and access of streams of sensors generated health data, into a customized Blockchain and other less secure repositories. As IoT devices cannot host Blockchain technology due to their limited memory, power, and computational resources, the Patient Centric Agent coordinates and communicates with a private customized Blockchain on behalf of the wearable devices. While the adoption of a Patient Centric Agent offers solutions for addressing continuous monitoring of patients’ health, dealing with storage, data privacy and network security issues, the architecture is vulnerable to Denial of Services(DoS) and single point of failure attacks. To address this issue, we advance a second contribution; a decentralised eHealth system in which the Patient Centric Agent is replicated at three levels: Sensing Layer, NEAR Processing Layer and FAR Processing Layer. The functionalities of the Patient Centric Agent are customized to manage the tasks of the three levels. Simulations confirm protection of the architecture against DoS attacks. Few patients require all their health data to be stored in Blockchain repositories but instead need to select an appropriate storage medium for each chunk of data by matching their personal needs and preferences with features of candidate storage mediums. Motivated by this context, we advance third contribution; a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The mapping between health data features and characteristics of each repository is learned using machine learning. The Blockchain’s capacity to make transactions and store records without central oversight enables its application for IoT networks outside health such as underwater IoT networks where the unattended nature of the nodes threatens their security and privacy. However, underwater IoT differs from ground IoT as acoustics signals are the communication media leading to high propagation delays, high error rates exacerbated by turbulent water currents. Our fourth contribution is a customized Blockchain leveraged framework with the model of Patient-Centric Agent renamed as Smart Agent for securely monitoring underwater IoT. Finally, the smart Agent has been investigated in developing an IoT smart home or cities monitoring framework. The key algorithms underpinning to each contribution have been implemented and analysed using simulators.Doctor of Philosoph

Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaverses

Metaverse is expected to emerge as a new paradigm for the next-generation Internet, providing fully immersive and personalised experiences to socialize, work, and play in self-sustaining and hyper-spatio-temporal virtual world(s). The advancements in different technologies like augmented reality, virtual reality, extended reality (XR), artificial intelligence (AI), and 5G/6G communication will be the key enablers behind the realization of AI-XR metaverse applications. While AI itself has many potential applications in the aforementioned technologies (e.g., avatar generation, network optimization, etc.), ensuring the security of AI in critical applications like AI-XR metaverse applications is profoundly crucial to avoid undesirable actions that could undermine users' privacy and safety, consequently putting their lives in danger. To this end, we attempt to analyze the security, privacy, and trustworthiness aspects associated with the use of various AI techniques in AI-XR metaverse applications. Specifically, we discuss numerous such challenges and present a taxonomy of potential solutions that could be leveraged to develop secure, private, robust, and trustworthy AI-XR applications. To highlight the real implications of AI-associated adversarial threats, we designed a metaverse-specific case study and analyzed it through the adversarial lens. Finally, we elaborate upon various open issues that require further research interest from the community.Comment: 24 pages, 11 figure

Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE) applications. These 5G challenges are behind worldwide efforts to enable future networks, such as sixth generation (6G) networks, to efficiently support sophisticated applications ranging from autonomous driving capabilities to the Metaverse. Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data. This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behavior prediction. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the existing surveys on machine learning for 6G IoT security and machine learning-associated threats in three different learning modes: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, including backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a side-by-side comparison of the state-of-the-art defense methods against edge learning vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed