Comprehensive Survey: Biometric User Authentication Application, Evaluation, and Discussion

This paper conducts an extensive review of biometric user authentication literature, addressing three primary research questions: (1) commonly used biometric traits and their suitability for specific applications, (2) performance factors such as security, convenience, and robustness, and potential countermeasures against cyberattacks, and (3) factors affecting biometric system accuracy and po-tential improvements. Our analysis delves into physiological and behavioral traits, exploring their pros and cons. We discuss factors influencing biometric system effectiveness and highlight areas for enhancement. Our study differs from previous surveys by extensively examining biometric traits, exploring various application domains, and analyzing measures to mitigate cyberattacks. This paper aims to inform researchers and practitioners about the biometric authentication landscape and guide future advancements

Data Security and User Authentication in Public Cloud Computing Environments

Cloud computing has become a prevalent paradigm for organisations and individuals, offering a range of services and resources that enable the efficient management, processing and storage of large quantities of data. However, despite its numerous advantages, cloud computing also presents significant challenges related to data security and user authentication. The distributed nature of cloud environments, coupled with the relinquishment of control over data to cloud service providers, has raised concerns regarding data vulnerability to various types of attacks, such as data breaches, malware injection, and denial of service. Moreover, traditional password and token-based authentication methods have been shown to be susceptible to a range of attacks, including brute force, interception, and unauthorised sharing. In response to these challenges, this thesis proposes a novel system architecture that enhances data security and user authentication in cloud computing environments by incorporating data fragmentation and biometric modalities. The proposed architecture consists of several key components, including a data fragmentation and secure storage module, a biometric authentication module, and a data access control module. By dividing data into multiple fragments and storing them across multiple cloud storage providers or locations, the architecture aims to protect data from unauthorised access and reconstruction by attackers. The biometric authentication module leverages individuals\u27 unique physiological or behavioural characteristics to provide a higher level of security compared to more traditional methods of authentication.To evaluate the effectiveness of the proposed architecture, a literature review is conducted, examining the state-of-the-art data security techniques for cloud environments, as well as biometric methods for user identification and authentication. The results of the performance analysis highlight the potential of the proposed architecture in addressing the data security and user authentication challenges associated with cloud computing. The distributed nature of the proposed system allows for the mitigation of risks related to data breaches and other cyber attacks. In addition, its performance against encryption demonstrates the potential of usage in environments where speed is paramount. Moreover, by integrating data fragmentation and biometric authentication techniques, the system provides a comprehensive solution that can be combined with other cloud data security mechanisms, to enhance the overall security of cloud environments further.This thesis contributes to the existing body of knowledge on cloud data security and biometric-based user authentication by proposing a novel system architecture that addresses the unique challenges associated with data security and user authentication in cloud computing environments. The study also identifies potential future research directions, such as exploring new data fragmentation and encryption techniques, improving biometric authentication methods, and investigating the impact of emerging technologies on cloud data security and user authentication

Deep Learning in Diverse Intelligent Sensor Based Systems

Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have fundamentally transformed the way the data are acquired, processed, analyzed, and interpreted. With the rapid development of deep learning technology and its ever-increasing range of successful applications across diverse sensor systems, there is an urgent need to provide a comprehensive investigation of deep learning in this domain from a holistic view. This survey paper aims to contribute to this by systematically investigating deep learning models/methods and their applications across diverse sensor systems. It also provides a comprehensive summary of deep learning implementation tips and links to tutorials, open-source codes, and pretrained models, which can serve as an excellent self-contained reference for deep learning practitioners and those seeking to innovate deep learning in this space. In addition, this paper provides insights into research topics in diverse sensor systems where deep learning has not yet been well-developed, and highlights challenges and future opportunities. This survey serves as a catalyst to accelerate the application and transformation of deep learning in diverse sensor systems

Enhancing societal security: a multimodal deep learning approach for a public person identification and tracking system

In public spaces, threats to societal security are a major concern, and emerging technologies offer potential countermeasures. The proposed intelligent person identification system monitors and identifies individuals in public spaces using gait, face, and iris recognition. The system employs a multimodal approach for secure identification and utilises deep convolutional neural networks (DCNNs) that have been pretrained to predict individuals. For increased accuracy, the proposed system is implemented on a cloud server and integrated with citizen identification systems such as Aadhar/SSN. The performance of the system is determined by the rate of accuracy achieved when identifying individuals in a public space. The proposed multimodal secure identification system achieves a 94% accuracy rate, which is higher than that of existing public space person identification systems. Integration with citizen identification systems improves precision and provides immediate life-saving assistance to those in need. Utilising secure deep learning techniques for precise person identification, the proposed system offers a promising solution to security threats in public spaces. This research is necessary to investigate the efficacy and potential applications of the proposed system, including accident identification, theft identification, and intruder identification in public spaces

Seguridad y privacidad en sistemas biométricos distribuidos incluyendo cadena de bloques y aprendizaje federado: nuevos esquemas de protección y de-identificación

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de Lectura: 17-05-2024This Thesis is focused on the security and privacy of state-of-the-art deep learning-based biometric systems in centralized and distributed paradigms. The main scientific goals of this PhD are 1) to analyze the main challenges and requirements of contemporary biometric recognition systems in terms of both security and privacy in single and multi-modal modalities; 2) to evaluate several state-of-the-art approaches, the information stored, the recognition performance, the potential advantages, and limitations; and 3) to review existing and propose novel metrics and algorithms to measure and improve the levels of security and privacy (noninvertibility, revocability and unlinkability) of biometric templates for both modalities. The Thesis addresses corresponding problems from a holistic perspective and proposes solutions based on state-ofthe- art in two main scientific disciplines: artificial intelligence using machine learning and deep learning methods, and cryptography. Due to the high level of popularity and deployment of face biometrics, the solutions given in this Thesis were practiced but not limited to this trait. In particular, we contributed to biometric protection by proposing a novel biometric template protection method. In addition, we addressed privacy preservation in biometrics using different strategies. To this end, we studied the impact of combining blockchain and biometrics in terms of its pros and cons on the security and privacy of biometrics. Moreover, we experimented with the privacy-by-design biometric models in distributed systems. Lastly, the utilization of adversarial examples as a method for preserving privacy in biometric systems has been implemented. This Dissertation consists of five parts. Part I first introduces the biometric systems and modalities that are being used in the current era, as well as a detailed description of related works for defining current security and privacy risks. This part finishes by providing metrics and databases that are used for this PhD study. The next three parts (Part II, III, IV ) address three research paths that this PhD study has pursued to fulfill the aforementioned scientific goals. Part II is focused on biometric template protection (BTP) as the forefront concept for fighting vulnerabilities threatening the security of biometric systems. This part gives a thorough explanation of the proposed novel BTP method, OTB-Morph, for biometric verification. In the third part (Part III of this Dissertation), biometric security and privacy for distributed systems and collective learning have been addressed. This part presents an interdisciplinary comprehensive survey for combining biometrics and blockchain from both technical and legal perspectives. Furthermore, it takes the security challenges of the federated learning paradigm for face recognition into consideration. The last experimental part (Part IV of this Dissertation) is completely focused on privacy preservation in biometric systems. To this end, this part recounts the detail of using adversarial examples as a privacy preservation method to de-identify face biometrics from personal photos. Finally, drawn from experimental findings Part V concludes this Thesis and presents the main line of the future work for the security and privacy of contemporary biometric systemsThe research described in this Thesis was carried out within the Biometrics and Data Pattern Analytics Laboratory - BiDA Lab at the Dept. of Tecnología Electrónica y de las Comunicaciones, Escuela Politécnica Superior, Universidad Autónoma de Madrid (from 2021 to 2023). The project was funded by a Marie Sk˚Aodowska-Curie Scholarship from the EU ITN project PriMa (ITN-2019- 860315

Secure and robust machine learning for healthcare: A survey

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks. In this paper, we present an overview of various application areas in healthcare that leverage such techniques from security and privacy point of view and present associated challenges. In addition, we present potential methods to ensure secure and privacy-preserving ML for healthcare applications. Finally, we provide insight into the current research challenges and promising directions for future research

Enhanced Efficiency and Productivity through AAMS

The Traditional attendance management systems, which rely on human operations or RFID-based solutions, frequently struggle with scalability, accuracy, and efficiency. This thesis proposes an Automated Attendance Management System (AAMS) that employs a customized YOLOv9-C model for real-time facial recognition via deep learning. The model's performance is significantly improved by adding Squeeze-and-Excitation (SE) blocks and the Complete Intersection over Union (CIoU) loss function. On a custom dataset, the baseline YOLOv9-C model had 86.2% precision and 84.9% recall, with a mean Average Precision (mAP) of 89.9% at IoU threshold of 0.5. However, the revised YOLOv9-C(M) model demonstrated significant gains, including a mAP of 93.8%, as well as improved precision (94.1%) and recall (96.6%). These improvements can be due to the introduction of SE blocks, which promote feature recalibration, and the CIoU loss function, which maximizes bounding box localization and increases detection accuracy even in tough conditions such as occlusion or dimly lit areas. The improved YOLOv9-C model consistently outperforms the existing YOLO models (YOLOv5, YOLOv7, and YOLOv8s), according to a comparison study. The mAP for YOLOv5 was 80.2%, YOLOv7 was 89.1%, and YOLOv8s was 91.4%. In contrast, the upgraded YOLOv9-C model outperformed the others, with greater robustness, precision, and recall. The system employs a one kind of custom dataset to evaluate the model's performance in some scenarios and settings, as well as to ensure trustworthy workforce detection in diverse contexts. By automating the attendance process, this technology reduces errors, saves administrative time, and promotes institutional efficiency

Gait analysis from encrypted video surveillance traffic

This thesis proposes an original video-based gait analysis technique, different from others existing in the literature. We leverage deep learning techniques to analyze video sequence packet size both in a virtual and real environment. Moreover, we address the case in which encryption mechanisms are adopted and we conclude the study proposing an incremental learning framework to render the system suitable to real life applications where training data becomes progressively available over time.ope

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)