In today's digital age, the convergence of Artificial Intelligence (AI) and biometric security systems has become increasingly vital for ensuring the authenticity and security of digital identities. Biometric security systems, a key component of this technological revolution, rely on the utilization of unique biological traits to authenticate individuals, such as electrocardiogram (ECG), electroencephalogram, fingerprints, iris scans, and facial recognition. By leveraging these distinctive biological traits, biometric security systems provide a robust and reliable means of verifying individuals' identities. These systems offer several advantages over traditional authentication methods, such as passwords or personal identification numbers (PINs), which can easily be compromised or forgotten. Biometric authentication is convenient, as it requires individuals to present their unique traits for identification, eliminating the need for additional tokens or passwords.

This chapter aims to provide a comprehensive survey of the various methods used to analyze the performance of biometric security systems. The chapter will begin by providing an overview of the background of biometric security, including a definition of biometric security and the different types of biometric technologies used. The chapter will then delve into the various metrics used to evaluate the performance of biometric security systems, including false acceptance rate (FAR), false rejection rate (FRR), receiver-operating characteristic (ROC) curve, equal error rate (EER), failure to enroll (FTE) rate, and failure to acquire (FTA) rate. The chapter will then examine the different methods used to evaluate the performance of biometric security systems, including testing and evaluation standards, performance testing procedures, and performance testing protocols. The chapter will also discuss various tools available for biometric security performance testing, analysis, and evaluation, including open-source and proprietary tools. In addition, the chapter will explore the security vulnerabilities of biometric security systems and the various threat models and attacks that these systems may face. The chapter will also discuss the different countermeasures and mitigation techniques that can be used to prevent and mitigate these attacks.

Finally, the chapter will present several case studies of biometric security performance evaluation, highlighting best practices and lessons learned from these evaluations. The chapter will conclude with a summary of key findings, implications for future research, and recommendations for biometric security performance evaluation.

Face recognition technology is a hot issue that today's neural networks need to continue to study in depth. With the development of deep neural networks, face recognition technology has achieved great results even in some datasets, and the computer's ability to recognize faces has exceeded human eye observations. However, with the widespread use of face recognition technology, the problem of privacy protection of face information has emerged. This chapter analyzes and summarizes the development status and technical achievements of face recognition technology under the federal learning framework. First of all, the definition status and development status of federated learning and traditional face recognition technology are introduced. Then the analysis summarizes the relevant achievements of face recognition technology based on the federated learning framework that have been proposed so far. Finally, we will analyze the possible problems and prospects for future development.

The study comprehensively evaluates deep learning models for detecting email phishing attacks. The study applies deep learning models, including convolutional neural networks, recurrent neural networks (RNNs), long short-term memory (LSTM), and bidirectional long short-term memory, to a dataset of phishing and legitimate emails. It evaluates their performance based on several metrics, including accuracy and precision, recall, and F1-score. The LSTM model achieved the highest accuracy of 99.41%, outperforming all other models, while the RNN model had the worst accuracy among all the models evaluated. This chapter provides valuable insights into the potential of deep learning models for detecting email phishing attacks. It highlights the need for further research to develop more effective and reliable approaches. The study also underscores the importance of developing robust and accurate phishing detection systems, given the significant impact that emails phishing attacks can have on individuals and organizations. The findings of this study have practical implications for the development of email phishing detection systems and provide a foundation for future research in this area.

Conventional alphanumerical passwords have been the most common authentication medium for years; however, it is plagued by a fundamental problem: secure strong passwords are hard to create and remember, leading to insecure practices. There have been several studies proposing alternatives to replace alphanumerical passwords. However, such alternatives have not reached mainstream usage or widespread application. Our study has reviewed several authentication schemes and found that graphical authentication can be an alternative solution, leveraging visual components instead of text and taking advantage of the human mind's ability to remember graphical and positional information. Our study conducts an in-depth analysis of the existing graphical passwords and presents a graphical one-time-password (OTP) scheme as a replacement for alphanumerical passwords. The proposed one-time graphical password is built upon the user remembering their four preselected picture passwords displayed on a random 4×4 matrix and entering the (x,y) coordinate of the picture password on the matrix. Several different categories of picture passwords are provided, with the authentication matrix displaying decoy images along with the picture passwords. An in-depth analysis of the security, memorability and usability aspects is presented to show that our graphical OTP scheme can successfully thwart common graphical password attacks (such as shoulder-surfing and dictionary attacks). We also demonstrate that the proposed graphical authentication system can protect against various known attacks and satisfies different criteria as a replacement for alphanumerical passwords.

The chapter on "Ethical and privacy concerns and challenges" delves into the critical aspects of ethics and privacy in the context of artificial intelligence (AI) in social cybersecurity. In today's digital age, where AI technologies are increasingly integrated into various aspects of our lives, including cybersecurity practices, it is essential to recognize the potential ethical and privacy implications that accompany their use. This chapter aims to provide an in-depth exploration of these concerns and their significance in shaping responsible and sustainable AI deployment in the realm of social cybersecurity.