Machine learning has become ubiquitous across industries for its ability to uncover in- sights from data. This research explores the application of machine learning for identifying phishing websites. The efficiency of different algorithms at classifying malicious sites is evaluated and contrasted. By exposing the risks of phishing, the study aims to develop reliable systems for fake website detection. The results showcase machine learning's capabilities for augmented cybersecurity through automated threat intelligence. Phishing employs social engineering techniques to disguise malicious links as trusted entities, tricking victims into revealing sensitive information. This work investigates phishing detection leveraging curated lists and machine learning for adaptive defense.

Integrating virtual reality (VR) and augmented reality (AR) technology into online stores enables more immersive and engaging shopping experiences, which is crucial for businesses to succeed in today's competitive e-commerce market. These technologies offer unique, personalized experiences that consider the preferences and requirements of each customer. This research aims to understand better the most recent developments in AR and VR technology, and how these technologies might be used in e-commerce. Multiple databases were used to conduct a thorough search, and the inclusion criteria focused on using AR and VR in e-commerce. A total of 55 papers were found and categorized based on the research methodologies and issues used. Based on the findings of the research paper, it can be concluded that integrating AR and VR technologies in e-commerce has significant potential to improve various aspects of the online shopping experience.

Blockchain technology and the metaverse are revolutionizing digital experiences by merging secure, cryptographic systems with shared virtual spaces. These advancements have immense potential across various sectors, including social interaction, education, and commerce. Despite these enhancements, current research remains fragmented. Thus, a comprehensive exploration of this domain is needed. This article conducts a systematic mapping study to understand research trends, challenges, and opportunities in the blockchain-based metaverse. The authors scrutinized 43 articles, categorizing them based on research themes and methodologies. Their analysis revealed a significant focus on technical aspects like blockchain implementation in virtual worlds and the design of virtual economies. The authors also found research that explores the social and cultural impacts of the blockchain-based metaverse. This study, thus, underscores the potential of the evolving field, emphasizing the need for research to fully understand its technical and societal implications. It also stresses the importance of evaluating the long-term sustainability, scalability, and potential risks associated with this promising, complex domain.

The combination of artificial intelligence (AI) methods like machine learning with cryptography is essential for more intelligent and adaptable security measures. This chapter of the book outlines the convergence of AI with cryptography in detail. It summarizes the foundations of cryptography and the major AI techniques such as machine learning, deep learning, and evolutionary algorithms. The merging of AI & cryptography to maximize the potential of automated cryptanalysis that comes in terms of exposing the vulnerabilities, state of the art ciphers that follow an improved cryptographic design, enhanced intrusion detection in the form of anomaly recognition and advanced secure collaboration protocols for safe collaborative learning without sacrificing the secrecy of information. Applications in domains like finance, healthcare, and internet of things (IoT), among others, are further proofs of the benefits of program crypto-AI techniques. In addition, adversarial actions against the candidate and ethical issues are also the challenges.

Large Language Models (LLMs) have emerged as transformative tools in business decision-making, offering capabilities such as automation, market analysis, and customer insights. However, their integration into business processes presents significant limitations, including biases in training data, lack of domain-specific knowledge, interpretability issues, and ethical concerns. These challenges can affect the quality and reliability of business decisions, potentially leading to biased or erroneous outcomes. This paper evaluates the key limitations of LLMs in business contexts and explores strategies to mitigate these issues. Proposed solutions include diversifying training data to reduce biases, fine-tuning models with industry-specific expertise, implementing explainable AI techniques for better transparency, and establishing ethical guidelines to safeguard against misuse. By addressing these limitations, businesses can harness the full potential of LLMs, ensuring more effective, fair, and accountable decision-making processes. This research provides a comprehensive approach for mitigating the risks associated with LLMs in business environments.