Customer Relationship Management (CRM) systems are essential tools for understanding customer behavior, enhancing engagement, and facilitating business growth. As more customer data is collected, the risks of privacy breaches and unauthorized access increase, making the challenge of data protection more significant. The primary focus of this research is to construct and deploy a hybrid Bobcat-based Federated AES Model (BbF-AESM) to enhance CRM practices via the confidentiality and processing of sensitive customer information. In this approach, Bobcat optimization is used to adjust the model’s parameters for better results and Accuracy. Federated Learning (FL) enables collaborative training of the model without exposing raw customer data, and AES encryption ensures that the data remains private during both transmission and storage. The proposed model was tested and validated against CRM databases in a Python environment, and performance evaluation utilized measures such as F-measure, Recall, Accuracy, AUC, and Precision. The outcome showed that the hybrid BbF-AESM surpassed other systems, achieving 98.5% accuracy, while the error rate was just 1.5%. These results emphasize the model’s ability to provide exact customer predictions and secure data management. Overall, the proposed framework facilitates improved privacy preservation and enhances computational efficiency and scalability in CRM systems.

The Internet of Things (IoT) is envisioned to link billions of diverse devices together. To safeguard privacy of user data, ensure authenticity of the data communicated between these devices, and guarantee their availability, robust authentication is needed. On the other hand, authentication techniques face several security challenges because IoT devices are resource-constrained (memory, battery, processor, etc.). To elaborate on this, we perform an extensive survey of authentication mechanisms in IoT, addressing the resource-constrained nature of devices, heterogeneity of networks, and prospective security challenges. We focus on five major classes of authentication protocols—namely, password-based, MAC-based, public identity-based, token-based, and biometrics-based protocols—and compare them based on communication and computation overhead, energy efficiency, scalability, and security. We also highlight the applicability of authentication mechanisms to four popular IoT platforms: RFID systems, smart grids, the Internet of Vehicles (IoV), and smart homes. Finally, we survey trending solutions such as implicit authentication using biometrics, blockchain-based identity management, and trusted computing. Beyond the qualitative comparison, this updated survey presents a formal, PRISMA-style review process including inclusion and exclusion criteria; a quantitative/information theoretic comparison of authentication classes (using Shannon entropy and confusion- matrix-derived metrics, the false acceptance rate, false rejection rate, equal error rate, and receiver operating characteristic reasoning); and discussion of higher-layer auth architectures including federated identity management, single sign-on (SSO), and OAuth/OpenID Connect that enable device-level protocols. Existing systematic reviews and auth schemes published since 2023 are also included to supplement previously compared device-centric works. Our analysis shows that token-based authentication currently offers the most balanced trade-off among security, scalability, and energy overhead for general-purpose IoT deployments, while biometric and implicit biometric schemes provide the strongest resistance to impersonation at higher computational cost and public-identity (asymmetric) schemes scale well but remain impractical for the most resource-constrained endpoints. In summary and to guide future work directly, the survey also outlines four promising directions towards which the community should further research: (i) hybrid schemes based on lightweight cryptography and behavioral or physiological biometrics; (ii) lightweight post-quantum authentication schemes, resistant to quantum computer attacks and deployable on constrained devices; (iii) continuous authentication and anomaly detection based on machine learning techniques; and (iv) federated and decentralized (blockchain-enabled) identity management solutions towards the minimization of single points of failure among heterogeneous IoT areas.

Due to the increasing complexity of cyberattacks, old-school cybercrime detection methods are no longer effective. This chapter looks at how machine learning-based intrusion detection systems and anomaly detection solutions can revolutionise the way cybersecurity is done today. By using various different machine learning (ML) algorithms, such as supervised, unsupervised, and reinforcement learning and find outliers (e.g. identify anomalies), detect new types of threats, and automatically respond to security incidents. This chapter provides a thorough overview of how ML is used for intrusion detection, including the use of deep learning algorithms for threat intelligence, behavioural and static analysis of malware, identifying phishing schemes using natural language processing, and detecting zero-day attacks. Additionally, this chapter dealing with unbalanced datasets, defending against adversarial attacks, using federated learning to cover multiple locations, and enabling explainability for ML-based cybersecurity.