The huge volume of data and requests for health insurance led to an increased fraud (counterfeit) in the sector either from stakeholders or users. Therefore, the weakness of traditional systems and lack of transparency in obtaining health insurance cards led to a crisis of confidence on part of both parties, regarding both privacy of the patient’s data or the fraudulent insurance cards. In addition to that, governments are spending a lot of time and money to get rid of this dilemma. Hence, a system that can solve the counterfeiting problem is highly needed. In this paper, we have proposed and implemented a Blockchain (BC) system to prevent counterfeiting in health insurance sector. We carried out several experiments in order to demonstrate the usability and efficiency of the designed system. The results show system strength and effectiveness in term of speed, security and privacy.

- The emergence of Internet of Things (IoT) has raised the need for high quality communications, and high performance networks. 5G cellular communication network exhibits the readiness to provide such high quality communication channels by using various advanced technologies. Device to device communications is a one of multiple technologies that have been suggested in 5G. By the employment of this technology, mobile devices can communicate with each other without the involvement of a base station (BS) which eliminates congestion, expands coverage area and increases throughput. Communicating devices set up a multi-hop path using nearby devices which act as relaying elements, or routers. However, the Self-organizing nature and the lack of centralized control of D2D make it easier to launch multiple types of attacks. In this paper, an intrusion detection system IDS is proposed using machine learning techniques. Eight types of attacks are considered to train the system for intrusion detection, then, multiple classification algorithms have been compared. Finally, a multiobjective model has been designed based on the results of comparison to secure the communication process under D2D technology. The used dataset is generated using Network Simulator NS-2.

Surveillance is becoming the norm in today’s life, especially with the pandemic of COVID-19. Surveillance of public crowds and activity is a controversial issue that can contradict the privacy of individuals (Federal Decree-Law No. (5) of 2012). This paper presents a comparative study of surveillance and privacy regulations and law in the UAE compared to the USA and the EU. The objective of this comparison is to highlight the amendments that have been adopted to improve laws and regulations, the need for further improvement, and the strengths and weaknesses in each of these countries. A discussion of different acts adopted in these countries and comparing them can help security experts to cooperate with legislators in order to rectify shortcomings and improve the acts adopted in their respective countries. Furthermore, we think that such a comparison can help system developers to find an easier way to accommodate the differences in security measures that they have to tackle and incorporate when they are serving customers in these countries and especially in the UAE. A legal framework has been proposed in order to define the maturity level of regulations adopted by a government in regard to surveillance and privacy laws and acts.

Ensuring secure communication for mobile patients in e-healthcare requires an efficient and robust key distribution mechanism. This study introduces a novel hierarchical key distribution architecture inspired by federated learning (FL), enabling seamless authentication for patients moving across different healthcare centers. Unlike existing approaches, the proposed system allows a central healthcare authority to share global security parameters with subordinate units, which then combine these with their own local parameters to generate and distribute symmetric keys to mobile patients. This FL-inspired method ensures that patients only need to store a single key, significantly reducing storage overhead while maintaining security. The architecture was rigorously evaluated using SPAN-AVISPA for formal security verification and BAN logic for authentication protocol analysis. Performance metrics—including storage, computation, and communication costs—were assessed, demonstrating that the system minimizes the computational load and reduces the number of exchanged messages during authentication compared to traditional methods. By leveraging FL principles, the solution enhances scalability and efficiency, particularly in dynamic healthcare environments where patients frequently switch between facilities. This work bridges a critical gap in e-healthcare security, offering a lightweight, scalable, and secure key distribution framework tailored for mobile patient authentication.

Blockchain technology has revolutionized digital financial transactions and asset ownership by enabling decentralized and automated operations through smart contracts. Solidity smart contracts, used in the Ethereum blockchain network, facilitate secure and trustless execution of agreements. However, like any code, smart contracts are prone to vulnerabilities. Considering the assets and value of currency these smart contracts handle, their exploitation leads to severe financial losses and loss of operations. Such exploits have resulted in billions of dollars in stolen or locked assets. In this paper, we present an ensemble multilabel classifier model approach for the automated detection of vulnerabilities in Solidity smart contracts using a real smart contract dataset, with a detailed methodological process that includes processing the dataset. The proposed model stack achieves excellent results with F1 scores ranging from 82.0% to 99.9% for each vulnerability dataset. The proposed model is also compared with common static analyzer tools and models proposed in the literature following a similar approach. Moreover, we package the models into a web application, demonstrating deployment and functionality.