E-health systems have revolutionized healthcare by enabling efficient data sharing and management. However, they face significant security and privacy challenges, including unauthorized access, data breaches, identity fraud, and insurance fraud. Existing solutions attempt to address these issues but suffer from single points of failure, lack of patient-defined access control, and inadequate privacy-preserving mechanisms. This paper proposes a dual-blockchain architecture integrated with Self-Sovereign Identity and Zero-Knowledge Proofs to enhance security, privacy, and fraud resilience. The framework employs Decentralized Identifiers and Verifiable Credentials for secure authentication while leveraging the InterPlanetary File System for decentralized Electronic Health Records storage. By addressing the limitations of current systems, the proposed solution ensures a more secure, scalable, and privacy-preserving e-health environment.

Port management is critical to the maritime transportation industry. Effective port management aims, among other things, to reduce the amount of time required to operate the cargo volume of the vessels. Numerous uncertainty factors, such as weather and mechanical issues, frequently affect maritime transportation, which can hamper port operations planning. The lack of certainty surrounding a number of relevant parameters makes efficient preparation for quayside operations difficult. As a result, when making decisions, it is critical to account for the possibility of uncertainty. The ability to deal with unfavorable and unpredictable events is one of the characteristics of a great practical solution. In this paper, we investigate a scenario in which machine learning techniques are used to predict data for both the berth allocation and the quay crane assignment problems. We model the problem as a mixed integer linear program with multiple objectives that takes berth allocation and quay crane assignment into account. We propose a genetic algorithm scheme to solve the problem. Crossover and mutation procedures are provided. Finally, managerial implications and future research directions are given.

Distributed Denial of Service (DDoS) attacks are becoming increasingly common and sophisticated, and supply chain networks are particularly vulnerable to these types of attacks due to their reliance on interconnected systems. Software-Defined Networking (SDN) has the potential to enhance resilience against DDoS attacks by providing a centralized control mechanism and the ability to reroute traffic dynamically. However, traditional methods for detecting and mitigating DDoS attacks in SDN-based networks may not be sufficient to protect against these threats fully. In this paper, we propose the use of machine learning techniques to detect and mitigate DDoS attacks in SDN-based supply chain networks. We will investigate how the centralized control mechanism of SDN can be leveraged to improve the effectiveness of machine learning-based DDoS attack detection and mitigation. Additionally, we evaluated these techniques' performance and effectiveness and discussed the trade-offs and limitations of using machine learning for DDoS attack detection and mitigation in SDN-based supply chain networks.

Blockchain technology provides a promising solution for collaborative economy systems by offering a decentralized, transparent, and secure platform. This is mainly accomplished through smart contracts, which are self-executing computer programs that facilitate, verify, and enforce the negotiation or performance of a contract. Digital tokens, on the other hand, are used to represent assets or currencies in these systems. Despite the benefits of Blockchain-based collaborative economy systems, significant security concerns are associated with them. These include the possibility of fraud, risk assessment, bugs in smart contracts, and cyber-attacks. For instance, attackers can exploit vulnerabilities in smart contracts to perform reentrancy and infinite loop attacks, leading to significant financial losses. To address these security challenges, this paper proposes integrating artificial intelligence models to prevent vulnerabilities in smart contracts and detect anomalies. Specifically, Graph Neural Networks models can be utilized to safeguard Blockchain-based collaborative economy platforms from attacks such as reentrancy and infinite loop attacks. According to the findings, this approach can accurately identify both normal and abnormal traffic and classify specific types of attacks. The framework's performance is further evaluated using various metrics to ensure its effectiveness in detecting anomalies, thereby providing an additional layer of security for Blockchain-based collaborative economy systems.

Software defined networks represent a new centralized network abstraction that aims to ease configuration and facilitate applications and services deployment to manage the upper layers. However, SDN faces several challenges that slow down its implementation such as security which represents one of the top concerns of SDN experts. Indeed, SDN inherits all security matters from traditional networks and suffers from some additional vulnerability due to its centralized and unique architecture. Using traditional security devices and solutions to mitigate SDN threats can be very complicated and can negatively effect the networks performance. In this paper we propose a study that measures the impact of using some well-known security solution to mitigate intrusions on SDN's performances. We will also present an algorithm named KPG-MT adapted to SDN architecture that aims to mitigate threats such as a Man in the Middle, Deny of Services and malware-based attacks. An implementation of our algorithm based on multiple attacks' scenarios and mitigation processes will be made to prove the efficiency of the proposed framework.

Software defined networks (SDNs) represent new centralized network architecture that facilitates the deployment of services, applications and policies from the upper layers, relatively the management and control planes to the lower layers the data plane and the end user layer. SDNs give several advantages in terms of agility and flexibility, especially for mobile operators and for internet service providers. However, the implementation of these types of networks faces several technical challenges and security issues. In this paper we will focus on SDN's security issues and we will propose the implementation of a centralized security layer named AM-SecP. The proposed layer is linked vertically to all SDN layers which ease packets inspections and detecting intrusions. The purpose of this architecture is to stop and to detect malware infections, we do this by denying services and tunneling attacks without encumbering the networks by expensive operations and high calculation cost. The implementation of the proposed framework will be also made to demonstrate his feasibility and robustness.