Cloud computing is a massive amount of dynamic ad distributed resources that are delivered on request to clients over the Internet. Typical centralized cloud computing models may have difficulty dealing with challenges caused by IoT applications, such as network failure, latency, and capacity constraints. One of the introduced methods to solve these challenges is fog computing which makes the cloud closer to IoT devices. A system for dynamic congestion management brokerage is presented in this paper. With this proposed system, the IoT quality of service (QoS) requirements as defined by the service-level agreement (SLA) can be met as the massive amount of cloud requests come from the fog broker layer. In addition, a forwarding policy is introduced which helps the cloud service broker to select and forward the high-priority requests to the appropriate cloud resources from fog brokers and cloud users. This proposed idea is influenced by the weighted fair queuing (WFQ) Cisco queuing mechanism to simplify the management and control of the congestion that may possibly take place at the cloud service broker side. The system proposed in this paper is evaluated using iFogSim and CloudSim tools, and the results demonstrate that it improves IoT (QoS) compliance, while also avoiding cloud SLA violations.

The cloud computing model offers a shared pool of resources and services with diverse models presented to the clients through the internet by an on-demand scalable and dynamic pay-per-use model. The developers have identified the need for an automated system (cloud service broker (CSB)) that can contribute to exploiting the cloud capability, enhancing its functionality, and improving its performance. This research presents a dynamic congestion management (DCM) system which can manage the massive amount of cloud requests while considering the required quality for the clients’ requirements as regulated by the service-level policy. In addition, this research introduces a forwarding policy that can be utilized to choose high-priority calls coming from the cloud service requesters and passes them by the broker to the suitable cloud resources. The policy has made use of one of the mechanisms that are used by Cisco to assist the administration of the congestion that might take place at the broker side. Furthermore, the DCM system is used to help in provisioning and monitoring the works of the cloud providers through the job operation. The proposed DCM system was implemented and evaluated by using the CloudSim tool.

The development of technology and the availability of patient data have been increasing the leveraging of a data-driven approach to improve diagnostic accuracy. This research introduces a virtual diagnosis program that employs neural networks to predict diseases based on a dataset of 4,920 patients and 132 symptoms. Through exploratory data analysis and correlation analysis, significant associations between symptoms and diseases are identified. The developed system achieves an impressive accuracy rate of 95.6% in diagnosing diseases by utilizing advanced optimization techniques for training the neural network model. This accuracy demonstrates the potential of the program to assist healthcare professionals in making accurate diagnoses, enhancing the precision and efficiency of disease identification. The data-driven approach of this virtual diagnosis tool complements medical expertise, offering valuable support for timely and accurate diagnoses.

Phishing could be considered a worldwide problem; undoubtedly, the number of illegal websites has increased quickly. Besides that, phishing is a security attack that has several purposes, such as personal information, credit card numbers, and other information. Phishing websites look like legitimate ones, which makes it difficult to differentiate between them. There are several techniques and methods for phishing detection. The authors present two machine-learning algorithms for phishing detection. Besides that, the algorithms employed are XGBoost and random forest. Also, this study uses particle swarm optimization (PSO) and grey wolf optimizer (GWO), which are considered metaheuristic algorithms. This research used the Mendeley dataset. Precision, recall, and accuracy are used as the evaluation criteria. Experiments are done with all features (111) and with features selected by PSO and GWO. Finally, experiments are done with the most common features selected by both PSO and GWO (PSO ∩ GWO). The result demonstrates that system performance is highly acceptable, with an F-measure of 91.4%.

This study examines the determinants of cybersecurity behaviour among employees in higher education institutions (HEIs) in the United Arab Emirates (UAE) through an integrated framework combining the Protection Motivation Theory (PMT), the Theory of Planned Behaviour (TPB), and the COM-B model. Using a convergent mixed-methods design, survey data from 250 participants and interviews with 18 staff members were analysed to identify behavioural drivers and construct a predictive model. Results showed that policy familiarity, security culture, and intrinsic motivation were the strongest predictors of secure behaviour, collectively explaining nearly 70% of variance. The predictive neural network model achieved high accuracy (F1 = 91.99%, R² = 0.94), with interpretability analysis ranking policy familiarity as the top determinant. Qualitative insights reinforced these findings, revealing that policy clarity, leadership engagement, and supportive culture enable consistent compliance. The study contributes a validated, theory-driven predictive framework for HEIs, bridging behavioural science and cybersecurity governance. The findings are based on self-reported cybersecurity behaviour and should be interpreted accordingly. It concludes that effective institutional cybersecurity depends on clear policies, inclusive culture, and sustained motivation rather than fear-based enforcement.

The security of Internet of Things (IoT)–Low-Power and Lossy Networks (LLNs) is crucial for their widespread adoption in various applications. The standard routing protocol for IoT-LLNs, IPv6 Routing Protocol over Low-Power and Lossy Networks (RPL), is susceptible to insider attacks, such as the version number attack (VNA), decreased rank attack (DRA), and increased rank attack (IRA). These attacks can significantly impact network performance and resource consumption. To address these security concerns, we propose the IbiboRPLChain Solution, a secure blockchain-based authentication method for RPL nodes. The proposed solution introduces an additional blockchain layer to the RPL architecture, enabling secure authentication of communication links between the routing layer and the sensor layer. The IbiboRPLChain Solution utilises smart contracts to trigger immediate authentication upon detecting routing attacks initiated by malicious nodes in an IoT-LLN environment. The evaluation of the proposed solution demonstrates its superior performance in mitigating insider attacks and enhancing IoT-LLN security compared to existing methods. The proposed solution effectively mitigates insider attacks by employing blockchain technology to authenticate communication links between routing and sensor nodes. This prevents malicious nodes from manipulating routing information and disrupting network operations. Additionally, the solution enhances IoT-LLN security by utilising smart contracts to trigger immediate authentication upon detecting suspicious activity, ensuring prompt action against potential threats. The findings of this research have significant implications for the development and deployment of secure IoT-LLNs.