Tariq Alwada’n

High-performance resource orchestration and robust data security represent critical, often competing, operational objectives in modern cloud computing architectures. This study presents a unified infrastructure framework designed to reconcile these requirements by integrating a Geographically Aware Placement Algorithm (GAPA) with an Artificial Intelligence-Driven Monitoring system (AIDAM). GAPA dynamically optimizes workload distribution based on regional server capacity and geographic proximity, while AIDAM leverages deep unsupervised autoencoders for real-time anomaly detection and threat mitigation. The framework was evaluated via deterministic simulation using production traces from the Google Cluster Data (2019) corpus under a systematic injection of volumetric Distributed Denial-of-Service (DDoS) anomalies. The empirical results demonstrate a 92% macro-averaged threat detection accuracy rate against low-and-slow traffic variations alongside a minimal cryptographic processing latency overhead of 3–5% relative to an unencrypted baseline scheduling configuration. Furthermore, the integrated pipeline achieved a 25% reduction in end-to-end network latency compared to traditional non-geographically aware heuristic models. These findings demonstrate that cloud infrastructure efficiency and security resilience can be simultaneously enhanced without requiring comprehensive physical re-engineering.

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 Internet of Things (IoT) continues to expand rapidly, with the Routing Protocol for Low-Power and Lossy Networks (RPL) serving as its core communication backbone. However, RPL remains vulnerable to a range of insider routing attacks such as the Version Number Attack (VNA) and Hello Flooding Attack (HFA), particularly in constrained IoT environments. In our previous work, IbiboRPLChain, we proposed a blockchain-based authentication mechanism to secure communication between routing and sensor nodes. This paper presents an evolved framework, IbiboRPLChain II, which integrates smart contracts, decentralised authentication nodes, and composite blockchain mechanisms to improve network resilience, scalability, and security. Our experiments, conducted using Cooja and Contiki OS, evaluate the system across multiple simulation seeds, demonstrating significant gains in Packet Delivery Ratio (PDR), energy efficiency, and delay mitigation. IbiboRPLChain II proves to be a robust solution for secure, lightweight, and scalable RPL-based IoT environments.

Cybersecurity in higher education institutions (HEIs) is increasingly recognized as a behavioral and organizational challenge, not solely a technical one. This study investigates the perceptions, awareness, and attitudes of faculty and staff in UAE HEIs toward cybersecurity policies and practices, with a focus on how institutional communication, policy frameworks, and cultural dynamics shape behaviors. Semi-structured interviews were conducted with academic and administrative staff, generating rich qualitative data that were analyzed thematically. The findings reveal three dominant themes. First, cybersecurity training and communication are often symbolic, generic, and disconnected from daily professional contexts, limiting their ability to enhance coping appraisal. Second, misalignment between policy and practice results in frustration and circumvention, as rigid protocols undermine perceived behavioral control and conflict with academic autonomy. Third, organizational culture and peer dynamics play a decisive role in shaping norms of compliance or non-compliance, with weak cultural embedding leaving security viewed as an IT department responsibility rather than a shared institutional value. These findings were integrated into a conceptual model grounded in Protection Motivation Theory (PMT), the Theory of Planned Behavior (TPB), and the COM-B framework. The model demonstrates how institutional mechanisms and cultural factors converge on employee attitudes, which in turn drive secure or insecure behaviors. The study advances theoretical understanding of cybersecurity as a behavioral phenomenon and offers practical recommendations for HEIs, including role-specific training, participatory policy design, and cultural embedding. By addressing attitudinal drivers, HEIs in the UAE can build more resilient cybersecurity cultures in increasingly complex threat landscapes.

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.

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 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 sensors layer. 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.

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.

This study explores the relationship between cybersecurity culture and employee behaviour in higher education institutions (HEIs) in the United Arab Emirates (UAE), using a cross-sectional, mixed-methods approach. The sample comprised 246 employees across faculty (43.5%), administrative staff (48%), and management (8.5%) roles, with varying levels of experience and technical readiness. Quantitative analyses revealed that perceived cybersecurity culture was the most significant predictor of behaviour (Spearman’s ρ = 0.62, p = 0.003), with behaviour scores ranging from 37.91 in “Developing Culture” contexts to 75.98 in “Exemplary Culture” settings. ANOVA results indicated significant differences in behaviour across role (F(2,83) = 5.72, p = 0.009) and experience levels (F(5,79) = 3.94, p = 0.021), with early-career staff scoring the lowest. Best practice adherence further explained behavioural variance (F(5,79) = 9.84, p < 0.001). Qualitative analysis identified three core challenges: restrictive system controls, outdated training, and leadership disengagement. These findings highlight the behavioural and contextual drivers of cybersecurity and emphasize the need for culture-first strategies that align institutional norms, communication, and leadership modelling with digital security objectives.

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%.

Large amounts of data are created from sensors in Internet of Things (IoT) services and applications. These data create a challenge in directing these data to the cloud, which needs extreme network bandwidth. Fog computing appears as a modern solution to overcome these challenges, where it can expand the cloud computing model to the boundary of the network, consequently adding a new class of services and applications with high-speed responses compared to the cloud. Cloud and fog computing propose huge amounts of resources for their clients and devices, especially in IoT environments. However, inactive resources and large number of applications and servers in cloud and fog computing data centers waste a huge amount of electricity. This paper will propose a Dynamic Power Provisioning (DPP) system in fog data centers, which consists of a multi-agent system that manages the power consumption for the fog resources in local data centers. The suggested DPP system will be tested by using the CloudSim and iFogsim tools. The outputs show that employing the DPP system in local fog data centers reduced the power consumption for fog resource providers.