International Journal of Wireless and Ad Hoc Communication

This study examines the importance of cybersecurity in Unmanned Aerial Vehicles (UAVs) due to the increasing technological advancements and subsequent vulnerabilities in these aerial systems. The rapid integration of UAVs across various sectors has led to a pervasive threat of cyber attacks, which necessitates comprehensive defenses to mitigate potential risks. This research outlines the complex landscape of UAV cybersecurity challenges through an in-depth analysis of attack scenarios and data features within the ECU-IoFT dataset. Using the XGBoost algorithm’s robustness, this study presents a proactive approach to classifying and mitigating cyber threats targeting UAV systems. Our findings demonstrate that XGBoost is effective at identifying different attack vectors, making it a possible key defense mechanism. The insights from this study not only highlight the changing nature of UAV cybersecurity but also provide practical steps for strengthening these aerial systems against imminent cyber threats to ensure their safe and resilient operation across multiple domains.

The study gives a complete plan for lowering disease through the use of ICT in personal healthcare. The Health Pattern Recognition (HPR), Dynamic Risk Assessment (DRA), and Personalized Intervention Strategy (PIS) formulas are all parts of this method. They are used to collect, prepare, and use data. This research focuses on cybersecurity using health pattern recognition (HPR), dynamic risk assessment (DRA), and personalized intervention strategies (PIS). PIS offers a comprehensive disease prevention approach in personal healthcare that takes advantage of technological advancements. Because they integrate secure data processing with privacy-preserving machine learning, these aspects assure the safety and validity of health data collected from wearable devices. This option allows for the assessment of medical records. It may be helpful to analyze the technique's accuracy and adherence to established security standards in order to evaluate its application for disease prediction and preventive health management. The HPR program looks at each person's health information to find trends in diseases and other results using machine learning. This helps with early evaluation and healthcare management that avoids problems. DRA keeps a person's risk rating up to date so that it takes into account any changes in their health. After that, people are given choices based on the results and risks that PIS has predicted. Some of the tests that were used to compare the suggested method to industry standards were accuracy, sensitivity, specificity, precision, and the Matthews Correlation Coefficient. The suggested way seems to work because it has better predicting power, fewer fake positives, and more users who are involved in preventive health management.

Connected Vehicle Systems (CVS) are a combination of transportation and digital technologies that have the potential to revolutionize road safety and efficiency. However, this interconnectivity exposes them to various evolving cyber threats that require proactive detection and mitigation strategies. This study examines the security threat landscape in CVS, focusing on the challenges posed by malicious intrusions, unauthorized access, and vulnerabilities within vehicular networks. By using Deep Neural Networks (DNNs) and conducting an extensive literature review on cybersecurity frameworks, autonomous vehicles, and network vulnerabilities, this research provides a robust methodology for detecting and mitigating attacks in vehicular networks. The results show that the proposed approach is effective with improved predictive capabilities as well as the ability to detect abnormal behaviors. The findings highlight the need for standardized cybersecurity frameworks, cooperation among stakeholders, and continuous improvement of security protocols to ensure safe interconnected vehicular networks in a rapidly changing technological environment.

The era of independent, secure, and scalable networks and applications that Web3.0 promised has arrived. The resilience and reliability of the network are directly tied to the architecture of the consensus mechanisms used in this context. In the paper "Crafting resilient consensus mechanisms for the Web3.0 network through edge intelligence," the authors describe a novel approach to strengthening consensus protocols by leveraging edge computing and artificial intelligence. The primary purpose of this project is to improve Web 3.0 security by implementing consensus methods based on edge intelligence. The goal of this attempt is to reduce the inefficiencies, scalability challenges, and environmental concerns associated with more conventional approaches such as proof-of-work and proof-of-stake. The proposed method combines real-time network research with local transaction verification. This eventually leads to more scalable, secure, and effective consensus procedures, which increases the resilience and greatly decreases the cost of Web3.0 networks.The proposed method recognizes the inefficiencies, lack of scalability, and environmental unfriendliness of standard consensus procedures like the Proof of Work (PoW) and Proof of Stake (PoS) consensus processes. This approach makes use of edge intelligence in real time to assess the state of the network and make appropriate adjustments in response. What emerges is a consensus process that is greener, more scalable, and more successful overall. In addition, we provide the local transaction verification (LTV) technique, which allows edge nodes to validate transactions locally, therefore reducing latency and maximizing transaction efficiency. Our findings demonstrate how edge intelligence might improve Web3.0 consensus processes. Extensive simulations and tests show that the suggested approaches outperform conventional consensus mechanisms in terms of efficiency, security, and scalability. Cost reductions for Web3.0 network operators are also emphasized to emphasize the value of our strategy. Consensus procedures for Web3.0 networks that include edge intelligence provide a viable path toward attaining the required resilience, efficiency, and scalability. This study lays the way for a new age of distributed systems, guaranteeing the resiliency and flexibility essential to the success of Web3.0.  

The Industrial Internet of Things (IIoT) is a challenging environment for ransomware threats, and it requires robust detection mechanisms to protect critical infrastructures. This study explores the complex landscape of ransomware attacks in IIoT and suggests proactive detection strategies. To develop an advanced detection model, this research uses the CATBoost algorithm that can handle categorical features by leveraging a comprehensive dataset that captures various attributes of ransomware incidents. The study also enhances the interpretability of the model by incorporating SHAP (SHapley Additive exPlanations) which explains how individual features affect ransomware identification in IIoT environments. Empirical evaluation demonstrates that the model can accurately classify ransomware instances with high precision and recall rates. Moreover, SHAP explanation reveals important features that influence the decisions made by the model thereby improving its interpretability and trustworthiness. The experimental results indicate that customized detection approaches are important and highlight the effectiveness of CATBoost algorithm in strengthening IIoT systems against ransomware attacks.

The security and privacy of data in an IoT-driven intelligence landscape is a major concern. This research examines the integration of Paillier homomorphic encryption into Federated Learning to enhance security while maintaining individual data privacy in such environments. The interconnectedness of devices in IoT frameworks poses a challenge in maintaining the confidentiality of sensitive information. By using Paillier encryption within Federated Learning, this problem is solved by securing learning parameters while still keeping data private. This approach demonstrates promising improvements without violating privacy through extensive simulations and comparative analyses across different model architectures. The results of this study highlight the potential effectiveness of this method for enhancing security measures in interconnected IoT environments.