IoT Security Concerns with Non-Fungible Tokens: A Review
By Ashwag Alotaibi, Huda Aldawghan, M. M. Hafizur Rahman
PDFAbstract
This study summarizes the body of research on the IoT and NFTs overlap, highlighting important security concerns, the function of blockchain technology, and implications for future study and smart environment applications. IoT devices provide creative solutions that boost operational effectiveness and enhance user experiences as they spread throughout different sectors. But there are also serious drawbacks to this expansion, especially in terms of security and privacy. At the same time, NFTs unique digital assets verified by blockchain technology—have become extremely popular because of their unique features and wide range of uses. This paper carefully looks at how security frameworks in digital ecosystems may be impacted by the integration of IoT and NFTs. The results emphasize how urgently this integration must be studied further to minimize new risks and maximize the advantages of IoT and NFTs across a variety of sectors. The study intends to contribute to a more secure and effective IoT ecosystem by examining the difficulties presented by this integration. Contributing to the development of a more robust and secure IoT ecosystem is the ultimate aim of this research. This study aims to open the door for future developments that optimize the benefits between the two technologies while reducing risks by recognizing and evaluating the difficulties brought about by the integration of IoT and NFTs. Both academics and industry stakeholders navigating the rapidly changing IoT and blockchain world will find great significance in the results of this research.
PhishGuard: AI-Driven Graph-Based Analysis for Smarter Email Security
By Harchana Ramesh, Noris Ismail, Nor Azlina Abd Rahman, Aitizaz Ali
PDFAbstract
This research presents a phishing detection system that integrates graph analytics and machine learning to improve email security. As phishing tactics become more sophisticated, traditional filters often fail to detect such threats effectively. This project proposes a dual-model solution: a RoBERTa-based transformer is used to classify the email body content, while a Neo4j-powered graph model analyses sender-receiver domain relationships using graph metrics such as PageRank, ArticleRank, and Degree Centrality. The rule-based system intelligently combines the predictions of the two models. Highly confident RoBERTa results are accepte d directly, whereas for the remaining cases, scores from the graph model are applied. For mid-confidence cases, a fixed rule-based thresholding logic is used to ensure robust classification. For real-time detection, a web interface was developed using Streamlit, integrating Gmail API and Google Apps Script for email quarantine. The system achieved an F1 score above 0.99 in testing, marking it as a fully stable system for spam identification. By combining content and relational signals, the work advances email security and accordingly fulfils Sustainable Development Goal 9 by fostering innovation infrastructure in digital safety.
Securing Healthcare Digital Twin with Blockchain: A Systematic Review of Architecture, Threats and Evaluation
By Dawood Alalisalem, Hafizur Rahman
PDFAbstract
Recently, it has been noted that the convergence of blockchain technology presents a promising paradigm for secure, privacy-preserving, and transparent healthcare systems. Moreover, Digital Twins enable real-time replication of patients, hospital operations, and medical devices, and their dependence on continuous sensitive data streams introduces the latest trust and Cybersecurity challenges. A systematic literature review aims to investigate how distributed ledger and blockchain technologies have been applied to secure healthcare digital twins from 2020 to 2025. Furthermore, the review addresses the proposed architecture of blockchain, the security objectives targeted, integration approaches within digital twins, and evaluation methods with limitations. The study follows PRISMA 2020 guidelines. Web of Sciences, IEEE Xplore, PubMed, Scopus, and ACM Digital Library were searched from January 2020 to October 2025 by using defined Boolean queries. Also, the focus of the inclusion criteria is on peer-reviewed studies that discussed blockchain for DT security in healthcare. Data extraction captured blockchain type, metadata, security mechanisms, DT domain, and evaluation methods. From the 487 identified records, only 20 successfully met the inclusion criteria. The fact behind it is that most studies only employed permissioned blockchains like Quorum and Hyperledger integrated with digital twins for monitoring patients, device lifecycle tracking, and data provenance. Some main security objectives include provenance assurance, access control, and integrity. Moreover, only some studies provide formal threat analysis or real-world deployment. Blockchain technology is reliable because it increases digital twin security through immutability, smart-contract-based governance, and decentralized trust. However, interoperability, scalability, and privacy-preserving computation remain the main barriers for clinical adoption.
A Multi-Layered Adaptive Cybersecurity Framework for the Banking Sector Integrating Next-Gen Firewalls with AI-Driven IDPS
By Sokroeurn Ang , Mony Ho , Sopheatra Huy , Midhunchakkaravarthy Janarthanan
PDFAbstract
The accelerated digital transformation of the banking sector has enhanced the delivery of financial services but simultaneously expanded the cyberattack surface, exposing institutions to advanced persistent threat (APT), zero-day exploit, and obfuscated malware. Conventional perimeter defenses, primarily Layer 3 and 4 firewalls and signature-based intrusion detection systems (IDS), offer insufficient protection against encrypted, evasive, and previously unknown cyberattacks, and frequently generate high false-positive rates that burden Security Operations Center (SOC). This study proposes a multilayered adaptive cybersecurity framework that integrates Layer 7 Next Generation Firewall (NGFW), hybrid Network and Host-based Intrusion Detection and Prevention System (NIDPS/HIDPS), and an AI-driven analysis engine. The framework employs a dual-stage detection architecture, combining Convolutional Neural Network (CNN) for spatial representation learning and Random Forest (RF) classifiers for anomaly decisioning. The model was evaluated using a strategically consolidated dataset derived from CIC-IDS-2017 and UNSW-NB15, specifically isolating cyberattack vectors prevalent in financial infrastructures (e.g., SQL Injection, DDoS, and Brute Force). The model achieves 99.65% detection accuracy and a reduced false-positive rate of 0.35%, significantly outperforming classical SVM and standalone signature-based systems. The results demonstrate that the proposed architecture aligned with NIST and PCI-DSS standard as well as defense-in-depth mechanism suitable for real-time, high-frequency financial environments.
A Secure Industrial Internet of Things (IIoT) Framework for Real-Time PI Control and Cloud-Integrated Industrial Monitoring
By Huda S. Jaafar, Ali A. Abed, Mahmood A. Al-Shareeda
PDFAbstract
The explosion of Industry 4.0 has augmented the demand for secure, agile, and real-time industrial monitoring and control applications. In this paper, a secure Industrial Internet of Things (IIoT) system for real-time PI control and cloud-based industrial monitoring is proposed. The developed system combines Siemens S-300 PLC (programmable logic controller) and Node-RED platform that provides real-time data acquisition, display, and remote control for industrial parameters like Level/Water temperature. An integrated end-to-end data pipeline is implemented to allow transparent information exchange between the PLC, Node-RED dashboard, MQTT-based services, and cloud infrastructures, providing real-time data synchronization as well as remote access. The PI controller, whose parameters can be adjusted online using a website or mobile app, is implemented by our framework without loss of control loop stability. Safety is improved by authentic access and the regulation of the user’s rights in order to avoid undelegated system operation. The experimental validation in a real-world industrial automation lab reveals the robustness of the real-time monitoring, enabling concurrent visualization over multiple interfaces and satisfactory PI control performance. The results substantiate the efficiency, applicability, and reliability of the developed IIoT system for real-world industrial monitoring and control scenarios.
Cybersecurity Risks and Challenges in Smart Cities: A Review with Insights for Cambodia
By Mony Ho, Sokroeurn Ang, Sopheaktra Huy, Midhunchakkaravarthy Janarthanan
PDFAbstract
Smart Cities depend on interconnected digital systems, IoT devices, cloud platforms, and continuous data exchange to deliver efficient and innovative public services. However, this high level of integration increases exposure to cybersecurity risks that can disrupt essential operations and compromise citizen privacy. This review examines major cybersecurity threats affecting Smart City infrastructures, including IoT weaknesses, data exposure, DDoS attacks, surveillance system intrusion, and cloud security issues. It then analyzes Cambodia’s specific challenges such as limited legal frameworks, fragmented governance, unstable infrastructure, shortages of cybersecurity skills, and financial constraints. Global frameworks including the NIST Cybersecurity Framework, Zero Trust Architecture, IoT security models, and Smart City security architectures are reviewed to identify best practices. A gap analysis highlights significant differences between international standards and Cambodia’s current readiness. Finally, the study proposes strategic recommendations to strengthen national policies, enhance technical and human capability, and improve infrastructure resilience. The findings provide valuable guidance for policymakers and stakeholders seeking to advance secure and sustainable Smart City development in Cambodia.
IoT-Driven Enterprise Development and Supply Chain Risk Management in Healthcare Organizations
By Mahmood A. Al-Shareeda, Iman Asker Hawi
PDFAbstract
In fact, it’s difficult to manage supply chain risk in today’s health care landscape where demand is uncertain, when suppliers are interrupted, under regulatory constraints or make for an extremely complicated environment. The Internet of Things (IoT) has become a key digital enabler for enhancing visibility, coordination and risk-aware decision-making within healthcare supply chains. Nevertheless, the empirical knowledge concerning how IoT capabilities sup- port orchestrating efficient organization-level supply chain risk management is still scant, especially in focus to enterprise development. Empirical research is employed to explore the relationship between IoT capabilities, firm growth and supply chain risk management among healthcare organizations. The data were obtained by a structured questionnaire conducted among physicians, nurses and management staff of information system department, enterprise department and supplies chain department. The collected data were analyzed and hypotheses tested using Structural Equation Modeling. The findings show that IoT competence has a positive and significant impact on corporate development and healthcare supply chain risk management. In addition, there is a significant mediating effect of enterprise development that enhances healthcare organizations’ capability for exploring, controlling and responding to supply chain risks. The implication from these findings is that even though IoT technologies provide a boost to real-time monitoring and visibility, their value in addressing supply chain risks gets significantly augmented with the integration and maturity of organizational capabilities. This research makes contributions to the literature by empirically demonstrating IoT-based risk management in healthcare supply chains and pinpointing enterprise development as a key organizational solution for translating technological capabilities into resilient and risk-aware supply chain practices. The results provide the management insiders with practical guidance to better improve supply chain resilience through strategic adoption of IoT and organizational formation.
A Smart Dashboard Framework for Urban Tourism Risk Analysis Using Deep Learning and Machine Learning
By Adona Kulathinal Josephi, Mahmud Maqsood
PDFAbstract
The study proposes an intelligent framework to evaluate tourism safety in Indian cities by integrating diverse, real-world data source, including crime statistics, hotel ratings, and user reviews. The methodology employs advanced artificial intelligence techniques, notably a fine-tuned BERT model for classifying user reviews into safety-related sentiment categories and XGBoost for predicting crime pattern prediction and city-level safety score computation. The analytical pipeline includes comprehensive data preprocessing, sentiment classification, predictive modeling, and cluster analysis to uncover patterns and associations. Cities are segmented into distinct risk categories based on crime density and public sentiment, enabling nuanced safety profiling. A noteworthy finding is the strong inverse relationship between tourist satisfaction and crime rates, underscoring the significant influence of safety perceptions on a destination’s attractiveness. The final output is an interactive Power BI dashboard that supports real-time filtering, geospatial analysis, sentiment mapping, and predictive insights. This decision-support system enables travelers to make informed choices, assists policymakers in identifying high-risk areas, and assists urban planners in designing targeted safety interventions. Overall, the research addresses a critical gap in tourism safety information and demonstrates the potential of AI in developing data-driven, transparent, and responsive tools for smart tourism management.