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.