Financial fraud remains a critical challenge for digital banking, requiring detection solutions that ensure both scalability and data privacy. Traditional centralized approaches face limitations due to security risks and system bottlenecks. This paper proposes FedFraud, a novel federated learning framework that detects fraudulent transactions without sharing raw data. FedFraud introduces two key innovations: (i) a trust-aware client aggregation mechanism that assigns weights based on update reliability, and (ii) an asynchronous communication protocol enabling clients to contribute updates independently. Evaluated on the Credit Card Fraud Detection dataset under a non-IID setup, FedFraud achieves an F1-score of 0.90 and AUC of 0.96, outperforming FedAvg and state-of-the-art methods like FedGAT and FL-BGAT. It also reduces privacy leakage to 1.5% and limits gradient reconstruction success to 15%, compared to 35% for FedAvg. Scalability tests show stable convergence with up to 100 clients, establishing FedFraud as an effective and privacy-preserving solution for decentralized financial fraud detection.

In this study, a new encryption model has been developed using w-cyclic codes over the finite field defined on the Eisenstein-Jacobi integer ring. In the newly developed encryption model, a key of the same length as the message is used to create a secure encryption model, ensuring its unbreakability.

The DNS systems serves an important role in accessing and managing domain names. Despite its importance, DNS is vulnerable to numerous network-based attacks such as Distributed Denial-of-Service DDoS attacks. Unfortunately, no comprehensive solution has been discovered to combat such attacks. In this paper, we proposed an approach to enhance DNS security by adopting RAID technology and data coding. This approach relies on multiple independent DNS servers holding distinct coded DNS records instead of relying on a single DNS server. Our proposed solution shows efficient results in overcoming data loss and disruption of DNS server operation. This solution lies on the fact that if one server is attacked or damaged, the other servers will continue working to resolve IP addresses without disruption. The results obtained indicate that the time taken to process multiple DNS requests may slightly increase compared to single DNS operation. However, the time required for DNS recovery from DDoS attack is significantly larger when only a single server is being used, which may take hours or even days

The increasing reliance on machine learning (ML) models in critical applications has heightened concerns about model poisoning attacks, where adversaries manipulate training data to compromise model integrity. To address this challenge, we propose a comprehensive framework for detecting ML model poisoning using a novel integration of forensic analysis techniques. Unlike traditional approaches that focus on input data anomalies, our framework analyzes the model itself to uncover signs of poisoning. We integrate reverse engineering (model inversion), topological data analysis, Shapley values, and Benford’s Law analysis to systematically identify subtle deviations indicative of adversarial manipulation. We evaluate our proposed framework on multiple deep learning architectures, including convolutional neural networks, ResNet-18 and YOLOv8, used to train models using the CIFAR-10 dataset. Further, we benchmark our framework against existing forensic analysis and ML model poisoning detection techniques, highlighting its improved detection capabilities. Our findings establish a robust and interpretable methodology for securing ML models against sophisticated model poisoning attacks, paving the way for enhanced resilience in real-world deployments.

The rapid expansion of Artificial Intelligence (AI) in digital platforms used by youth has created significant challenges related to privacy, autonomy, and data protection. While AI-driven personalization offers enhanced user experiences, it often operates without clear ethical boundaries, leaving young users vulnerable to data exploitation and algorithmic biases. This paper presents a call to action for ethical AI governance, advocating for a structured framework that ensures youth-centred privacy protections, transparent data practices, and regulatory oversight. We outline key areas requiring urgent intervention, including algorithmic transparency, privacy education, parental data-sharing ethics, and accountability measures. Through this approach, we seek to empower youth with greater control over their digital identities and propose actionable strategies for policymakers, AI developers, and educators to build a fairer and more accountable AI ecosystem.

By increasing smart objects as high-potential computing devices and 5G technology, the Internet of Things (IoT) has emerging technology to provide a data-centric infrastructure for detecting safe device-to-device communications by supporting security and privacy issues. As 5G-enabled communication technology, Vehicle-to-Vehicle (V2V) communication provides a wirelessly exchange information connection between smart vehicles, intelligent devices and cloud-edge computing environment. This connection should be established with a safe and secured run-time protection system to avoid many critical anomalies and misbehavior problems. Detecting run-time malicious transformations with data-centric misbehaving reactions is a main challenge for autonomous vehicle communications with 5G-enabled communication technology. This paper provides a hybrid Genetic Algorithm-based Ensemble Bagged Trees (GA-EBT) algorithm for a data-centric misbehavior detection approach to support the V2V communications against malicious and misbehavior transactions. For evaluation of the proposed algorithm, four real test-cases are applied for messaging injection attacks in the V2V environments with compare to the state-of-the-art machine learning algorithms. The experimental results show that the proposed hybrid approach can achieve to optimal high rate accuracy factor with 99.999, precision and recall factors with 100% and F1-Score factor with 100% to detect unexpected cyber-attacks for the V2V communications in the IoT environment.

The increasing need to protect individual privacy in data releases has led to significant advancements in privacy-preserving technologies. Differential Privacy (DP) offers robust privacy guarantees but often at the expense of data utility. On the other hand, data pooling, while improving utility, lacks formal privacy assurances. Our study introduces a novel hybrid method, termed PoolDiv, which combines differential privacy with data pooling to enhance both privacy guarantees and data utility. Through extensive simulations and real data analysis, we assess the performance of synthetic datasets generated via traditional DP methods, data pooling, and our proposed PoolDiv method, demonstrating the advantages of our hybrid approach in maintaining data utility while ensuring privacy.

Aiming to address issues such as low security, inefficient encryption and decryption, weak robustness, image tampering, and the misuse of existing image encryption algorithms in blockchain-based applications for image sharing and transmission, this paper proposes a traceable image privacy protection scheme based on blockchain and homomorphic algorithm. Firstly, Paillier algorithm and image block technology were used to preprocess and encrypt the image. Feature images are extracted from encrypted images using lifting wavelet transform (LWT) and singular value decomposition (SVD). By means of dynamic S-box, binary watermark information is subjected to confusion, diffusion and XOR operations to generate zero-watermarking images for lossless embedding of traceability information. Then, the zero-watermarking image and its corresponding key are securely stored on the International File System (IPFS) through a smart contract mechanism to regulate image transactions and enable traceability of the image. Finally, the smart contract is utilized to detect any inaccuracies and errors in the zero-watermarking image, enabling tamper detection of said image. In experimental tests, the proposed scheme was shown to enable data distribution and computation with private data in ciphertext state, exhibiting high efficiency in encryption and decryption. Additionally, the zero-watermarking algorithm exhibits strong anti-attack capabilities and robustness. By considering image privacy protection and secure storage, tamper detection and traceability of encrypted images are effectively addressed.

In Industrial Internet of Things (IIoT), the absence of standardized forensic processes presents substantial hurdles to effective investigations. As IIoT devices become ubiquitous in critical infrastructure, ensuring consistency and reliability in forensic procedures becomes imperative. This paper aims to front the critical need for standardized IIoT forensic processes, challenges, impact and industry and government roles in achieving this. In addition, a taxonomy of forensic processes in IIoT has been suggested. By addressing concerns, the objective is to pave the way for consistent and effective forensic investigations across varied IIoT landscapes through standardized approaches.

Connectivity and data exchange are key features of Industry 4.0. In this paradigm, (Industrial) Internet of Things ((I)IoT) devices are a vital component facilitating the collection and transmission of environmental data from the physical sys- tem to the central station for processing and analysis(Digital Twin). However, although (I)IoT devices play a critical role in this process, they are not inherently equipped to run strong encryption mechanisms to secure the data they transmit over wired or wireless channels. This research aims to explore the potential of Digital Twins (DT) in securing Industry 4.0 appli- cations and the security mechanism employed to ensure con- fidentiality, integrity, and authentication of data communi- cated between (I)IoT and DT through a Systematic Literature Review (SLR). This systematic literature review, based on the analysis of 67 papers published between 2018 and 2023, un- derscores the evolving significance of Digital Twin technol- ogy, particularly within the ambit of Industry 4.0. The find- ings illuminate the pervasive influence of Digital Twin tech- nology across multiple industrial sectors. The result SLR re- vealed that DT is growing and being widely adopted as a se- curity tool particularly in Industry 4.0 using enabling tech- nology like machine learning, data analytics, blockchain, and 5G networks to provide security solutions such as intrusion detection, vulnerability assessment, cyber range, and threat intelligence.

The Internet of Things (IoT) is a new well-structured emerging technology with communication of smart devices using the 5G technology, infrastructures of roads, vehicles, smart cities, traffic systems and user applications. The IoT applications facilitate providing prompt emergency responses, and improved quality of vehicles, and road services, with cost-effective activities in the intelligent transportation systems. According to the growth of 5G technology and complex communications between smart devices and intelligent vehicles, Federated Learning is applied to support privacy and security factors in safety-critical transportation systems using innovative prediction methods in Internet of Vehicle (IoV) environments. Therefore, Federated Learning (FL) approaches can support safety, privacy and security for the sensitive personal information of users and critical aspects of vehicles in intelligent transportation systems and IoV. This comprehensive review paper delves into the innovative integration of blockchain technology with federated learning and the dynamic domain of IoV. It extensively analyzes the primary concepts, methodologies, and challenges associated with the deployment of FL in IoV. This study categorically examines three main types of federated learning approaches vertical, horizontal, and decentralized each tailored to specific IoV communication scenarios like Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Cloud (V2C). The study emphasizes various applications of FL, including cyber-attack detection, data sharing, traffic prediction, and privacy preservation based on critical Quality of Service (QoS) factors. Finally, some main challenges and new open issues are discussed and assessed for federated machine learning approaches in the IoV.

This systematic literature review scrutinizes the implementation and analysis of zk-SNARK, zk-STARK, and Bulletproof non-interactive zero-knowledge proof (NIZKP) protocols in privacy-preserving applications across diverse sectors. Examining 43 research works published from 2015 to April 2023, we categorized findings into financial, medical, business, general, and other domains. Our analysis highlights significant variations in real-world performance across implementations utilizing NIZKP protocols. However, divergent methodologies in security analyses hindered conclusive comparisons. Addressing research gaps, our future endeavors aim to establish a real-world benchmark for these protocols.

This paper shows how the Generalized Novel Enhancement Quantum Representation (GNEQR) and the Novel Enhancement Quantum Representation (NEQR) can encrypt color and grayscale healthcare images with quantum algorithms. The proposed method ensures the security of medical media, which is crucial for safeguarding patient confidentiality and safety, and is supported by e-health systems. Healthcare facility staff members send cipher color images to the cloud, which they then receive at a different facility. By decrypting the content of the images, healthcare staff can securely assist users. C# and Asp.net core MVC on Visual Studio 2022 were utilized to implement the proposed encryption approach, and Azure cloud was used. The e-health system gives the proposed method a safe and effective way to be used in real life. The proposed algorithm uses bit-plane scrambling to scramble the original image. Then, a 9D chaotic map is utilized to generate an image key, which is used to produce the key image and the scrambled position. A quantum XOR operation is performed between the scrambled image and the scrambled position of the key image. The final encrypted image is made by mixing up the color channels of the image. A similar approach is followed for grayscale images, but instead of using GNEQR, a Novel Enhancement Quantum Representation (NEQR) is employed. Additionally, the color channels are not scrambled in this case. Analyses of numbers and simulations show that the proposed method is more effective, reliable, and useful than its classical counterpart. The proposed method can be used with different types of medical images, such as those from radiology and pathology, and can be used in telemedicine. It provides a secure way to transmit medical images without compromising patient privacy. Overall, the proposed framework for quantum encryption of healthcare images using GNEQR and NEQR could change how medical images are sent and protected. It is expected to impact the healthcare industry significantly and can be applied in various e-health systems.

The extension of emerging renewable energy sources such as wind and water turbines, solar panels, and the increasing usage of electric vehicles requires the supply and distribution of energy in a small device on local scale and it has created new methods of supplying and selling electricity. Middle buyers and end users can obtain the local energy with the peer-to-peer trading method in this large and hierarchical market. This method enables market to manage and exchange the electricity between major suppliers and medium and local levels. Blockchain technology is developing in peer-to-peer exchange of electricity and acts as a reliable, efficient and safe technology in the electricity trading market. In this method, while preserving the privacy of electricity users, by using smart contracts and by removing intermediaries in the energy supply and demand market, direct commercial interactions between energy suppliers and consumers are done. The Blockchain technology, while creating trust between the parties in the energy market, reduces the cost of electricity trading and increases its scalability with using the intermediate energy aggregators. In this research, the blockchain-based model, is presented for distribution and peer-to-peer transactions in the energy market. The suggested model provides the possibility of registration low-cost instant transactions at the power grid in any specific period of time. The above method, unlike periodic payments, provides immediate access to bills and small payments. Since the transactions outside the blockchain chain are not recorded, this system guarantees its honest and independent operation without fraud and failure. The smart contract method based on blockchain, reduces the transaction fees and speeds up electricity trading.

Physicians and scientists hope that health-data will provide new insights into improving medical care and optimizing healthcare costs. However, data protection laws in Europe often place limits on the use of patient data. During the COVID-19 pandemic, both digital immunity records and data on infections were needed for pandemic management. Using this example, the research project aims to create a system concept for vaccination, testing, and recovery proof called P3VT (Privacy Preserving Pass for Vaccination and Testing), that will make collected data available to research and policy pandemic management in real time and anonymized. P3VT was developed consistently considering the goals of privacy-by-design, data minimisation and transparency of the EU-GDPR. Expert interviews validated the system from medical, technical and privacy perspectives. P3VT offers the following advantages compared to the EU digital COVID certificate: Pseudonymized proof of vaccination, testing, and recovery, reducing misuse of sensitive personal data Transparency about the nature, timing, and purpose of the proof strengthens user trust Use of anonymous data improves pandemic management, and research P3VT is thus an exemplary solution for the comprehensive provision of health-data for research purposes in combination with high level of data protection. Further areas of application are conceivable.

Blockchain is emerging as a solution to secure healthcare records but faces certain shortcomings like transaction time, execution time, gas consumption, etc. The current article designed an extensive blockchain-based healthcare system (MyEasyHeathcare) with reduced gas consumption and execution time, along with enhanced security at three levels. At the first level, the professionals and patients get registered, which provides identity access management. Secondly, authorization is required for each registered entity by the owners. Lastly, the third level includes a doctor-patient relationship where a specific patient is assigned to a particular doctor by the hospital’s owner. The data is protected from the outer world and is preserved only between the doctor and the patient. Moreover, to include the majority of tasks for hospital management, the developed system incorporates a smart contract to record seven different parameters for patient diagnosis by physician and fifteen different parameters by a pathologist. The designed system is evaluated for the amount of gas consumed and execution cost to decide the usage in the real world; the results testify the proposed system is useful in the real world.

All forms of data are meant to be secured. The rise in cyber-crime since beginning of the pandemic and the emergence of a variety of techniques and tactics utilized by malware authors has made identification and recovery difficult. LockBit ransom-ware has been in news in recent times. The APT behind deployment of this ransom-ware virus has compromised several organizations and the impacts are, worth more than millions, lives of people by stealing their personal identifiable information. It is important to understand the behavior of such malware, to block it and to identify the anti-analysis stuff it performs. The challenges here were a packed sample, a rebooting command-line trigger and encryption of several files that can possess high trouble to operations of any workplace.

Recently, blockchain-based IoT solutions have been proposed that address trust limitation by maintaining data consistency, immutability, and chronology in IoT environments. However, IoT ecosystems are resource-constrained and have low bandwidth and finite computing power of sensor nodes. Thus, the inclusion of blockchain requires an effective policy design regarding consensus and smart contract environments in heterogeneous IoT applications. Recent studies have presented blockchain as a potential solution in IoT, but an effective view of consensus and smart contract design to meet the end application requirements is an open problem. Motivated by the same, the survey presents the integration of suitable low-powered consensus protocols and smart contract design to assess and validate the blockchain-IoT ecosystems. We discuss the key blockchain concepts and present the scalability and performance issues of consensus protocols to support IoT. Further, we discuss smart contract vulnerabilities and blockchain attacks. Open issues and future directions are presented, supported through a case study of low-powered consensus protocol design in the blockchain- IoT ecosystem. The survey intends to drive novel solutions for future consensus and safe, smart contract designs to support applicative IoT ecosyst