Security Solutions Research Articles

Research on Challenges and Solutions for Optimizing Read Rate in RFID Systems

Radio Frequency Identification (RFID) uses electromagnetic fields for the automatic identification and tracking of object tags, which is widely used in supply chain management, transport and retail. And key issues in its application encompass reliability, security, and privacy. In contrast to mainstream research that primarily focuses on solutions for security and privacy, the paper concentrates on boosting the overall reliability of RFID systems, particularly in fault detection of read rates. Through the review and analysis of the relevant literature, this paper highlights the key challenges and issues in the RFID field, particularly focusing on read rate. Various methods proposed to improve RFID read rates are reviewed, including hardware optimization, tag design, software algorithm improvement, and environmental adjustments. In addition, it outlines the effectiveness of different strategies, addresses the challenges faced in real-world applications, notes the limitations of the research, and proposes potential directions for future studies. The results demonstrate that anti-collision algorithms such as DFSA and improved binary tree search can effectively reduce signal conflicts in multi-tag environments

SHERM: System of Electronic Health Record Management

The rise of non-communicable diseases (NCDs), particularly in India, highlights the need for innovative health- care solutions. SHERM (System of Electronic Health Record Management Software) addresses these challenges by offering a next-generation platform that integrates machine learning (ML), NFC, and QR technologies to enhance healthcare delivery. SHERM combines advanced data collection techniques, predic- tive analytics, and secure, real-time data access to improve patient outcomes and streamline healthcare processes. Its user-friendly design, developed using the MERN stack and Ionic framework, ensures inclusivity for differently-abled and rural populations while maintaining robust data security. The platform empowers healthcare providers with personal- ized insights for precise diagnoses and treatments, while pharma- ceutical companies gain actionable demographic data to optimize resources. By evolving its repository with minimal manual input, SHERM ensures scalability and long-term efficiency. By bridging gaps in traditional healthcare systems, SHERM demonstrates how technology can address NCD challenges, of- fering a scalable, secure, and accessible solution that enhances healthcare outcomes for all stakeholders. Index Terms—Electronic Health Records, Non-Communicable Diseases, Machine Learning, NFC, QR Code, Healthcare Acces- sibility

A Decentralized Storage and Security Engine (DeSSE) Using Information Fusion Based on Stochastic Processes and Quantum Mechanics

In the context of data security, this work aims to present a novel solution that, rather than addressing the topic of endpoint security—which has already garnered significant attention within the international scientific community—offers a different perspective on the subject. In other words, the focus is not on device security but rather on the protection and security of the information contained within those devices. As we will see, the result is a next-generation decentralized infrastructure that simultaneously integrates two cognitive areas: data storage and its protection and security. In this context, an innovative Multiscale Relativistic Quantum (MuReQua) chain is considered to realize a novel decentralized and security solution for storing data. This engine is based on the principles of Quantum Mechanics, stochastic processes, and a new approach of decentralization for data storage focused on information security. The solution is broken down into four main components, considered four levels of security against attackers: (i) defocusing, (ii) fogging, (iii) puzzling, and (iv) crypto agility. The defocusing is realized thanks to a fragmentation of the contents and their distributions on different allocations, while the fogging is a component consisting of a solution of hybrid cyphering. Then, the puzzling is a unit of Information Fusion and Inverse Information Fusion, while the crypto agility component is a frontier component based on Quantum Computing, which gives a stochastic dynamic to the information and, in particular, to its data fragments. The data analytics show a very effective and robust solution, with executions time comparable with cloud technologies, but with a level of security that is a post quantum one. In the end, thanks to a specific application example, going beyond purely technical and technological aspects, this work introduces a new cognitive perspective regarding (i) the distinction between data and information, and (ii) the differentiation between the owner and the custodian of data.

Intelligent two-phase dual authentication framework for Internet of Medical Things

The Internet of Medical Things (IoMT) has revolutionized healthcare by bringing real-time monitoring and data-driven treatments. Nevertheless, the security of communication between IoMT devices and servers remains a huge problem because of the inherent sensitivity of the health data and susceptibility to cyber threats. Current security solutions, including simple password-based authentication and standard Public Key Infrastructure (PKI) approaches, typically do not achieve an appropriate balance between security and low computational overhead, resulting in the possibility of performance bottlenecks and increased vulnerability to attacks. To overcome these limitations, we present an intelligent two-phase dual authentication framework that improves the security of sensor-to-server communication in IoMT environments. During the registration phase, our framework is based on Elliptic Curve Diffie-Hellman (ECDH) for rapid key exchange, and during real-time communication, our framework uses the Advanced Encryption Standard Galois Counter Mode (AES-GCM) to encrypt data securely. The efficiency of the proposed framework was rigorously tested through simulations that evaluated encryption-decryption time, computational cost, latency, and packet delivery ratio. The security resilience was also evaluated against man-in-the-middle, replay, and brute force attacks. The results show that encryption/decryption time is reduced by over 45%, overall computational cost by 45.38%, and latency by 28.42% over existing approaches. Furthermore, the framework achieved a high packet delivery ratio and strong defense against cyber threats for maintaining the confidentiality and integrity of the medical data across IoMT networks. However, the dual authentication approach doesn’t affect the functionality of medical IoT devices while enhancing IoMT security, which makes it an ideal integration option for existing healthcare systems.

Defend Against Property Inference Attack for Flight Operations Data Sharing in FedMeta Framework

Flight operations data play a central role in ensuring flight safety, optimizing operations, and driving innovation. However, these data have become a key target for cyber-attacks, and are especially vulnerable to property inference attacks. Aiming at property inference attacks in shared application model training, we proposed FedMeta-CTGAN, a novel approach that leverages federated meta-learning and conditional tabular generative adversarial networks (CTGANs) to protect flight operations data. Motivated by the need for secure data sharing in aviation, as highlighted by the Federal Aviation Administration’s requirement for ADS-B Out equipment on aircraft to create a shared situational awareness environment, our method aims to prevent sensitive information leakage while maintaining model performance. FedMeta-CTGAN exploits the natural privacy-preserving properties of a two-stage update in meta-learning, using real data to train the CTGAN model and synthetic fake data as query data during meta-training. Comprehensive experiments using a real flight operation dataset demonstrate the effectiveness of our proposed method. FedMeta-CTGAN adapts quickly to unbalanced data, achieving a prediction accuracy of 96.33%, while reducing the attacker’s inference AUC score to 0.51 under property inference attacks. Our contribution lies in the development of a secure and efficient data-sharing solution for flight operations data, which has the potential to revolutionize the aviation industry.

Data-Driven Technologies for Energy Optimization in Smart Buildings: A Scoping Review

Data-Driven Technologies for Energy Optimization in Smart Buildings: A Scoping Review

Enhanced Intrusion Detection in Drone Networks: A Cross-Layer Convolutional Attention Approach for Drone-to-Drone and Drone-to-Base Station Communications

Due to Internet of Drones (IoD) technology, drone networks have proliferated, transforming surveillance, logistics, and disaster management. Distributed Denial of Service (DDoS) attacks, malware infections, and communication abnormalities increase cybersecurity dangers to these networks, threatening operational safety and efficiency. Current Intrusion Detection Systems (IDSs) fail to handle drone transmission data’s dynamic, high-dimensional nature, resulting in inadequate real-time anomaly identification and mitigation. This study presents the Cross-Layer Convolutional Attention Network (CLCAN), a new IDS architecture for IoD networks. CLCAN accurately detects complex cyber threats using multi-scale convolutional processing, hierarchical contextual attention, and dynamic feature fusion. Preprocessing methods like weighted differential scaling and gradient-based adaptive resampling improve data quality and reduce class imbalances. Contextual attribute transformation captures the nuanced network behaviors needed for anomaly identification. The proposed technique is shown to be necessary and effective by real-world drone communication dataset evaluations. CLCAN outperforms CNN, LSTM, and XGBoost with 98.4% accuracy, 98.7% recall, and 98.1% F1-score. The model has a remarkable AUC of 0.991. CLCAN can handle datasets of over 118,000 balanced data records in 85 s, compared to 180 s for comparable frameworks. This study pioneers a unified security solution for Drone-to-Drone (D2D) and Drone-to-Base Station (D2BS) communications, filling a crucial IoD security gap. It protects mission-critical drone operations with a strong, efficient, and scalable IDS from emerging cyber threats.

MasqueradeGAN‐GP: A Generative Adversarial Network Framework for Evading Black‐Box Intrusion Detection Systems

ABSTRACTThe proliferation of sophisticated malware poses an escalating threat to economic and national security, demanding a reevaluation of cybersecurity in next‐generation wireless networks like 6G. Integrating artificial intelligence (AI) offers a crucial opportunity to enhance network defenses against increasingly complex cyber threats. However, the growing complexity of 6G technologies exposes current Intrusion Detection Systems (IDS) to covert malware exploitation, underscoring the need for more adaptive and resilient detection mechanisms. In response to these challenges, this study presents MasqueradeGAN‐GP, an innovative framework based on Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN‐GP). The MasqueradeGAN‐GP framework consists of a generator, responsible for transforming raw malicious traffic into a semblance of benign activity, and a discriminator, which dynamically assimilates the feature set of the IDS to discern between genuine and adversarial traffic. Additionally, it includes a restrictive modification mechanism to ensure the fidelity of attack vectors. Experiments conducted on the CICIDS 2017 and NSL‐KDD datasets indicate that MasqueradeGAN‐GP effectively evades detection, suggesting its potential for advancing IDS capabilities and reinforcing AI‐driven security solutions within the 6G landscape. This contributes to building a more robust detection system capable of facing adversarial malware attacks in future wireless communications.

Implementasi Software-Defined Network Terintegrasi Firewall pada Proxmox untuk Pengontrolan Konfigurasi Jaringan dan Pengamanan Layanan Container

Virtualization technology has helped companies consolidate various server roles into a single physical server, reducing hardware costs. Hypervisor is a software in virtualization that is used to manage server hardware, allowing multiple Virtual Machines (VM)/Containers (CT) to run on a single physical machine. Companies face various challenges to remain competitive in the digital era, such as the need for rapid deployment of virtual guests and virtual networks on hypervisors in development, testing, and production environments, as well as securing network services. The purpose of this study is to implement SDN on hypervisors to centrally control virtual network configurations with a simple design, reducing setup and maintenance costs and time. In addition, it also implements a firewall and Virtual Private Network (VPN) based on OpenVPN and a reverse proxy to secure the hypervisor and VM/CT so that services remain available. This study presents a new approach that integrates Software-Defined Network (SDN)-based network management with comprehensive security solutions on hypervisors. This approach combines efficiency in network management and security that have rarely been focused on simultaneously in previous studies. The research method uses the Network Development Life Cycle (NDLC). The hypervisor used is Proxmox Virtual Environment (PVE) which is installed on the Virtual Private Server (VPS) provider IDCloudHost. Based on the results of the trials that have been carried out, it can be concluded that the simple zone type SDN on PVE can be used to control network configurations centrally and more simply such as routing, Dynamic Host Configuration Protocol (DHCP), Source Network Address Translation (SNAT), hostname registration and Internet Protocol (IP) from CT to forward lookup zone on the Domain Name System (DNS) server. Activating the firewall and creating rules at the cluster and CT levels from PVE and OpenVPN can protect the infrastructure when accessed both internally and externally. While the implementation of nginx reverse proxy can secure access to HTTP/HTTPS services on CT in PVE.

Enterprise internal audit data encryption based on blockchain technology.

Internal auditing demands innovative and secure solutions in today's business environment, with increasing competitive pressure and frequent occurrences of risky and illegal behaviours. Blockchain along with secure databases like encryption improves internal audit security through immutability and transparency. Hence integrating blockchain with homomorphic encryption and multi-factor authentication improves privacy and mitigates computational overhead. Recently, blockchain applications for internal audits in the enterprise sector are still emerging. Thus, blockchain technology in auditing provides the benefits of enhanced transparency and immutability in data processing, which can establish new solutions for internal auditing but still lacks encryption techniques. The research proposed a framework called "BlockCryptoAudit" to enhance internal audit processes through cryptographic encryption methods and blockchain technology, ensuring secure and transparent audit operations. The proposed approach integrates an additive homomorphic Paillier encryption scheme with blockchain to create a safe and tamper-resident audit trail. Utilizing homomorphic Paillier encryption, BlockCryptoAudit ensures that computations may be performed on encrypted audit data while safeguarding data privacy. The applied blockchain hyperledger component guarantees the immutability and transparency of encrypted audit records, resulting in a decentralized and tamper-resistant record. By limiting data accessibility to authorized individuals based on specified responsibilities, role-based access restrictions handled using smart contracts further strengthen security. The study protects audit data's security and confidentiality by encrypting it and putting it on a blockchain. The study compares the proposed BlockCryptoAudit with models like B-OAP, BSE-DF, and EG-FLB regarding risk mitigation, audit quality, security overhead, and audit trail effectiveness. With little security overhead, BlockCryptoAudit beats out B-OAP, BSE-DF, and EG-FLB in terms of risk mitigation (98%) and audit quality (99%). It is an effective way to improve internal audit processes and guarantee data integrity due to its high performance.

Adaptive Attribute-Based Honey Encryption: A Novel Solution for Cloud Data Security

Adaptive Attribute-Based Honey Encryption: A Novel Solution for Cloud Data Security

Enhancing Smart Healthcare Networks: Integrating Attribute-Based Encryption for Optimization and Anti-Corruption Mechanisms

This study investigates the feasibility and effectiveness of integrating Attribute-Based Encryption (ABE) into smart healthcare networks, with a particular focus on its role in enhancing anti-corruption mechanisms. The study provides a comprehensive analysis of current vulnerabilities in these networks, identifying potential data security risks. An anti-corruption mechanism is designed to ensure data integrity and reliability. The ABE approach is then empirically compared to other prominent encryption algorithms, such as Identity-Based Encryption, Data Encryption Standard, Advanced Encryption Standard, and Rivest-Shamir-Adleman algorithms. These methods are evaluated based on access latency, data transmission speed, system stability, and anti-corruption capabilities. Experimental results highlight the strengths of the ABE algorithm, demonstrating an average access latency of 31.6 milliseconds, a data transmission speed of 3.56 MB/s, and an average system stability of 98.74%. Furthermore, when integrated into anti-corruption mechanisms, ABE effectively protects against data tampering and misuse, ensuring secure data transmission. Compared to alternative algorithms, ABE offers a more efficient, secure, and stable solution for data management within smart healthcare networks, supported by its robust anti-corruption capabilities. This positions ABE as an optimal choice for safeguarding the integrity and security of healthcare data.

Adversarial Machine Learning Challenges in Modern Network Security Systems

Hostile machine learning has network security issues that reduce prediction model accuracy. A full defence against these assaults entails establishing hostile scenarios, strengthening models via strategy training, and applying powerful defences. Small adjustments introduce antagonistic inputs into the research. These teach the model to recognize and withstand deception attempts. The proposed solution competed with Trust Shield, Secure Guard, Defend, and Adversary Block in rigorous performance testing. The recommended strategy has a 95.0% success rate for discovering assaults and a much lower 5.0% false positive rate. This is much superior to conventional approaches. Due to its modest accuracy loss and rapid response, it's effective at fighting assaults. This comprehensive overview demonstrates the wide-scale application of the strategy with minimal resources. Finally, this research emphasizes the need for robust and adaptable AI security. This will assist in creating secure and trustworthy AI solutions to protect sensitive data and ensure prediction model accuracy in an increasingly hostile future.

Deployment of Hybrid Chaotic Hashes for Blockchain Driven Internet 4.0 applications

The evolution of Internet 4.0 demands robust, secure, and scalable solutions to meet the growing needs of digital transactions and interconnectivity, and blockchain technology has emerged as a foundational enabler for these applications. However, blockchain's reliance on traditional cryptographic methods presents vulnerabilities that can be exploited in increasingly sophisticated cyber landscapes. This paper introduces the deployment of Hybrid Chaotic Hashes for enhanced security and efficiency in blockchain-driven Internet 4.0 applications. By integrating chaotic systems with hash functions, hybrid chaotic hashes provide a more unpredictable, complex cryptographic layer that enhances data integrity, confidentiality, and resistance to attacks. The unique properties of chaotic functions—nonlinearity, ergodicity, and sensitivity to initial conditions—make them advantageous for hashing in blockchain environments. This study highlights the practical applicability and resilience of hybrid chaotic hashes which is nonlinear technique in Internet 4.0.

AdversaFlow: Visual Red Teaming for Large Language Models with Multi-Level Adversarial Flow.

Large Language Models (LLMs) are powerful but also raise significant security concerns, particularly regarding the harm they can cause, such as generating fake news that manipulates public opinion on social media and providing responses to unethical activities. Traditional red teaming approaches for identifying AI vulnerabilities rely on manual prompt construction and expertise. This paper introduces AdversaFlow, a novel visual analytics system designed to enhance LLM security against adversarial attacks through human-AI collaboration. AdversaFlow involves adversarial training between a target model and a red model, featuring unique multi-level adversarial flow and fluctuation path visualizations. These features provide insights into adversarial dynamics and LLM robustness, enabling experts to identify and mitigate vulnerabilities effectively. We present quantitative evaluations and case studies validating our system's utility and offering insights for future AI security solutions. Our method can enhance LLM security, supporting downstream scenarios like social media regulation by enabling more effective detection, monitoring, and mitigation of harmful content and behaviors.

LAIOV-5G: Lightweight Authentication Scheme for IoV Based On 5G Technology in Smart City Environment

Abstract: The Fifth Generation (5G) networks have enabled the development of smart cities in which massive amounts of data are collected, stored and disseminated. The ultimate objective of these smart cities is to cut costs and improve security performance. In this environment, Internet of Vehicles (IoV) helps connect vehicles, pedestrians, control rooms, and some roadside infrastructure. Owing to the insecure nature of the communication channel utilized in IoV to exchange information, it is important to develop practical techniques to preserve data confidentiality and privacy. To this end, numerous security solutions have been proposed over the recent past. Unfortunately, most of these authentication techniques have security flaws which endanger the transmitted data, while some of them are highly inefficient. To address these gaps, we present a Lightweight Authentication Scheme for the Internet of Vehicles (IoV) based on 5G technology (LAIOV-5G).The security analysis carried out demonstrates that LAIOV-5G mitigates numerous potential attacks that threaten the IoV communication in a smart city environment. In addition, the performance analysis of LAIOV-5G verifies its effectiveness and efficiency.

SecEdge: A novel deep learning framework for real-time cybersecurity in mobile IoT environments.

The rapid growth of Internet of Things (IoT) devices presents significant cybersecurity challenges due to their diverse and resource-constrained nature. Existing security solutions often fall short in addressing the dynamic and distributed environments of IoT systems. This study aims to propose a novel deep learning framework, SecEdge, designed to enhance real-time cybersecurity in mobile IoT environments. The SecEdge framework integrates transformer-based models for efficient handling of long-range dependencies and Graph Neural Networks (GNNs) for modeling relational data, coupled with federated learning to ensure data privacy and reduce latency. The adaptive learning mechanism continuously updates model parameters to counter evolving cyber threats. The framework's performance was evaluated in a simulation environment using the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets. Results demonstrated that SecEdge outperformed state-of-the-art methods with a detection rate of 98.8% for DoS attacks on NSL-KDD, 98.5% for MitM attacks on UNSW-NB15, and 98.7% for data injection attacks on CICIDS2017.

PrePaMS: Privacy-Preserving Participant Management System for Studies with Rewards and Prerequisites

Taking part in surveys, experiments, and studies is often compensated by rewards to increase the number of participants and encourage attendance. While privacy requirements are usually considered for participation, privacy aspects of the reward procedure are mostly ignored. To this end, we introduce PrePaMS, an efficient participation management system that supports prerequisite checks and participation rewards in a privacy-preserving way. Our system organizes participations with potential (dis-)qualifying dependencies and enables secure reward payoffs. By leveraging a set of proven cryptographic primitives and mechanisms such as anonymous credentials and zero-knowledge proofs, participations are protected so that service providers and organizers cannot derive the identity of participants even within the reward process. In this paper, we have designed and implemented a prototype of PrePaMS to show its effectiveness and evaluated its performance under realistic workloads. PrePaMS covers the information whether subjects have participated in surveys, experiments, or studies. When combined with other secure solutions for the actual data collection within these events, PrePaMS can represent a cornerstone for more privacy-preserving empirical research.

Analysis of OPC Data Using Federated Learning: An Evaluation of Performance and Privacy

This study examines the benefits of applying federated learning (FL) technology to OPC (Operational Performance Control) systems within industrial automation and data analysis processes. FL enables each production facility to process its data locally while only transmitting model parameters to a central server, thereby preserving data privacy. This approach provides significant advantages in industrial environments, particularly concerning data privacy and communication costs. The study evaluates FL's potential to ensure data privacy, reduce communication costs, improve efficiency in training time, and deliver high performance in predictive maintenance and quality estimation. Model performance was analyzed using accuracy, F1 score, precision, and loss metrics; the results demonstrated that FL achieved a 90% accuracy rate, offering competitive performance compared to centralized modeling. In predictive maintenance and quality analysis specifically, FL achieved 85-88% accuracy while reducing network data load by 65%. These findings validate that FL provides a secure, cost-effective, and efficient solution for industrial data analysis processes by eliminating the need for centralized data collection. In conclusion, FL and OPC integration supports data privacy, cost savings, and communication efficiency in industrial processes. The study highlights that FL could become a prevalent technology in industrial data analysis, establishing a new standard particularly in digital manufacturing processes.

Enhancing the Resilience of Cloud-Based Security Solutions: Lessons from CrowdStrike Outage

Over the past few years, the usage of security measures and tools that are based in the cloud has increased dramatically. However, the recent CrowdStrike outage which disrupted many organizations shows the increasing need for securing reliability and resilience of cloud based systems. Most of the existing ones are designed as a reactive approach, we suggest a new methodology focusing on proactive measures based on an AI enabled cascade failure prediction which utilizes cutting edge machine learning techniques, bio inspired resilience patterns and quantum aware architecture to address and mitigate security failures before they happen. Our framework managed to achieve 99.2 percent of successful predictions about potential security failures, and end users were able to reduce system recovery time by 76 percent for all experimental deployments which is a real progression in the area of cloud security resilience.