Security Enhancement Research Articles

Unlocking Complexity: D-Wave\'s Role in Quantum Computing Breakthroughs

In this new era of Quantum Computing, a transformative paradigm is emerging for the enhancement of Cyber Security using the special-purpose systems like D-Wave's quantum annealers which helps the encryption system become more resilient. As quantum computing evolves, it provides new paradigms for breaking the traditional encryption methods, as the recent breakthroughs is accomplished by researchers from Shanghai University that uses D-Wave's quantum annealing capabilities in providing the researchers to successfully demonstrated the potential to compromise widely used encryption systems, including RSA and AES. The usage of quantum annealing (QA) that makes the D-Wave’s a specialized quantum system which enables an efficient computation in tackling cryptographic problems, demonstrates the implications for the data security and privacy by applying the quantum-classical hybrid architectures. This research aims to get attention on the urgent need for developing a new security measures in response to the vulnerabilities exposed by the quantum advancements which emphasizes the crucial role of D-Wave's in unlocking the complexities of quantum computing and its implications for the future of cybersecurity.

Perceptions and challenges of blockchain adoption in tourism industry: a study on trust, privacy and security

Purpose This study aims to investigate the perceptions of service providers in the travel and hospitality industry toward the adoption of blockchain technology (BCT), focusing on its impact on consumer experiences and expectations, especially in terms of trustworthiness and the management of privacy and security concerns. Design/methodology/approach The research used a quantitative methodology, collecting data from 135 industry practitioners across five tourism sectors (lodging, connected industries, entertainment, FandB, transportation) in Europe. This approach aimed to understand the diverse perspectives on the benefits and challenges of implementing BCT in their opera Findings The results indicate mixed perceptions regarding blockchain adoption. While there are positive views on BCT’s potential to enhance customer experience and service quality, there are significant concerns about its impact on trust and security. A consensus exists between customer and provider perspectives on BCT’s trustworthiness, but significant differences were observed in views on privacy and security enhancement. Originality/value This research contributes to the existing literature by providing insights into the service providers’ and customers’ perspectives on BCT within the travel and hospitality industry. It underscores the complexities of adopting new technologies and calls for more in-depth studies to address the identified concerns, thereby offering a novel viewpoint on the adoption of distributed ledger technologies in enhancing customer experience and service delivery.

Security enhancement for NOMA-PON with 2D cellular automata and Turing pattern cascading scramble aided fixed-point extended logistic chaotic encryption

Security enhancement for NOMA-PON with 2D cellular automata and Turing pattern cascading scramble aided fixed-point extended logistic chaotic encryption

Assaults against pandemic heroes: an observational study in the Metropolitan City of Genoa in Italy

Abstract Background Escalating violence against healthcare workers is a significant public health issue that undermines care quality and workers’ health. This descriptive study aims to examine trends in aggression across different pandemic phases. Methods The study analysed reported violence among healthcare workers from 2018 to 2023 within a Local Health Authority in Genoa, Italy. Variables of interest included the severity of aggression, the professionals affected, and the locations of incidents. Data was analysed using appropriate statistical methods, including multinomial logistic regression, to assess relationships across pandemic phases. Results Reported aggressions rose from 48 incidents in 2018 to 103 in 2023, totaling 463. Pre-pandemic physical violence was at 39%, dropping to 27% during and after the pandemic (χ²=7.4, df = 2, p < 0.025). Nurses experienced the highest violence rates, with an average of 72% pre-pandemic and 74% post-pandemic, reducing to 51% during the pandemic if compared with doctors (Coef -0.80; 95%CI -1.45|-0.15). Over half of incidents occurred in Emergency and Psychiatric Services, from 77% before to 51% after pandemic. Conversely, violence in Local Services rose from 7% before to 24% during and 28% after pandemic (χ²=56.9, df = 6, p < 0.000). If compared to Hospitals, violence in Local Services increased during (Coef 1.44; 95%CI 0.40|2.48) and post-pandemic (Coef 1.001; 95%CI 0.13|1.87), and in Psychiatric Services decreased post-pandemic (Coef -0.98; 95%CI -1.67|-0.29). Conclusions Violence against healthcare workers shifted during and post-pandemic, mirroring the decentralisation of healthcare due to pandemic. The urgent development and implementation of effective violence prevention strategies are imperative. These should include training, psychological support, security enhancements, and a strong organisational culture that prioritises safety and respect. Protecting healthcare workers is crucial for their well-being and to ensure high healthcare quality. Key messages • Escalating violence undermines care quality and healthcare worker well-being. Effective prevention strategies are essential, including training, support, and an organisation that prioritises safety. • Nurses are the main victims of violence. Violence is higher in Emergency Departments, but incidents are increasing in Local Health Services, due to organisational changes during and post-pandemic.

Decentralized Machine Learning Framework for the Internet of Things: Enhancing Security, Privacy, and Efficiency in Cloud-Integrated Environments

The Internet of things (IoT) presents unique challenges for the deployment of machine learning (ML) models, particularly due to constraints on computational resources, the necessity for decentralized processing, and concerns regarding security and privacy in interconnected environments such as the Internet of cloud. In this paper, a novel decentralized ML framework is proposed for IoT environments characterized by wireless communication, dynamic data streams, and integration with cloud services. The framework integrates incremental learning algorithms with a robust decentralized model exchange protocol, ensuring that data privacy is preserved, while enabling IoT devices to participate in collaborative learning from distributed data across cloud networks. By incorporating a gossip-based communication protocol, the framework ensures energy-efficient, scalable, and secure model exchange, fostering effective knowledge sharing among devices, while addressing the potential security threats inherent in cloud-based IoT ecosystems. The framework’s performance was evaluated through simulations, demonstrating its ability to handle the complexities of real-time data processing in resource-constrained IoT environments, while also mitigating security and privacy risks within the Internet of cloud.

Demystification of Readiness, Security, and Technological Enhancements in the Adoption of a Cashless Economy

The adoption of a cashless economy was accelerated globally by the devastating impact of the COVID-19 pandemic. Brunei Darussalam was not excluded from this trend, as pandemic-related restrictions were implemented to ensure the safety of its population. In light of the COVID-19 crisis, this research paper examines the factors influencing the readiness and acceptance of a cashless economy among working society in Brunei Darussalam. The integrated concepts of the Technology Acceptance Model (TAM) and Technology Readiness Index (TRI) are applied to examine perceptions impacting their acceptance and readiness to continue adopting a cashless economy. The methodology includes a literature review and the use of secondary data from government reports and industry publications. A quantitative approach is employed, utilizing an online survey to collect non-probability samples from 212 respondents. The main instruments used in the survey are structured questionnaires. The study’s findings show that factors such as the assessment of payment modes, technological development, digital literacy, knowledge, regulatory policies, and security concerns significantly affect working society’s perceptions, readiness, and acceptance of a cashless economy. These results provide insights for policymakers and stakeholders on the key factors influencing continued cashlessness adoption and shaping societal behavior towards cashless payments.

Visual Lock - Enhancing Security with Visual Password

The prevailing method of user authentication commonly involves the submission of a text-based password along with a username. However, this approach has inherent drawbacks that have been widely acknowledged. A significant challenge is the difficulty users face in remembering passwords, leading to the adoption of short and simplistic ones, which in turn renders them susceptible to easy guessing or hacking. To address these concerns, this article introduces a novel financial graphical password authentication system. The operational details are elucidated through real-world examples, emphasizing key features of the system. In response to the limitations of conventional text-based password authentication, a groundbreaking financial graphical password method is proposed in this article. The prevailing issue of users resorting to short and easily guessable passwords due to memory constraints is addressed through this innovative approach. The article not only explains the functioning of the new system using real-world instances but also underscores its crucial features that enhance security in user authentication

Natural Language Processing for Sentiment Analysis in Social Media Posts to Identify Suspicious Behaviour

This article reports a study on the use of the natural language processing approach in the sentiment analysis of social media posts for detecting suspicious activities involving deep learning methods. The primary objective is to design an automated system capable of identifying unfavourable sentiments and patterns that imply suspicious activity such as cyberbullying, hate speech and disinformation. The methodology involves collecting a diverse data set of 2,000 posts from platforms such as X and Facebook, pre-processed by tokenisation and normalisation. The sentiment analysis model uses a long short-term memory (LSTM) network because it finds that the LSTM network is good for recognising long-term dependencies between texts. Other heuristics are used to give prominence to posts with malicious behavioural profiles to flag them accordingly. The model is designed and tuned to pre-labelled data sets. This work shows considerable improvement over previous techniques at categorising the posts as suspicious by showing higher accuracy, precision, recall and F1 score ratios. In doing so, this research positively impacts the enhancement of paranoid security or the totality of social media safety by offering a strong early detection mechanism for reducing risky behaviours on social networks.

Machine learning applications of network security enhancement: review

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

Using 3D Hand Pose Data in Recognizing Human–Object Interaction and User Identification for Extended Reality Systems

Object detection and action/gesture recognition have become imperative in security and surveillance fields, finding extensive applications in everyday life. Advancement in such technologies will help in furthering cybersecurity and extended reality systems through the accurate identification of users and their interactions, which plays a pivotal role in the security management of an entity and providing an immersive experience. Essentially, it enables the identification of human–object interaction to track actions and behaviors along with user identification. Yet, it is performed by traditional camera-based methods with high difficulties and challenges since occlusion, different camera viewpoints, and background noise lead to significant appearance variation. Deep learning techniques also demand large and labeled datasets and a large amount of computational power. In this paper, a novel approach to the recognition of human–object interactions and the identification of interacting users is proposed, based on three-dimensional hand pose data from an egocentric camera view. A multistage approach that integrates object detection with interaction recognition and user identification using the data from hand joints and vertices is proposed. Our approach uses a statistical attribute-based model for feature extraction and representation. The proposed technique is tested on the HOI4D dataset using the XGBoost classifier, achieving an average F1-score of 81% for human–object interaction and an average F1-score of 80% for user identification, hence proving to be effective. This technique is mostly targeted for extended reality systems, as proper interaction recognition and users identification are the keys to keeping systems secure and personalized. Its relevance extends into cybersecurity, augmented reality, virtual reality, and human–robot interactions, offering a potent solution for security enhancement along with enhancing interactivity in such systems.

Challenges and Advances in Analyzing TLS 1.3-Encrypted Traffic: A Comprehensive Survey

The widespread adoption of encrypted communication protocols has significantly enhanced network security and user privacy, simultaneously elevating the importance of encrypted traffic analysis across various domains, including network anomaly detection. The Transport Layer Security (TLS) 1.3 protocol, introduced in 2018, has gained rapid popularity due to its enhanced security features and improved performance. However, TLS 1.3’s security enhancements, such as encrypting more of the handshake process, present unprecedented challenges for encrypted traffic analysis, rendering traditional methods designed for TLS 1.2 and earlier versions ineffective and necessitating the development of novel analytical techniques. This comprehensive survey provides a thorough review of the latest advancements in TLS 1.3 traffic analysis. First, we examine the impact of TLS 1.3’s new features, including Encrypted ClientHello (ECH), 0-RTT session resumption, and Perfect Forward Secrecy (PFS), on existing traffic analysis techniques. We then present a systematic overview of state-of-the-art methods for analyzing TLS 1.3 traffic, encompassing middlebox-based interception, searchable encryption, and machine learning-based approaches. For each method, we provide a critical analysis of its advantages, limitations, and applicable scenarios. Furthermore, we compile and review key datasets utilized in machine learning-based TLS 1.3 traffic analysis research. Finally, we discuss the main challenges and potential future research directions for TLS 1.3 traffic analysis. Given that TLS 1.3 is still in the early stages of widespread deployment, research in this field remains nascent. This survey aims to provide researchers and practitioners with a comprehensive reference, facilitating the development of more effective TLS 1.3 traffic analysis techniques that balance network security requirements with user privacy protection.

Enhancing Security in Geographic Information Systems: Anonymization and Differential Privacy Techniques for Protecting Sensitive Geospatial Data

As Geographic Information Systems (GIS) increasingly facilitate the analysis and sharing of geospatial data, the protection of sensitive information becomes paramount. This research explores the implementation of anonymization and differential privacy techniques to enhance security in GIS. Anonymization methods effectively remove or obscure personally identifiable information from geospatial datasets, while differential privacy introduces a mathematical framework that allows for the sharing of aggregate data without compromising individual privacy. This study evaluates the strengths and weaknesses of these techniques, demonstrating their effectiveness in maintaining the utility of geospatial data while safeguarding sensitive information. Through case studies and comparative analysis, we provide insights into best practices for integrating these privacy-preserving strategies into GIS applications, ensuring compliance with legal regulations and fostering public trust in geospatial technologies.

Examining the Mediating Role of Attachment Styles Between Perceived Partner Responsiveness and Marital Satisfaction: Evidence from Sikkimese Couples

The dyadic study investigated how attachment (avoidance and anxious styles) mediates the relationship between perceived partner responsiveness and marital satisfaction with a sample of 152 couples from Northeast India. Data were collected through door-to-door surveys, and analysis was conducted using the Actor and Partner Interdependence Mediation Model (APIMeM). Results indicate that husbands’ avoidance attachment played a mediating role between the couples’ perceived partner responsiveness and husbands’ marital satisfaction, while anxious attachment showed no mediating relationship. These findings suggest that interventions aimed at reducing avoidant attachment behaviors could enhance marital satisfaction, particularly in cultural contexts similar to Northeast India. These insights, discussed within the framework of the Attachment Security Enhancement Model (ASEM), highlight the importance of understanding cultural nuances in relationship dynamics. The study emphasizes the importance of considering diverse perspectives beyond traditional West vs East frameworks when examining relationship dynamics.

Finding and Mitigating a Vulnerability of the Color Wheel PIN Protocol

There is an increasing usage in the banking sector of Smartphones enabled with Near Field Communication (NFC), to improve the services offered for the customers. This usage requires a security enhancement of the systems that employ this technology like the Automated Teller Machines (ATMs). One example is the Color Wheel Personal Identification Number (CWPIN) security protocol designed to authenticate users on ATMs using NFC enabled smartphones without typing the PIN code directly. CWPIN has been compared in the literature to several other protocols and was considered easier to use, more cost-effective and more resistant to various attacks on ATMs such as card reader skimming, keylogger injection, shoulder surfing, etc. Nevertheless, we demonstrate in this paper that CWPIN is vulnerable to the multiple video recordings intersection attack. We do so through concrete examples and a thorough analysis that reveals a high theoretical probability of attack success. A malicious party can use one or two hidden cameras to record the ATM and smartphone screens during several authentication sessions, then disclose the user's PIN code by intersecting the information extracted from the video recordings. In a more complex scenario, these video recordings could be obtained by malware injected into the ATM and the user's smartphone to record their screens during CWPIN authentication sessions. Our intersection attack requires a few recordings, usually three or four, to reveal the PIN code and can lead to unauthorized transactions if the user's smartphone is stolen. We also propose a mitigation of the identified attack through several modifications to the CWPIN protocol and discuss its strengths and limitations.

Large Language Models Meet Next-Generation Networking Technologies: A Review

The evolution of network technologies has significantly transformed global communication, information sharing, and connectivity. Traditional networks, relying on static configurations and manual interventions, face substantial challenges such as complex management, inefficiency, and susceptibility to human error. The rise of artificial intelligence (AI) has begun to address these issues by automating tasks like network configuration, traffic optimization, and security enhancements. Despite their potential, integrating AI models in network engineering encounters practical obstacles including complex configurations, heterogeneous infrastructure, unstructured data, and dynamic environments. Generative AI, particularly large language models (LLMs), represents a promising advancement in AI, with capabilities extending to natural language processing tasks like translation, summarization, and sentiment analysis. This paper aims to provide a comprehensive review exploring the transformative role of LLMs in modern network engineering. In particular, it addresses gaps in the existing literature by focusing on LLM applications in network design and planning, implementation, analytics, and management. It also discusses current research efforts, challenges, and future opportunities, aiming to provide a comprehensive guide for networking professionals and researchers. The main goal is to facilitate the adoption and advancement of AI and LLMs in networking, promoting more efficient, resilient, and intelligent network systems.

Enhancing Security and Traceability in Aerospace Supply Chains through Block Chain Technology

This study explores the impact of blockchain technology on aerospace supply chains, focusing on mitigating risks such as supply chain disruptions, counterfeit parts, and regulatory compliance challenges. A mixed-methods approach was employed, combining quantitative and qualitative analysis using data sourced from publicly available databases, including the Federal Aviation Administration (FAA), the Aerospace Industries Association (AIA), the International Air Transport Association (IATA), and reports from blockchain technology providers. Descriptive analysis identified supply chain disruptions (M = 3.74, SD = 1.17) as the most significant risk, followed by counterfeit parts (M = 3.34, SD = 1.20). Structural Equation Modeling (SEM) demonstrated that part authentication and provenance tracking had the strongest impact on improving traceability (β = 0.40, p < 0.001) and transparency (β = 0.38, p < 0.001). A Cost-Benefit Analysis (CBA) revealed a positive return on investment, with cost-benefit ratios up to 2.31. Blockchain adoption was also found to reduce CO₂ emissions by up to 25%, while improving operational efficiency through shorter procurement lead times and lower administrative costs. However, regulatory misalignment and interoperability challenges remain barriers to full adoption. The study concludes that blockchain is a transformative tool for enhancing traceability, security, and sustainability in aerospace supply chains.

Machine Learning Advancements in Cyber-Physical Systems: Enhancing Security, Performance, and Privacy

Machine Learning Advancements in Cyber-Physical Systems: Enhancing Security, Performance, and Privacy

Enhancing Security in Social Networks through Machine Learning: Detecting and Mitigating Sybil Attacks with SybilSocNet

This study contributes to the Sybil node-detecting algorithm in online social networks (OSNs). As major communication platforms, online social networks are significantly guarded from malicious activity. A thorough literature review identified various detection and prevention Sybil attack algorithms. An additional exploration of distinct reputation systems and their practical applications led to this study’s discovery of machine learning algorithms, i.e., the KNN, support vector machine, and random forest algorithms, as part of our SybilSocNet. This study details the data-cleansing process for the employed dataset for optimizing the computational demands required to train machine learning algorithms, achieved through dataset partitioning. Such a process led to an explanation and analysis of our conducted experiments and comparing their results. The experiments demonstrated the algorithm’s ability to detect Sybil nodes in OSNs (99.9% accuracy in SVM, 99.6% in random forest, and 97% in KNN algorithms), and we propose future research opportunities.

Enhancing Security in UAV-Assisted Image Data Collection for Internet of Things

Enhancing Security in UAV-Assisted Image Data Collection for Internet of Things

Enhancing Security and Risk Management for UNESCO Archives: A Case Study of the National Archives in Wroclaw and Crisis Management Strategies for Uniformed Services

Enhancing Security and Risk Management for UNESCO Archives: A Case Study of the National Archives in Wroclaw and Crisis Management Strategies for Uniformed Services