Security Measures Research Articles

IoT-Enabled Secure and Scalable Cloud Architecture for Multi-User Systems: A Hybrid Post-Quantum Cryptographic and Block Chain-Based Approach towards a Trustworthy Cloud Computing

Cloud computing remains one of the most significant advancements in resource provision to organizations in the latest decades since they initiate conveniences on demand access. The adoption of the IoT has brought about a new era of collaborative computing by employing a pool of linked smart sensors and devices that will produce and analyze great amounts of data, thus creating new related problems in terms of size and security, hence increasing even more the importance of traditional security measures. Therefore, in this paper, we synthesize a new SSCA that combines IoT and cryptographic mechanisms, to build highly available and secure cloud systems, which makes these systems multi-users and fully usable by many users at the same time. The design resolves to a distributed structure where sever al cloud nodes will address user s requests proficiently and it uses Multicast and Broadcast Rekeying Algorithm (MBRA) to maintain the privacy and the confidentiality of the users’ detail, employing a cryptosystem that integrates MBRA, Post Quantum Cryptography (PQC) and block chain. The architecture using IoT devices collects data from the distributed sensing resources through the data storage layer; it enforces the security integrity of the information collected by employing the MBRA-PQC encryption algorithms; the blockchain provides the security for the confidential data by storing it in unalterable and dispersed registers. The proposed approach is then demonstrated with different data sets and its performance is measured based on the response time, the throughput and scalability, security and reliability. The outcome of the expressions reveals the efficiency of the proposed SSCA compared to MHE-IS-CPMT as it reduced by 1. 67 seconds and 0. 97 seconds at 250 and 1000 devices respectively. Similarly, the result as AUC value for SSCA showed a better improvement than the MHE-IS-CPMT, EAM, SCSS, and SHCEF models with improvements in percent at 25-user level: 6. 30%, 6. 90%, 7. 60%, and 7. 30%, respectively as well as; at 50-user level: 5. 20%, 9. 30%, 11. 50%.

Enhancing Cashless Transactions: The Role and Benefits of Fingerprint Technology

In the modern times cashless transactions have become increasingly prevalent in today's digital economy and strong security measures are important to safeguard all the sensitive data related to finances. Fingerprint recognition emerges as a safe solution to upgrade the security and convenience of existing cashless systems. This research paper explores the utilization and benefits of fingerprint recognition./ The paper begins by understanding the basics of this technology, its advancement and uses. It then dives into the integration of cashless transactions with fingerprint recognition systems highlighting the potential benefits. Moreover, this paper also analyzes real- word scenarios and implementation across various industries and geographies. Furthermore, the technical considerations and challenges associated with implementation are also discussed which includes scalability, accuracy, accessibility of the technology. The strategies to make this technology more efficient and compliant are also discussed. In conclusion, this research accentuates the potential to revolutionize the landscape of cashless transactions using fingerprint recognition technology. By addressing and solving the challenges the full potential of biometric technology to upgrade digital payments can be unlocked.

Innovative approaches in complex data forensics: error rate assessment and its impact on cybersecurity

In the realm of cybersecurity, the integrity of forensic investigations is paramount, especially as the volume and complexity of data continue to escalate. This paper explores innovative approaches to complex data forensics, focusing on the methodologies used to assess error rates in data retrieval and analysis. High error rates in forensic processes can compromise the reliability of findings, leading to erroneous conclusions that may impact security measures and legal proceedings. This research examines various techniques for error rate assessment, including statistical methods and data validation protocols, which serve to quantify the accuracy of forensic analysis. Furthermore, the paper discusses the profound implications that high error rates can have on the integrity of forensic findings, emphasizing the need for meticulous attention to detail in data handling and processing. To counter these challenges, we present strategies aimed at enhancing data reliability, such as implementing rigorous quality assurance processes, leveraging machine learning algorithms for anomaly detection, and utilizing advanced encryption methods to protect data integrity throughout the forensic lifecycle. By addressing the critical role of error rate assessment in data forensics, this research contributes to the broader discourse on cybersecurity and underscores the necessity of adopting robust methodologies to ensure accurate and reliable forensic outcomes in an increasingly complex digital landscape.

Physical Patrol and Border Security Management at the Namanga One Stop Boarder Post, Kenya

Border administration encompasses all legal and administrative measures a sovereign state implements to secure, protect, and strengthen the sovereignty, security, safety, and integrity of its territory. Consequently, it is essential to any national migration strategy. The border administration policies emphasize the necessity for all participants in border management to be engaged to enhance the effectiveness of border operations. Security measures are normally enhanced along the border posts to improve on the national security of a given country by preventing entrance of terrorists, illegal goods and human trafficking. These security measures ensure safety at the border. The primary objective was to evaluate the effects of physical patrol on border security management at the Namanga one stop boarder post, Kenya. A descriptive survey research design was utilized. The population comprised of the employees at the Namanga OSBP, including police officers, customs officials, immigration officers, and National Intelligence Service employees, among others. The research used stratified random sampling. The data was collected via the use of questionnaires. Data analysis was done with the help of SPSS and then presented using tables, percentages, graphs and pie charts. This study found out that border security is complex and it requires various interventions, which entails a combination of technology and human security personnel for effective operations. In regard to objective one, the researcher finds that physical patrol emerges as a fundamental border security management measure, and is considered essential in enhancing security at the border point by minimizing insecurity incidents and eliminating potential of border security breaches. Based on the findings, this study concluded that various security measures, including physical patrols, integrated scanning, and electronic monitoring, are essential in the management of border security and in enhancing services. A major recommendation of the study is to promote greater multi-agency and multi-stakeholder collaboration as a means of detaining individuals attempting to enter a country illegally and effectively seizing any contraband goods. This necessitates more efficient border patrols and surveillance strategies.

Healthcare with Wearable Devices and Machine Learning

As health management continues to grow in importance globally, driven by the quest for improved health outcomes, longevity, and personalized care, machine learning (ML) has become a popular technique for detecting and analyzing health indicators, playing an essential role in multiple areas of health detection, such as continuous heart and blood pressure monitoring, blood sugar tracking for diabetics, sleep analysis to improve sleep quality, and even disease prediction. With wearable devices, a particular scale of health detection data has been accumulated at this stage. With this data, machine learning can be predictive analytics to assess real-time health conditions, enabling early intervention and personalized treatment plans. However, data quality remains the most critical issue for machine learning-based health detection systems, as inaccuracies in sensor readings can lead to misdiagnoses or incorrect treatment decisions. In addition, privacy concerns are significant, as the sensitive health information collected by these devices requires strong security measures to protect users’ data. Moreover, the interpretability of machine learning algorithms, especially those involving deep learning, can be limited, hindering clinicians’ understanding and trust. This paper addresses these challenges by providing a comprehensive overview of relevant data sets used in health monitoring and examining the latest machine learning algorithms tailored for wearables-based health detection. It further delves into the current limitations and challenges facing this emerging field, providing insights into potential solutions and future directions for research and development.

Maritime Security under the Lens: Evaluating its effectiveness in Deterring Piracy and Armed Robbery at Sea

Piracy is a significant threat in maritime zones, severely disrupting navigation and exerting a detrimental impact on global trade and commerce. This research critical examines the effectiveness of current maritime security measures, arrangements, and regulatory frameworks specifically designed to combat piracy and armed robbery at sea by exploring a range of strategies employed by both individual nations and international bodies, to counter the persistent maritime threat of armed robbery. Current research identifies key gaps and limitations within these existing measures of maritime securities, offering valuable insights into their practical efficacy and highlighting areas where improvements are necessary. The study evaluates the capacity of the existing frameworks by utilizing qualitative research methodology to theoretically address and prescribe effective mitigation strategies to deter piracy and armed robbery in today’s complex maritime environments, thereby contributing to the ongoing discourse on strengthening and enhancing global maritime security.

Developing a trustworthy national health data space: public needs and policy implications

Abstract Background The ongoing development of a national health data space in Switzerland presents an opportunity to transform health delivery and care by enabling large-scale secondary health research. Central to this development is the Health2030 strategy, which calls for the establishment of public trust in the reliability and security of data collection, storage, and exchange. Methods We conducted four focus groups across the German, French and Italian-speaking regions of Switzerland to inform the development of the Swiss health data space that fosters public trust. To inform Swiss policy, we held a panel discussion with patient experts and healthcare professionals to translate the focus group findings into governance and public communication requirements. We synthesized our findings using a thematic analysis approach and proposed a guiding health policy strategy. Results Citizens perceive the health data space as trustworthy conditional on the delivery of its promised higher levels of transparency, security, and lower risk compared to other data sharing initiatives. To ensure its trustworthiness, citizens call for the provision of value-based consent to control data access based on the actors involved and research purposes, alongside implementing accountability measures for data misuse. Governance framework requirements entail establishing an advisory group for the health data space with sufficient representation of the Swiss population, including members of the public, patient advisory groups, and researchers. Public communication requirements call for ongoing monitoring of public opinion on health data space, alongside establishing public engagement initiatives, such as public fora, to inform ongoing progress. Conclusions Policy efforts should prioritize the meaningful involvement of relevant stakeholders throughout all stages of the health data space development to foster trust. Central to these efforts is balancing public expectations with their practical implementation. Key messages • For the health data space to be trustworthy, citizens require transparency on their data use and the security measures taken, value-based consent for data access, and accountability measures. • Diverse representation in governance and continuous public involvement are key policy measures to build public trust in the development of national health data spaces.

6.I. Round table: Strengthening preparedness for public health emergencies: lessons learned and future perspectives

Abstract In the face of intensifying global health threats, robust health security measures in Europe and worldwide have never been more pressing. The COVID-19 pandemic has underscored the critical need for coordinated action, adaptive strategies, and robust collaborative mechanisms to effectively respond to emerging infectious diseases and other public health emergencies. This workshop serves as a platform to examine the multifaceted landscape of health security actors, focusing on collaborative initiatives and lessons learned from recent crises. By convening experts and stakeholders from across Europe, the workshop aims to achieve several key objectives: i) To provide an overview of collaborative initiatives established to strengthen European and international health security. By showcasing successful models of cooperation, participants will gain insights into effective strategies for fostering collaboration among diverse stakeholders. ii) Emphasize the role of European and international institutions, such as the European Centre for Disease Prevention and Control (ECDC) and the World Health Organization (WHO), in supporting states both in coordinating regional response efforts and in developing the necessary health workforce competencies and capabilities to respond to health emergencies. iii) Drawing on experiences from recent health emergencies, including outbreaks of high-risk pathogens, such as Ebola, the workshop will facilitate a critical examination of lessons learned. Through interactive discussions, participants will identify challenges, best practices, and opportunities for enhancing preparedness and response capacity. iv) By interactive presentations and panel discussions, the workshop will provide a platform for knowledge exchange and networking among participants. By sharing experiences, expertise, and innovative approaches, participants will contribute to a collective understanding of the challenges and opportunities in strengthening health security. This workshop seeks to illuminate key strategies, capacity-building efforts, and real-world lessons learnt that contribute to enhancing response capabilities to public health emergencies. Participants will gain a deeper understanding of emergency participation mechanisms, essential skills required, and available training resources to enhance preparedness. Key messages • Collaboration and adaptive strategies are essential for strengthening international health security in the face of emerging infectious diseases and other public health emergencies. • Lessons learned from recent crises provide valuable insights into effective response strategies and opportunities for enhancing preparedness and response capacity.

Consumer Adoption of Mobile Payments in Nigeria: the Interplay of Trust, Security, and Privacy in Nigeria

Security threats persist globally, impacting various sectors, including the mobile payment industry. In Nigeria, safeguarding sensitive consumer financial data is crucial in consumer usage. Adequate security measures are essential to protect these assets, as trust, security, and privacy are pivotal in driving consumer adoption of mobile payment systems. This study aimed to advance a model that examines how the interplay between trust, security, and privacy collectively fosters consumer confidence regarding the widespread use of mobile payments. Based on the Mobile Payment Adoption Model, the hypothetical framework incorporates critical components to enhance consumer adoption of mobile payments in Nigeria by focusing on trust, security, and privacy. This model emphasizes the continuous authentication and validation of users and devices to address security concerns. By integrating features that bolster user trust and ensure robust data protection, the framework aims to enhance mobile payment systems' perceived security and privacy. It supports seamless transactions while addressing privacy issues, thereby fostering greater consumer confidence and the adoption of mobile payment solutions.

Technological Innovation for Sustainable Supply Chain Management in the Food Industry

This study explores the critical role of technological innovations—specifically blockchain, Internet of Things (IoT), and artificial intelligence (AI)—in enhancing sustainable supply chain management within Malaysia's food industry. As the industry faces increasing pressure to adopt sustainable practices due to environmental concerns, regulatory demands, and consumer expectations, these technologies offer significant potential for improving traceability, reducing waste, and optimizing operational efficiency. The research highlights how blockchain enhances transparency and ethical sourcing, IoT enables real-time monitoring to minimize waste, and AI optimizes demand forecasting and inventory management, reducing overproduction and resource waste. However, the study also identifies substantial barriers to the adoption of these technologies, particularly for small and medium-sized enterprises (SMEs). These challenges include high implementation costs, a shortage of skilled labor, and concerns over data security and privacy. To address these issues, the study recommends starting with pilot projects to mitigate risks, investing in continuous workforce training, enhancing data security measures, and leveraging government support and industry collaborations. By overcoming these challenges, Malaysia's food industry can effectively integrate these innovations, leading to more sustainable, efficient, and resilient supply chains. This research underscores the importance of strategic planning and continuous improvement in fully realizing the benefits of technological advancements, thereby positioning Malaysia’s food industry as a leader in sustainable practices within the global market.

Evaluation of Quality Online Banking Services Using WASPAS Method

The rapid expansion of the Internet and advances in digital technology have revolutionized traditional banking methods, leading to the emergence of online banking. Online banking enables customers to conduct financial transactions anywhere, anytime, with unprecedented convenience and accessibility. This paper provides a comprehensive analysis of online banking, examining its benefits, challenges and impact on both banks and consumers. Key aspects such as security concerns, customer satisfaction, cost effectiveness and complexity in implementing online banking systems are discussed. Also, how online banking is reshaping the banking landscape, promoting greater competition, financial inclusion and digital transformation in the global economy. The advent of the Internet and digital technologies has fundamentally changed the way financial institutions operate, paving the way for online banking. Online banking, also referred to as internet or e-banking, allows customers to access a wide range of financial services through digital platforms. These services include checking account balances, transferring funds, paying bills and managing investments all from the convenience of a personal device. As customers increasingly demand fast, convenient and secure banking services, financial institutions have responded by embracing digital transformation. The shift to online banking has brought significant benefits such as reduced operational costs, improved customer experience and increased efficiency in financial services. However, along with these advantages, banks also face challenges related to cyber security, customer trust and the need for continuous technological upgrades to meet evolving consumer needs. The importance of research related to online banking lies in its profound impact on the global financial system and its growing relevance in the digital age. Understanding online banking is important for financial institutions, regulators and customers as it changes traditional banking practices and introduces new opportunities and challenges. Technological Advances and Innovations: Online banking is at the forefront of financial technology (FinTech), creating innovations in service delivery. Exploring this area allows for a deeper understanding of how technological advances such as mobile banking, artificial intelligence (AI) and block chain are improving efficiency, customer experience and the competitive landscape of banking institutions. Customer Behavior and Satisfaction: As more consumers shift to digital platforms, research on online banking provides insights into changing customer expectations and behavior. It highlights key factors influencing customer satisfaction, including usability, convenience and trust, helping banks refine their services to meet growing demands for seamless, personalized experiences. Cost Efficiency and Operational Impact: The financial sector has experienced significant cost reductions and operational improvements through the adoption of online banking. Understanding how banks can use online platforms to optimize resource allocation, reduce transaction costs and improve efficiency is critical for stakeholders looking to stay competitive and increase profitability. Security and risk management: As online banking systems become more pervasive, so do the associated risks—especially those related to cyber security. Research in this domain is essential for developing effective security protocols, understanding fraud prevention strategies, and managing data privacy concerns. It ensures continuous improvement of security measures to protect both financial institutions and consumers. Customer Satisfaction (Benefit), Transaction Speed (Benefit, sec), Implementation Cost (Non-Benefit, $000), Security Rating (Non-Benefit). System A, System B, System C, System D, System E.

How CCTV shapes perceptions of safety in London’s public transport: a quantitative analysis

ABSTRACT The perception of safety while using public transportation varies among individuals, influenced by personal experiences and individual characteristics. Closed-circuit television (CCTV) systems are commonly employed to deter crime in public areas, including transit environments where safety concerns are prevalent. This study aims to scientifically assess the effectiveness of CCTV in enhancing the perceived safety of public transportation users. A comprehensive survey was conducted, involving over 200 students from various universities to ensure a representative sample. The survey targeted public transportation users and focused on their sense of security in relation to the presence of CCTV. Through quantitative analysis, three specific research questions were addressed. The results indicate that the presence of CCTV alone is insufficient to significantly improve feelings of safety among public transportation users. These findings suggest important policy implications: enhancing perceived safety in public transportation requires more than just the implementation of security measures. It is essential to inform and educate the public about actual crime rates, existing security measures, and the importance of crime reporting. This comprehensive approach can more effectively address the multifaceted nature of safety perception in public transportation.

Leveraging Advanced Machine Learning Algorithms for Enhanced Cyberattack Detection on U.S. Business Networks

Cyberattacks' rising volume and sophistication have made conventional security measures, such as firewalls, signature-based intrusion detection systems, and antivirus software, increasingly inadequate. The upsurge of cyber threats has been one of the most pressing predicaments for U.S. organizations in the digital age. With the increased dependence on internet-based forums, cloud computing, and interconnected networks, companies face an advancing number of extreme cyberattacks. The chief objective of this research project is to design and deploy proven machine learning methods to enhance the detection and combating of cyberattacks on U.S. organization networks. This research project retrieved a cyber-attack dataset from Kaggle.com, which had a collection of public datasets of cyber threats. This dataset was curated precisely, offering a realistic representation of cyber-attack scenarios, making it an ideal playground for various analytical tasks. The collection was classified as per the source of the relevant information, such as host-based datasets, network traffic datasets, malware or fraud reports, or a special section for datasets that can be classified according to a specific source. The dataset comprised numerous network traffic attributes such as source and destination IP addresses, ports, protocol, payload size, and attack labels. For this research project, three machine learning algorithms were used, namely: Logistic Regression, XG-Boost and Random Forest. This research project applied performance metrics such as accuracy, precision, recall, and F1 score for the performance of the classification models were considered. The result illustrated that the random forest model was far superior in accuracy compared to the logistic regression model; particularly, it had excellent accuracy. Through the use of advanced machine learning models, organizations will be in a position to devise more dynamic and intelligent security systems that evolve with the threat landscape. These intelligent systems monitor every kind of anomaly, malicious activity, and threat response with unparalleled effectiveness. The findings of this research project have significant implications for enhancing cybersecurity in U.S. organizational networks.

To Study on Cyber Crime Burgeoning in India and Crucial Actions Need for Every Cybercrime Victim

India is facing a great deal of challenges due to the rapid digitalization and broad adoption of technology in many different areas. One such concern is cybercrime. In this paper explores the nature, scope, and impact of cybercrime in the Indian context. The rising ubiquity of internet usage has rendered people, companies, and governmental organizations more susceptible to a range of cyber threats, such as identity theft, financial fraud, data breaches, and cyber harassment. Despite developments in cyber security protection, the lack of awareness and poor regulatory frameworks has aggravated the situation. To address these issues, the Indian government has put in place a number of efforts, including the National Cyber Security Policy and the Information Technology Act, 2000. But enforcement is still patchy, and a lot of crimes go unreported or ignored. The problem has been made worse by insufficient regulatory frameworks and a lack of knowledge, even in spite of advances in cyber security measures. But enforcement is still patchy, and a lot of crimes go unreported or ignored. This research paper emphasizes India's cybercrime victims have several legal options, the need for a multimodal strategy that includes improved cyber security infrastructure, better legislative frameworks and greater public awareness in order to tackle cybercrime. In light of the transnational character of cyber threats, it also highlights the significance of international cooperation. In the end, a thorough plan is necessary to protect India's digital environment and guarantee the security and safety of its people in a world that is becoming more interconnected by the day

Workplace violence against healthcare workers in Türkiye: Experiences, opinions, and suggestions

Workplace violence against healthcare workers is a complex issue with significant implications for the safety and wellbeing of professionals in the healthcare sector. Drawing on data from 136 participants across Türkiye, this study explores the prevalence, forms, impacts, and management strategies of workplace violence in healthcare settings. The demographic characteristics of the participants reveal a diverse composition of healthcare workers affected by workplace violence, highlighting the universal nature of the issue. Verbal abuse emerges as a predominant form of violence, ranging from insults and threats to humiliation, while instances of physical violence pose grave risks to healthcare professionals. The study also underscores the inadequacy of support systems within healthcare institutions and the legal system, exacerbating the distress experienced by healthcare workers. Strategies for managing workplace violence include internal coping mechanisms, peer support, and institutional intervention, underscoring the need for comprehensive training programs and enhanced institutional protocols. Recommendations for prevention encompass a multifaceted approach involving enhanced security measures, support systems, and policy reforms at various levels. The study concludes with a call to action for concerted efforts to address workplace violence against healthcare workers, emphasizing the importance of targeted interventions to ensure the safety and well-being of professionals in their workplace. These findings provide valuable insights into the complexities of workplace violence in healthcare settings and highlight the urgent need for action to address this pressing issue.

Enhancing Bank Credit Card Transaction Fraud Detection with Machine Learning Techniques

Abstract: Because of the increasing prevalence and sophistication of credit card theft, standard detection measures frequently fail. This study investigates the use of several machine learning algorithms to improve fraud detection in bank credit card transactions. The research aims to develop enhanced fraud detection systems by employing an integrated strategy involving data preprocessing, the application of various classification algorithms, and performance evaluation. The study thoroughly examines algorithms such as Random Forest, Logistic Regression, and Neural Networks, focusing on their predictive capabilities and practical applications. The findings indicate that machine learning provides a robust framework for increasing the accuracy and efficiency of fraud detection systems, consequently assisting financial institutions in protecting customer transactions and strengthening security measures. Moreover, by prioritizing robust model selection and feature engineering, banks can significantly enhance their fraud detection performance. This research could greatly influence how financial institutions handle fraud, potentially reducing losses, improving operational efficiency, and securing transaction environments. The insights gained from this study are also valuable for informing broader financial security strategies and guiding future research in the field.

Construction and Application of a Private 5G Standalone Medical Network in a Smart Health Environment: Exploratory Practice From China.

To date, the differentiated requirements for network performance in various health care service scenarios-within, outside, and between hospitals-remain a key challenge that restricts the development and implementation of digital medical services. This study aims to construct and implement a private 5G (the 5th generation mobile communication technology) standalone (SA) medical network in a smart health environment to meet the diverse needs of various medical services. Based on an analysis of network differentiation requirements in medical applications, the system architecture and functional positioning of the proposed private 5G SA medical network are designed and implemented. The system architecture includes the development of exclusive and preferential channels for medical use, as well as an ordinary user channel. A 3-layer network function architecture is designed, encompassing resource, control, and intelligent operation layers to facilitate management arrangements and provide network open services. Core technologies, including edge cloud collaboration; service awareness; and slicing of access, bearer, and core networks, are employed in the construction and application of the 5G SA network. The construction of the private 5G SA medical network primarily involves system architecture, standards, and security measures. The system, featuring exclusive, preferential, and common channels, supports a variety of medical applications. Relevant standards are adhered to in order to ensure the interaction and sharing of medical service information. Security is achieved through mechanisms such as authentication, abnormal behavior analysis, and dynamic access control. Three typical medical applications that rely on the 5G network in intrahospital, interhospital, and out-of-hospital scenarios-namely, mobile ward rounds, remote first aid, and remote ultrasound-were conducted. Testing of the 5G-enabled mobile ward rounds showed an average download rate of 790 Mbps and an average upload rate of 91 Mbps. Compared with 4G, the 5G network more effectively meets the diverse requirements of various business applications in prehospital emergency scenarios. For remote ultrasound, the average downlink rate of the 5G network is 4.82 Mbps, and the average uplink rate is 2 Mbps, with an average fluctuation of approximately 8 ms. The bandwidth, performance, and delay of the 5G SA network were also examined and confirmed to be effective. The proposed 5G SA medical network demonstrates strong performance in typical medical applications. Its construction and application could lead to the development of new medical service models and provide valuable references for the further advancement and implementation of 5G networks in other industries, both in China and globally.

Simulation-based evaluation of advanced threat detection and response in financial industry networks using zero trust and blockchain technology

The financial sector is increasingly facing advanced cyber threats, necessitating a shift from traditional security measures to more dynamic frameworks. This study presents a novel integration of Zero Trust architecture with hybrid access control system and blockchain technology to enhance security in financial institutions. Zero Trust enforces continuous authentication and dynamic access controls, while blockchain secures digital identities and transaction logs through its immutable ledger, ensuring data integrity and non-repudiation. The proposed framework, evaluated using OMNeT++ simulations enhanced by Ethereum-Ganache, shows improved detection accuracy, reduced false positives, and increased resistance to insider threats and other attacks. It also strengthens compliance with regulatory requirements through robust audit trails, providing enhanced protection for sensitive financial data.

Artificial Intelligence in Cybersecurity : Advancing Threat Modeling and Vulnerability Assessment

This article examines the transformative role of Artificial Intelligence (AI) in revolutionizing cybersecurity practices, with a particular focus on threat modeling and vulnerability assessment. As cyber threats grow increasingly sophisticated, traditional security measures often fall short in providing comprehensive protection. We explore how AI and machine learning algorithms enhance the accuracy and efficiency of threat modeling by analyzing vast datasets to identify patterns and anomalies indicative of potential security risks. The article also investigates AI's contribution to vulnerability assessment, highlighting its capacity for continuous monitoring and real-time analysis of diverse data sources, including network traffic, system logs, and threat intelligence feeds. By simulating various attack scenarios and prioritizing security vulnerabilities, AI-driven tools enable organizations to adopt a more proactive and dynamic approach to cybersecurity. While acknowledging the challenges and ethical considerations associated with AI implementation in this domain, this article underscores the significant potential of AI in fortifying cyber defenses and safeguarding digital assets in an ever-evolving threat landscape.

Enhancing IoT Security through an Artificial Neural Network Approach

This study aims to fortify Internet of Things (IoT) security through the strategic implementation of Artificial Neural Networks (ANNs). With the rapid expansion of IoT devices, traditional security measures have struggled to cope with the dynamic and complex nature of these environments. ANNs, known for their adaptability, are explored as a promising solution to enhance security. The central objective is to significantly improve the accuracy of IoT security measures by optimizing ANN architectures. Using a curated dataset with key environmental parameters, the study evaluates three ANN models—Backpropagation Neural Network (BPNN), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM). The evaluation metrics include accuracy, precision, recall, and F1-score across different train-test splits. Results show that LSTM consistently outperforms BPNN and MLP, demonstrating superior accuracy and the ability to capture temporal dependencies within IoT security data. Implications stress the importance of aligning model selection with specific application goals, considering factors like computational efficiency. In conclusion, this research contributes valuable insights into the practical implementation of ANNs for IoT security, guiding future optimization efforts and addressing real-world deployment challenges to safeguard sensitive data and ensure system resilience in the evolving IoT landscape.