Security Risks Research Articles

Evaluating the benefits and risks of facial recognition payment systems adoption in Malaysia

PurposeCurrent developments in the FinTech payment industry have shown a rapid revolution in Industry 4.0, and understanding the factors affecting individual acceptance of facial recognition payment (FRP) is crucial. Hence, this study aims to evaluate the benefits and risks of FRP system adoption in Malaysia.Design/methodology/approachThe perceived risks and benefits framework is adopted as the foundation in this study to examine the various risks and benefits that users perceive, along with the trust factor, to study the relationships between these variables. Data were collected via an online questionnaire, and the hypotheses were tested using Partial Least Squares analysis on 277 responses.FindingsThe results revealed that perceived risk is a significant predictor of users' intention to use the FRP system. Privacy risk and financial risk significantly influence perceived risks, while security risk does not. Although convenience, perceived ease of use and perceived trust positively influence perceived benefits, perceived benefits do not significantly influence adoption intention. Moreover, perceived trust negatively affects perceived risks while positively affecting both perceived benefits and adoption intention. Additionally, personal innovativeness moderates the relationship between perceived risks and the intention to use the FRP system.Practical implicationsThis study helps policymakers and service providers understand individuals’ concerns and expectations regarding FRP systems. It aids practitioners in developing strategies to build trust, address innovativeness differences and mitigate risks, serving as a roadmap for integrating these systems into Malaysia's financial landscape.Originality/valueThis study distinguishes itself from prior research by evaluating FRP system adoption in Malaysia through the lens of perceived risks and benefits framework. It also explores personal innovativeness as a moderator, examining its impact on the relationship between usage intention and perceived risks and benefits. Additionally, it highlights perceived trust as a crucial factor influencing individuals' intention to adopt FRPs.

The security implications of using feminist methodologies to study gender-based violence in Yemen

IntroductionThis paper examines the political and security implications of gender-based violence (GBV) research in Yemen during the period (2019–2023). As various radical groups are gaining power over Yemeni land, radical views toward women and gender equity and equality shape the experiences of GBV survivors, practitioners, and researchers in the North of Yemen. Policing Houthi ideologies in Yemen have curtailed GBV research and subjected feminist research to myriad risks. If this situation continues, experiences of women and girls in Yemen will not be captured by research. Their stories and needs will not be captured by humanitarian and peace-building actors.MethodologyFindings of this study are based on primary data from key informant interviews with 25 GBV researchers actively engaged in Yemen. Sampling followed the snowballing technique.ResultsThe findings examine political and security power dynamics after the Houthi radical group took control in the north of Yemen, and implications on GBV research design. Anti-feminist ideologies coupled with extreme security measures have impacted quality of GBV study methodologies as well as researchers' safety and mobility. Donors of previously established GBV programs and research were harassed to change research topics or lose permission to speak to local communities or collect data from aid beneficiaries. Researchers who do not follow new rules of engagement with the community are detained, harassed and their devices and databases are confiscated. Terms like “gender” and “GBV” are not deemed acceptable as these are western concepts that do not align with new community values. The findings highlight the need to use conflict-sensitivity and Do No Harm principles in settings where GBV work is scrutinized. It also challenges the orthodox definition of “GBV evidence” and explores the ethical implications of the use of alternative means to collecting data. Findings also provide insight into valuable alternative methodologies that allow local and national researchers to continue studying GBV in conflict impacted areas without exposing themselves to actual or perceived risk. The paper proposes concrete approaches that can mitigate political and security risk on both researchers and GBV survivors.

Security Risk Assessment of Teleoperated Vessels and Associated Centers: Parameter Identification

Security Risk Assessment of Teleoperated Vessels and Associated Centers: Parameter Identification

Intrusion detection in cluster‐based wireless sensor networks: Current issues, opportunities and future research directions

AbstractWireless sensor network (WSN) cluster‐based architecture is a system designed to control and monitor specific events or phenomena remotely, and one of the important concerns that need quick attention is security risks such as an intrusion in WSN traffic. At the same time, a high‐level security method may refer to an intrusion detection system|intrusion detection systems (IDS), which may be employed effectively to achieve a higher level of security in detecting an intruder attack or any attack initiated within a WSN system. The significance of the detection of network intrusions on heterogeneous cluster‐based sensor networks with wireless connections, as well as the approaches to machine learning utilised in IDS model development, were discussed. In addition, this research conducted several comparative studies of feature selection techniques and machine learning methodologies in the development of intrusion detection systems. The authors used a bibliometric indicator to identify the leading trends when it comes to IDS, and the VOS viewer was used to create a spatial mapping of co‐authorship, co‐occurrence, and citation types of analysis with their respective units of study. The purpose of this research paper is to generate relevant findings and a research problem formulation that can lead to a research gap in the research topic's domain area.

A High-Concurrency Tasks Emergency Offloading Framework for Industrial Edge Networks

In the envisioned scenario of smart factories, large-scale distributed intelligent industrial devices generate a substantial number of concurrent heterogeneous industrial tasks. A single device is inadequate to meet the demanding requirements of complex tasks, which necessitate high computational capabilities and low latency in the system. Typically, multi-access edge computing (MEC) is employed to address this challenge. However, industrial edge networks face threats such as network attacks, natural disasters, and hardware failures, posing security risks to the industrial edge network. Therefore, we have designed an emergency offloading framework to enhance its resilience. Based on a task path analysis method with multiple priority levels, we developed an emergency task scheduling pre-classification (ETSPC) algorithm. Experimental results demonstrate that the proposed algorithm significantly reduces the delay of emergency offloading for high-concurrency tasks, with a maximum reduction of up to 19.2%. In extreme scenarios, the task completion rate of the ETSPC algorithm increases by 45.3% compared to the control algorithm.

API Security Challenges and Risk Mitigation in Fintech Applications

API Security Challenges and Risk Mitigation in Fintech Applications

Real-Time Data Governance and Compliance in Cloud-Native Robotics Systems

This study investigates the frameworks and challenges of real-time data governance and compliance in cloud-native robotics systems, focusing on data integrity, cloud security, regulatory adherence, and cybersecurity risks. Using extensive datasets from the Amazon AWS Open Data Registry, the EU GDPR Enforcement Tracker, and Kaggle’s IoT dataset, the analysis explores cloud-native systems' data accuracy, security, and governance. Data were extracted through a standardized process: performance metrics, including latency and error rates, were gathered from Amazon AWS to assess system efficiency, GDPR violation records were analyzed from the EU Enforcement Tracker to understand compliance trends, and data volume and governance metrics from Kaggle’s IoT dataset were correlated to identify governance challenges. Together, these data sources provide comprehensive insights into how cloud-native systems can be optimized for real-time operations. The study highlights the cloud security benefits and governance advantages inherent to cloud-native frameworks, such as real-time monitoring, automated threat detection, and data encryption, which collectively reduce unauthorized access risks while supporting operational efficiency. Findings indicate high data accuracy (0.51% error rate) and low latency (mean of 48.96 ms) across systems; however, processing time variability (standard deviation of 28.61 ms) signals a need for further optimization in time-sensitive environments. The regression analysis of GDPR violations reveals a substantial penalty increase of €53,789.41 per violation, emphasizing the financial risks of non-compliance. Correlation analysis (r = 0.083 for data volume and governance failures) suggests that external cybersecurity threats have a greater impact on governance than internal metrics, underscoring the importance of adaptive governance frameworks that support both data integrity and regulatory compliance in cloud-native robotics systems.

The Impact of Consumer Perceptions of Privacy and Security Risks on Re-purchasing Intention for Online Shopping in Chiang Mai Province, Thailand

Background and Aim: Chiang Mai Province, a growing economic center in northern Thailand, is a preferred mode of consumerism due to its convenience and wide product selection, but faces barriers to adoption due to concerns about these risks. This research investigates the impact of consumer perceptions of privacy and security risks on re-purchasing intention for online shopping in Chiang Mai Province, Thailand. The study aims to understand how these perceptions influence consumer behavior in the digital marketplace. Materials and Methods: A quantitative cross-sectional survey design was employed, with data collected from 385 online shoppers in Chiang Mai Province. The research instrument consisted of a structured online questionnaire assessing perceived privacy and security risks, as well as re-purchasing intentions. Multiple regression analysis was used to test the hypotheses. Results: The findings reveal that both perceived privacy and security risks significantly influence re-purchasing intention. Consumers who perceive higher levels of privacy and security are more likely to engage in repeat online purchases. The model explained a substantial proportion of the variance in re-purchasing intention, highlighting the importance of these factors in shaping online shopping behavior. Conclusion: This research provides valuable insights for e-commerce businesses and policymakers in Chiang Mai Province. By prioritizing and investing in measures that enhance consumer perceptions of privacy and security, businesses can foster trust, encourage repeat purchases, and drive sustainable growth in the online marketplace.

FEATURES OF THE DETERMINING THE STATE POLICY OF UKRAINE IN THE AREA OF DE-OCCUPATION

This article aims to explore the peculiarities of defining the concept of "state policy" in Ukraine in the context of de-occupation. The relevance of this research is grounded in the ongoing territorial challenges that Ukraine faces due to military aggression and the occupation of its territories. The need for a coherent and strategically sound approach to regaining control over these regions makes the issue highly pertinent. The study adopts an analytical research methodology, focusing on the theoretical foundations of state policy in the sphere of de-occupation. The research also utilizes comparative analysis, examining the different models of de-occupation that have been applied historically and internationally, including military, political-diplomatic, institutional, and economic models. The findings indicate that the concept of “state policy” in de-occupation processes is multi-faceted and requires a comprehensive approach, combining various strategies. Key components of the theoretical basis for state policy in de-occupation are identified, including the political and legal foundation, socio-economic integration, national reconciliation and cultural integration, security measures, and international support. Each of these elements plays a crucial role in the successful recovery of occupied territories and the re-establishment of state sovereignty. The study highlights the importance of synchronizing these components to ensure long-term stability and development in post-conflict regions. The practical value of the article lies in its potential to serve as a guideline for policymakers and government officials in Ukraine, offering a structured understanding of how these key components can be effectively integrated into the country's de-occupation strategy. By focusing on both theoretical and practical dimensions, the article provides a framework that can contribute to the formulation of effective policies aimed at the reintegration of occupied territories, addressing challenges such as legal ambiguity, socio-economic disparities, and security risks. This research contributes to the ongoing discourse on de-occupation by offering insights into how Ukraine can enhance its efforts in restoring territorial integrity in the face of current geopolitical challenges.

A Framework for Real-Time AI-Driven Secure Code Analysis Integrated with DevSecOps in Cloud-Native CI/CD Pipelines

Abstract—This paper presents a novel framework for inte- grating real-time AI-driven secure code analysis into DevSecOps practices within cloud-native CI/CD pipelines. As organizations increasingly adopt cloud- native architectures and agile develop- ment methodologies, the need for robust, automated security mea- sures becomes paramount. Our proposed framework leverages advanced machine learning algorithms to perform continuous, real-time code analysis, identifying potential vulnerabilities and security risks throughout the development lifecycle. By seamlessly integrating with existing CI/CD tools and cloud platforms, our solution enables organizations to enforce security policies, detect threats, and remediate issues without compromising development velocity. We evaluate the effectiveness of our framework through a series of case studies across diverse software projects, demon- strating significant improvements in threat detection accuracy, reduced false positives, and overall security posture. Our results indicate that the proposed AI-driven approach can enhance code security by up to 40% compared to traditional static analysis tools, while maintaining the agility of modern development prac- tices. This research contributes to the evolving field of DevSecOps by offering a scalable, intelligent solution for embedding security into the heart of cloud-native software development processes. Index Terms—DevSecOps, AI-driven security, cloud- native, CI/CD pipelines, secure code analysis

Online Voting System Using Blockchain: A Secure and Transparent Approach

Blockchain technology has emerged as a viable solution for secure and transparent digital voting systems. Traditional voting systems, particularly online voting mechanisms, are often vulnerable to security risks such as hacking, tampering, and vote manipulation. Furthermore, maintaining voter anonymity and ensuring data integrity are significant challenges. Blockchain, with its distributed and immutable ledger, offers potential solutions by providing a secure and decentralized approach to recording votes. This paper explores the architecture, implementation, and advantages of a blockchain-based online voting system that leverages the security features of blockchain technology to improve the voting process. By utilizing cryptographic principles and decentralized data storage, the system enhances transparency, reduces fraud risks, and ensures that votes remain immutable. The proposed system includes mechanisms for voter registration, vote casting, and result verification, all of which are secured by smart contracts. Experimental results demonstrate that blockchain can maintain voter anonymity, resist tampering, and provide an auditable voting trail. Additionally, the study discusses challenges in scalability, latency, and regulatory compliance, offering insights into potential improvements and future work. Overall, the research indicates that blockchain based voting has the potential to transform electoral processes, providing a trusted digital voting alternative that aligns with democratic principles. Key Words: Blockchain Technology, Digital Voting, Online Voting Systems, Data Integrity, Transparency, Security, Cryptography, Smart Contracts, Voter Anonymity, Tamper-Resistance, Immutable Ledger, Scalability, Decentralization.

Advice from a Doctor or AI? Understanding Willingness to Disclose Information Through Remote Patient Monitoring to Receive Health Advice

Remote Patient Monitoring (RPM) devices transmit patients' medical indicators (e.g., blood pressure) from the patient's home testing equipment to their healthcare providers, in order to monitor chronic conditions such as hypertension. AI systems have the potential to enhance access to timely medical advice based on the data that RPM devices produce. In this paper, we report on three studies investigating how the severity of users' medical condition (normal vs. high blood pressure), security risk (low vs. modest vs. high risk), and medical advice source (human doctor vs. AI) influence user perceptions of advisor trustworthiness and willingness to disclose RPM-acquired information. We found that trust mediated the relationship between the advice source and users' willingness to disclose health information: users trust doctors more than AI and are more willing to disclose their RPM-acquired health information to a more trusted advice source. However, we unexpectedly discovered that conditional on trust, users disclose RPM-acquired information more readily to AI than to doctors. We observed that the advice source did not influence perceptions of security and privacy risks. We conclude by discussing how our findings can support the design of RPM applications.

Media image transmission and privacy protection for internet of things security

Internet of Things (IoT) is becoming more popular with the increase in advancements of technology and is widely used in various sectors. The data are collected from the real environment and then transmitted over the networks. Due to their restricted resources, processing capacity, and memory, IoT gadgets are susceptible to certain security risks. Several encryption methodologies were established to provide safe communication between IoT devices with minimal computing cost and bandwidth consumption, due to the rise in media date. This research proposes a revolutionary compact pixel crypto (CPC) technique that can accommodate the modest transmission speeds of IoT gadgets. The suggested techniques reduces the computational burden and data quantity by encrypting the image data using pixel driven selective encryption and bloke scanning compression. The suggested method’s effectiveness is examined using the network simulator (NS-2) platform. According to the findings of the experiments, the suggested method outperforms the previous approaches in terms of both packet rate and energy usage. The proposed approach shortens the time needed for node side encryption and decryption operations with improving the system’s energy effectiveness and connectivity performance.

Secure and Lightweight Cluster-Based User Authentication Protocol for IoMT Deployment

Authentication is considered one of the most critical technologies for the next generation of the Internet of Medical Things (IoMT) due to its ability to significantly improve the security of sensors. However, higher frequency cyber-attacks and more intrusion methods significantly increase the security risks of IoMT sensor devices, resulting in more and more patients’ privacy being threatened. Different from traditional IoT devices, sensors are generally considered to be based on low-cost hardware designs with limited storage resources; thus, authentication techniques for IoMT scenarios might not be applicable anymore. In this paper, we propose an efficient three-factor cluster-based user authentication protocol (3ECAP). Specifically, we establish the security association between the user and the sensor cluster through fine-grained access control based on Merkle, which perfectly achieves the segmentation of permission. We then demonstrate that 3ECAP can address the privilege escalation attack caused by permission segmentation. Moreover, we further analyze the security performance and communication cost using formal and non-formal security analysis, Proverif, and NS3. Simulation results demonstrated the robustness of 3ECAP against various cyber-attacks and its applicability in an IoMT environment with limited storage resources.

CSTAN: A Deepfake Detection Network with CST Attention for Superior Generalization

With the advancement of deepfake forgery technology, highly realistic fake faces have posed serious security risks to sensor-based facial recognition systems. Recent deepfake detection models mainly use binary classification models based on deep learning. Despite achieving high detection accuracy on intra-datasets, these models lack generalization ability when applied to cross-datasets. We propose a deepfake detection model named Channel-Spatial-Triplet Attention Network (CSTAN), which focuses on the difference between real and fake features, thereby enhancing the generality of the detection model. To enhance the feature-learning ability of the model for image forgery regions, we have designed the Channel-Spatial-Triplet (CST) attention mechanism, which extracts subtle local information by capturing feature channels and the spatial correlation of three different scales. Additionally, we propose a novel feature extraction method, OD-ResNet-34, by embedding ODConv into the feature extraction network to enhance its dynamic adaptability to data features. Trained on the FF++ dataset and tested on the Celeb-DF-v1 and Celeb-DF-v2 datasets, the experimental results show that our model has stronger generalization ability in cross-datasets than similar models.

Vietnam’s hedging amid U.S.-China Mekong rivalry: risk management under uncertainties

This paper examines how Vietnam has navigated U.S.-China rivalry in the Mekong basin while seeking to ensure its security, prosperity, and autonomy. I use the theoretical lens of hedging, defined as an insurance-maximising behaviour under conditions of high uncertainty. While existing scholarship has extensively examined Vietnam’s hedging on maritime security issues and the broader U.S.-China strategic and economic competition, this paper makes an empirical contribution by focusing on the relatively understudied angle of hedging in the Mekong basin context. I argue that Vietnam has hedged by (1) diversifying its Mekong partnerships with both the United States and China and (2) selectively cooperating with China under the Lancang-Mekong Cooperation. These acts aim to minimise Vietnam’s exposure to the long-term consequences of several non-traditional security risks, namely ecological challenges, water insecurity, economic vulnerabilities, and energy insecurity. Vietnam’s Mekong hedging also insures against the traditional security risks of big-power entrapment and military aggression as U.S.-China competition intensifies.

Natural Hazards and Climate Change Impacts on Food Security and Rural–Urban Livelihoods in Mozambique—A Bibliometric Analysis and Framework

Mozambique is confronted with numerous risks related to food security and natural disasters. The study conducted a literature review on natural hazards and food security. This can help to identify gaps and further areas of research. A bibliometric analysis was conducted using standardized text search terms, and the VOSviewer tool was used to analyze over 7000 scientific articles and cluster over 60,000 keyword co-occurrences. The results show that research on natural hazards for food security needs to be integrated. The priority topic of disasters focuses on specific hazards such as climate change, floods, and hurricanes, which are also linked to demographic and other social variables. More studies on food security, such as droughts, sustainable development, and other human and social conditions, are being conducted. Resilience as an emerging research paradigm needs to be addressed in comparison. One result is an analytical framework on impacts on food security in the context of disaster risk, based on the empirical findings of the literature review. It shows how everyday risks such as disease or food security can be conceptually better linked to natural hazards and resilience. It shows that further research is needed on the interlinkages of multiple risks, of which Mozambique is an outstanding example. The methodology presented is also applied to provide a framework for linking multiple risks to food security and natural hazards. The innovative dimension of the research is that this inquiry constitutes one of the pioneering attempts to conduct a bibliometric analysis of the linkages between natural hazards, food security, and resilience in Mozambique. Another noteworthy contribution is introducing a novel analytical framework that integrates food security and disaster risks.

Information Technology Governance Using the COBIT 2019 Framework at PT Bank Pembangunan Daerah Papua

Effective and efficient information technology (IT) management is crucial for the banking industry, especially for PT Bank Pembangunan Daerah Papua (BPDP), which seeks to reinforce its role within the Papua region. Leveraging the COBIT 2019 framework, BPDP aims to enhance IT governance, addressing operational efficiency, information security, and regulatory compliance. This study highlights BPDP's strategic priorities, such as revenue growth, fostering innovation, and customer service excellence, which are essential for sustaining competitive advantage amidst regional geopolitical challenges. By identifying these key priorities, the research uncovers critical areas of improvement, including IT investment optimization, mitigation of regulatory and security risks, and solidifying IT's strategic role in driving business growth. To evaluate BPDP's IT governance maturity, this study applies COBIT 2019's ten design factors, from enterprise goals and risk profiles to IT-related issues, compliance needs, and technology adoption strategies. The findings underscore gaps in risk management, project lifecycle oversight, and resource allocation, offering actionable insights for governance improvement. Enhanced IT integration can support BPDP's larger objectives of sustainable growth and economic development within the region. Furthermore, the study demonstrates that a well-structured governance framework strengthens BPDP's operational efficiency, builds customer trust, and ensures alignment with regulatory standards and evolving technology landscapes. By adopting these governance recommendations, BPDP can achieve long-term success and secure its position as a prominent financial institution in Papua, thus reinforcing its commitment to regional economic growth and compliance with national regulatory standards.

Managing forage for grain: Strategies and mechanisms for enhancing forage production to ensure feed grain security 1

The “Greater Food” approach has replaced the older “taking grain production as a top priority” approach. The importance of feed and forage as the material basis for guaranteeing high-quality development of the livestock industry has gradually become prominent. However, owing to the tradition of “both human staple food and animal feed relying on grain production” in China and the decoupling of feed crop planting and livestock farming, the risk of feed grain security has increased, especially as it relates to the supply of high-quality protein feed ingredients from abroad, which is facing a bottleneck. To ensure food security, effective domestic agricultural production should be adopted. Nevertheless, guaranteeing the supply of high-quality protein feed through domestic soybean production is difficult because of limited arable land; furthermore, pressure on the staple food supply is still extreme. In this article, the historical and realistic implications for the security risks of feed grain in China are analyzed. Proposals are made to separate staple food grains for humans from the feed grain supply for animals and to develop high-quality forage to reduce feed grain use. High-quality forage can be supplied via intercropping with grain crops in arable land and reseeding perennial legumes or grasses into natural grasslands. However, “managing forage for grain” needs to be supported via technical paths and policies as the forage industry develops to effectively increase the capacity to ensure feed grain security.

Addressing Supply Chain Security Risks through Security Transparency

Addressing Supply Chain Security Risks through Security Transparency