Security Concern Research Articles

Unveiling Users' Security and Privacy Concerns Regarding Smart Home IoT Products from Online Reviews

The Internet of Things (IoT) has revolutionized the global market with lifestyle products such as fitness trackers (FT), smart home speakers (SHS), and surveillance and security camera systems (SSCS). While offering convenience, these products also introduce potential security and privacy (S&P) risks to buyers, often going unnoticed. Consumers’ incomplete mental models of the risks involved and the information asymmetry between buyers and sellers only add to the problem. Understanding consumer concerns in online product reviews can play a crucial role in bridging the gap of such information asymmetry. By establishing a balanced flow of information between buyers and sellers, manufacturers can leverage genuine concerns expressed in reviews to enhance product features while educating users about misinformation in reviews. In this study, we collected FT, SHS, and SSCS product reviews from three Amazon domains: the US, the UK, and India. Using a keyword-based search method focused on S&P concerns, we discovered a considerable number of reviews expressing notable concerns regarding security and privacy. Our qualitative analysis revealed that data security is a common concern across all product types. Further, our quantitative analysis exposed significant geographic variations, with the concern ratio being higher in the US than in the UK for all device types and higher than in the Indian domain for security cameras. These findings highlight the need for tailored security measures and user awareness campaigns in different parts of the world to address the identified concerns effectively.

Secure IoT: Deep Learning-Based Intrusion Detection for Attack Detection and Prevent

Nowadays, the wide adoption of the modern Internet of Things (IoT) paradigm has brought about the tremendous development of smart cities. Smart cities operate in real-time world to promote ease and also the human life quality with regard to efficiency and comfort. A security concern along with privacy is considered as a foremost issue in several smart cities. there is basically a requirement for Intrusion Detection Systems for mitigating the IoT-related security outbreaks which took the entire benefits of security liabilities. In existing works, the accuracy in the procedure of detection and security are the main challenge. The non-attacked data is sent to user in a secured way using Improved RSA scheme. After which the user gets and decrypts them. The decrypted data is then forecasted for further examination. The experimental outcomes of proposed techniques employed in Feature Selection, classification, and also secure data transmission are related with the traditional methods.

Geopolitics of the Horn of Africa: Arab Countries’ Stance on the Grand Ethiopian Renaissance Dam, with Special Focus on the GCC Position

The Horn of Africa holds a strategic position, bordering on the Red Sea, the Gulf of Aden, the Indian Ocean, and the Nile region. The Bab al-Mandab Strait, which connects three continents – Asia, Africa, and Europe-lie within its ambit. Over the past decade, external powers have begun to take a heightened interest in this region. Consequently, security issues in this strategically vital area have become increasingly salient, and local conflicts, famines, the proliferation of terrorist groups and maritime piracy have all found their way onto wider regional and international agendas. The current conflict between Ethiopia, Sudan, and Egypt regarding the Grand Ethiopian Renaissance Dam (GERD) is of particular significance in this new scenario. This paper explores the position of the Gulf Cooperation Council (GCC) countries and the general Arab stance on the dispute. It is worth noting that despite the considerable efforts made by Egyptian diplomacy to establish a unified Arab position on the Renaissance Dam issue, there are significant differences among Arab countries in regard to the issue. At the level of the GCC countries, the United Arab Emirates adopts a relatively flexible and neutral position, sometimes positioning itself as a mediator between the conflicting parties. Conversely, Kuwait maintains a firm stance in support of the Egyptian-Sudanese position, considering water security for Egypt and Sudan as a justifiable national security concern for the two countries. Saudi Arabia appears to be neutral in the conflict and emphasises that the African Union should play the primary role in resolving the issue. Even the two downstream countries, Egypt and Sudan, have failed to adopt cohesive stances on the issue. At the Arab Summit held in Algeria on 2 November 2022, significant differences between Sudan and Egypt emerged in the working of the final document, even though they had been intent on welding wider Arab support behind their conflict with Ethiopia. This paper explores the nature of, and the reasons behind, the differences between GCC countries and the broader Arab community regarding the Renaissance Dam issue. Furthermore, it examines the potential for developing a unified yet flexible Arab Gulf position which could help to lay the basis for a balanced and fair agreement between Ethiopia, Sudan, and Egypt on the Renaissance Dam issue.

User Privacy Protection via Windows Registry Hooking and Runtime Encryption.

The Windows registry contains a plethora of information in a hierarchical database. It includes system-wide settings, user preferences, installed programs, and recently accessed files and maintains timestamps that can be used to construct a detailed timeline of user activities. However, these data are unencrypted and thus vulnerable to exploitation by malicious actors who gain access to this repository. To address this security and privacy concern, we propose a novel approach that efficiently encrypts and decrypts sensitive registry data in real time. Our developed proof-of-concept program intercepts interactions between the registry's application programming interfaces (APIs) and other Windows applications using an advanced hooking technique. This enables the proposed system to be transparent to users without requiring any changes to the operating system or installed software. Our approach also implements the data protection API (DPAPI) developed by Microsoft to securely manage each user's encryption key. Ultimately, our research provides an enhanced security and privacy framework for the Windows registry, effectively fortifying the registry against security and privacy threats while maintaining its accessibility to legitimate users and applications.

Elicitation of security threats and vulnerabilities in Insurance chatbots using STRIDE

Although chatbots are used a lot for customer relationship management (CRM), there needs to be more data security and privacy control strategies in chatbots, which has become a security concern for financial services institutions. Chatbots gain access to large amounts of vital company information and clients’ personal information, which makes them a target of security attacks. The loss of data stored in chatbots can cause major harm to companies and customers. In this study, STRIDE (viz. Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, Elevation of privilege) modelling was applied to identify the data security vulnerabilities and threats that pertain to chatbots used in the insurance industry. To do this, we conducted a case study of a South African insurance organisation. The adopted methodology involved data collection from stakeholders in the insurance organisation to identify chatbot use cases and understand chatbot operations. After that, we conducted a STRIDE-based analysis of the chatbot use cases to elicit security threats and vulnerabilities in the insurance chatbots in the organisation. The results reveal that security vulnerabilities associated with Spoofing, Denial of Service, and Elevation of privilege are more relevant to insurance chatbots. The most security threats stem from Tampering, Elevation of privilege, and Spoofing. The study extends the discussion on chatbot security. It fosters an understanding of security threats and vulnerabilities that pertain to insurance chatbots, which is beneficial for security researchers and practitioners working on the security of chatbots and the insurance industry.

Evaluating User Perceptions and Security Concerns in Unified Payments Interface (UPI) Services

Evaluating User Perceptions and Security Concerns in Unified Payments Interface (UPI) Services

Detection and mitigation of DDoS attacks in SDN based intrusion detection system

Software defined networks (SDN) have completely revolutionized the management and operation of networks. This novel technology entails a distinctive approach to management. Amidst the advancements, a notable security concern arises in the form of distributed denial of service (DDoS) attacks. To counteract this attack, the deployment of intrusion detection systems (IDS) assumes paramount importance. IDS plays a critical role in monitoring network traffic, promptly detecting irregularities that may signify a potential denial of service (DoS) assault. This study delves into a comprehensive exploration of a DDoS attack on an SDN network using the OpenDaylight controller and the Mininet emulator. Furthermore, the assessment extends to evaluating the DDoS attack's repercussions and the effectiveness of IDS in mitigating such risks. Various performance metrics, including throughput according to delay time, are monitored to gauge network performance under duress. The difference in throughput curves when comparing scenarios with and without IDS highlights the significant impact of intrusion detection. When the IDS was absent, there was a noticeable increase in oscillations, indicating greater network susceptibility. On the other hand, the presence of an IDS created a more regulated environment, reducing variances and promoting a more stable network.

Security Concerns of the Russian Federation and Türkiye's Position Within the Scope of Georgia Intervention to South Ossetia in 2008

This article provides a comprehensive analysis of the geopolitical landscape in the South Caucasus, focusing on Russia's strategic interests, NATO and EU expansion efforts, and Turkey's role as a regional actor. It begins by examining Russia's "Near Environment" doctrine, emphasizing Moscow's perceived security threats and its proactive measures to maintain influence in its immediate surroundings. The 2008 conflict between Russia and Georgia serves as a focal point, illustrating the complex interplay of historical, ethnic, and political factors in the region. The article explores the origins of the Georgian-Ossetian crisis, the escalation into the "Five-Day War," and the subsequent recognition of South Ossetia and Abkhazia by Russia. It also examines Turkey's stance on the conflict, balancing support for Georgia's territorial integrity with economic and strategic interests in the region that advocates respect for Georgia's territorial integrity and sovereignty in the context of international law, but at the same time does not ignore its security concerns of Russia and aims to maintain a policy of balance in the Black Sea Basin and the Caucasus in the context of regional peacekeeping. Furthermore, the analysis highlights the international community's response to the conflict and its implications for regional stability. The conclusion underscores the significance of the South Caucasus in global geopolitics, particularly in shaping the balance of power in the Black Sea region. Overall, this article offers valuable insights into the complex dynamics of conflict and cooperation in a strategically vital area of Eurasia.

The Australia-Tuvalu Falepili Union Treaty: Opportunities and Controversies

Abstract The Prime Ministers of Australia and Tuvalu signed an agreement creating the Australia-Tuvalu Faelepili Union on 9 November 2023. The agreement commits both States to a closer relationship and includes important provisions related to climate cooperation and security. As a small, low-elevation island State, climate change is recognised as Tuvalu’s primary national security concern. Through the agreement, Australia commits to Tuvalu’s continued sovereignty notwithstanding climate change-related impacts and to create a special human mobility pathway for Tuvaluan citizens to move to Australia. The agreement also provides that Tuvalu will mutually agree with Australia any arrangement with any other State or entity on security and defence-related matters. This is in keeping with Australia’s treaty arrangements with other neighbours in the Pacific region and has been seen as a counterweight to China’s growing presence in the region.

A quantitative analysis of the security of PoW-based blockchains

This study analyzes the security implications of Proof-of-Work blockchains with respect to the stale block rate and the lack of a block verification process. The stale block rate is a crucial security metric that quantifies the proportion of rejected blocks in the blockchain network. The absence of a block verification process represents another critical security concern, as it permits the potential for invalid transactions within the network. In this article, we propose and implement a quantitative and analytical model to capture the primary operations of Proof-of-Work blockchains utilizing the Performance Evaluation Process Algebra. The proposed model can assist blockchain designers, architects, and analysts in achieving the ideal security level for blockchain systems by determining the proper network and consensus settings. We conduct extensive experiments to determine the sensitivity of security to four aspects: the number of active miners and their mining hash rates, the duration between blocks, the latency in block propagation, and the time required for block verification, all of which have been shown to influence the outcomes. We contribute to the findings of the existing research by conducting the first analysis of how the number of miners affects the frequency of stale block results, as well as how the delay in block propagation influences the incentives received by rational miners who choose to avoid the block verification process.

ANFIS-based power management and islanding detection utilizing permeation rate(γ) and relaxation parameter(ζ) for optimal operation of multiple grid-connected microgrids

Microgrid(s) integration into distribution networks and the growing use of Distributed Generation Systems necessitate the switch from traditional fossil fuel-based power generation methods to renewable resource-based ones. To achieve optimal power delivery, an adaptive method for solving power quality delivery problems posed by this transition is required. Unexpected islanding is an important power security concern that can lead to equipment damage, electrical risks, and decrease in power quality delivery. To address this problem, ANFIS-Based power management and islanding detection utilizing permeation rate(γ) and relaxation parameter(ζ) is proposed in this paper based on a rate of change of output voltage(ROCOV) of power sources at the point of common coupling in a 34-bus distribution network. Modeling of the distribution network, distributed generators, ANFIS training, and simulations was done using MATLAB/Simulink software. Results show that a 0.3 and 1.01 value indicates permeation rate of Microgrid(s) in a grid-tied mode whereas a 0 value indicates relaxation parameter of Microgrid(s) disconnected from the main distribution network. Islanding detection time of 0.02 sec was recorded when all Microgrids were disconnected during disturbances, and a 0.04 sec islanding detection time for disconnecting individual power sources at a time was recorded respectively. In general, islanding causes a system nominal voltage(400 V) deviation of 7 %. However, this was quickly restored without causing network voltage fluctuations due to the adaptive nature of ANFIS. Comprehensive simulations were used to validate the suggested method. The outcomes demonstrated that the proposed approach effectively distinguishes between islanding and non-islanding events and can quickly and accurately identify islanding as compared to other related works.

Social life cycle assessment in current and future Norwegian livestock production

Abstract Purpose This study is a social life cycle assessment (S-LCA) of per capita consumption of animal protein in Norway in 2019. The animal protein consisted of milk, beef, pork, poultry and egg. Based on the 2019 results, two scenarios, TrendProd and ChangeProd, were qualitatively assessed. The TrendProd scenario is a continuation of the current trend towards highly efficient livestock production, whilst ChangeProd focuses on using livestock feed based on bioresources that are unsuitable for human consumption. Methods The assessment of current livestock production followed the updated version of the social LCA guidelines by UNEP (2020). A survey amongst stakeholders was conducted for the selection of subcategories. The product system was divided into the following steps: imported feed, domestic off-farm feed, livestock production, manufacturing, distribution and use. A total of 25 subcategories across six stakeholder groups (i.e. workers, local community, society, value chain actors, consumers and children) were included in the assessment. A reference scale approach was used with a scale from 1 to 4. The assessment included quantitative and qualitative data from statistics, indices, surveys, etc. The social performance of the scenarios was assessed by the authors’ expert evaluation using the Delphi approach, comparing performance of indicators relative to the current production. Results and discussion The life cycle for the current Norwegian livestock production had an acceptable or high social performance for 17 of the selected subcategories. Five subcategories had a low performance (fair salary, equal opportunities, animal welfare, food security, promoting social responsibility) and three had a very low performance (health and safety, cultural heritage, fair competition). The assessment of the scenarios indicated that anticipated improvements in skills and technology were key drivers for maintaining or enhancing social performance in certain subcategories. The use of activity variables for measuring the relative importance of each unit process is recommended as it reflects actual distribution and is consistent with the method of environmental LCA. Conclusions Overall, the social life cycle assessment of current livestock production demonstrated high social performance. A stakeholders’ survey resulted in a new subcategory, ‘food security’, which served to include societal concern for self-sufficiency and food security in social LCA. Limitations and uncertainties of the study have also been identified, some relating to data and choice of indicators and others to the application of method, e.g. activity variables, development of reference scale and aggregation of results.

Profiles of Antibiotic Resistance Genes in the Coastal Aquatic Environment of Xiamen City

The coastal areas and the adjacent islands are the hotspots of human economic and social activities, including urbanization, industrialization, and agricultural practices, which have profound impacts on the ecological environment of the coastal environment. Antibiotic resistance genes （ARGs）, as emerging contaminants, have become hot topics in water ecological security and public concern. However, the profiles of antibiotic resistome in the costal water remain largely unknown, impeding resistome risk assessment associated with coastal environments. In this study, the high-throughput quantitative PCR technique was used to investigate the abundance and distribution of ARGs in the coastal environment of Xiamen City. Combined with the 16S rDNA gene amplicon sequencing method, the structure and composition of the microbial community in a water environment were investigated, and the influencing factors and associated mechanism of ARGs in seawater were deeply explained. The results of this study showed that a total of 187 ARGs were detected in the coastal water environment, and the abundance level was up to 1.29×1010 copies·L-1. Multidrug resistance, aminoglycosides, and β lactamase resistance genes were the three main classes of antibiotic resistance genes in the water environment of the Xiamen coastal zone. On the whole, the profile of ARGs was of high abundance, great diversity, and common co-existence, and the coastal water environment was an important hot area and reservoir for antibiotic resistance genes. Twenty-two microbes, including Nautella, Candidatus, Tenacibaculum, Rubripirellula, and Woeseia, were potential carriers of the corresponding 16 antibiotic resistance genes. The mobile genetic elements （MGEs） and microbial community structure accounted for 93.9% of the variation in environmental resistance genes in water. Therefore, microbial community and its mobile genetic elements were the most important driving forces for the occurrence and evolution of ARGs in coastal waters. Based on the results, it is implied that the environmental antibiotic resistance genes in the waters near Xiamen Island have potential risks to water ecological security and human health and highlight the necessity for comprehensive surveillance of ARGs associated with microbial contamination in the coastal aquatic environment.

Public attitudes to China in the ‘Five Eyes': unpacking views across the Anglosphere security community

ABSTRACT China is an important security concern for the United States and its allies, including the Five Eyes intelligence-sharing group that is sometimes described as the core of the ‘Anglosphere’ security community. While we would expect securitising discourses at the elite level to reproduce some common perceptions of China, to what extent are attitudes to China shared across the publics in these countries? In this article, we unpack public attitudes towards China in the United States, the United Kingdom, Canada, Australia, and New Zealand. Drawing on the results of public opinion surveys we conducted in 2022, we note areas of similarity and divergence then drill down into the drivers of public attitudes. We show that even though aggregate attitudes towards China in the five countries appear to align with official security discourses, this hides significant variation in how different groups within these societies view China. In particular, ethnic minorities and recent immigrants, along with members of higher socio-economic classes, urban residents, and young people, tend to be more positive towards China. Our findings bring new insights into the potency of government-driven securitisation, particularly in terms of identifying groups within societies that are less inclined to follow their government’s view of China.

Data Privacy and Security Concerns in AI-Integrated Educational Platforms

As educational institutions increasingly embrace AI technologies to enhance learning experiences, a paramount concern arises regarding the privacy and security of sensitive student data. This research paper delves into the challenges and implications associated with the integration of AI in educational platforms, focusing specifically on data privacy and security issues. The study aims to identify potential risks, assess current safeguards, and propose strategies to mitigate threats, ensuring a responsible and secure implementation of AI in education. By examining the intersection of technological advancement and privacy concerns, this research contributes to the ongoing dialogue on ethical considerations in the realm of AI-driven education transformation. Furthermore, this research endeavors to propose robust frameworks and strategies to safeguard the integrity and confidentiality of data in AI driven educational environments. By addressing these concerns head-on, we strive to contribute valuable insights to the ongoing discourse on balancing the transformative benefits of AI in education with the imperative to protect the privacy and security of all stakeholders involved.

Optimizing IoT Intrusion Detection Using Balanced Class Distribution, Feature Selection, and Ensemble Machine Learning Techniques.

Internet of Things (IoT) devices are leading to advancements in innovation, efficiency, and sustainability across various industries. However, as the number of connected IoT devices increases, the risk of intrusion becomes a major concern in IoT security. To prevent intrusions, it is crucial to implement intrusion detection systems (IDSs) that can detect and prevent such attacks. IDSs are a critical component of cybersecurity infrastructure. They are designed to detect and respond to malicious activities within a network or system. Traditional IDS methods rely on predefined signatures or rules to identify known threats, but these techniques may struggle to detect novel or sophisticated attacks. The implementation of IDSs with machine learning (ML) and deep learning (DL) techniques has been proposed to improve IDSs' ability to detect attacks. This will enhance overall cybersecurity posture and resilience. However, ML and DL techniques face several issues that may impact the models' performance and effectiveness, such as overfitting and the effects of unimportant features on finding meaningful patterns. To ensure better performance and reliability of machine learning models in IDSs when dealing with new and unseen threats, the models need to be optimized. This can be done by addressing overfitting and implementing feature selection. In this paper, we propose a scheme to optimize IoT intrusion detection by using class balancing and feature selection for preprocessing. We evaluated the experiment on the UNSW-NB15 dataset and the NSL-KD dataset by implementing two different ensemble models: one using a support vector machine (SVM) with bagging and another using long short-term memory (LSTM) with stacking. The results of the performance and the confusion matrix show that the LSTM stacking with analysis of variance (ANOVA) feature selection model is a superior model for classifying network attacks. It has remarkable accuracies of 96.92% and 99.77% and overfitting values of 0.33% and 0.04% on the two datasets, respectively. The model's ROC is also shaped with a sharp bend, with AUC values of 0.9665 and 0.9971 for the UNSW-NB15 dataset and the NSL-KD dataset, respectively.

An Exploratory Study of Fintech Security Concerns among its Users in Nigeria

The rapid growth of financial technology (Fintech) in Nigeria has revolutionized the way people conduct financial transactions. However, this technological advancement brings along a host of security concerns that impact users' trust and confidence in using Fintech platforms. This exploratory study aims to investigate the security concerns among Fintech users in Nigeria, shedding light on the underlying factors contributing to these concerns. This research employs a mixed-methods approach, combining qualitative interviews and quantitative surveys to gather comprehensive data. The qualitative phase involves in-depth interviews with a diverse sample of Fintech users, exploring their perceptions, experiences, and concerns related to the security aspects of Fintech services. The quantitative phase consists of a survey administered to a larger sample, aiming to quantify the prevalence and intensity of security concerns among Fintech users in Nigeria. The results highlighted the importance of addressing data breaches, privacy concerns, and authentication methods to alleviate user apprehensions. Participants expressed a desire for more transparency regarding the security practices of Fintech providers through improved communication and education to enhance user awareness and understanding of the security measures in place. This corroborates the notion that users' trust in Fintech services is depends on their perception of the security measures implemented by providers. The study's contribute to the existing literature on Fintech security, providing an in-depth understanding of the specific security concerns faced by Nigerian users, and identify potential strategies to enhance the security measures and alleviate users' concerns, which will ultimately foster greater adoption and utilization of Fintech services. Keywords: Fintech, financial services, security concerns, privacy, data breaches

An intelligent optimized secure blockchain mechanism for cloud auditing

The cloud auditing model was attractive for the cloud storage system to check the integrity of the stored user files. Hence, to maximize the security concern, federated learning and the blockchain were introduced. But, in some cases, security issues arose because of the extensive unstructured data and attack vulnerability. So, to avoid security problems and offer better protection in a cloud environment, a novel Optimized Buffalo-based Homomorphic SHA Blockchain (OBHSB) is proposed. In this model for accessing the cloud storage data with the critical matching method, if any of the unauthenticated users are trying to access the file initially, the system checks the key matching parameter. The proposed model was developed to provide better security in big data presented in the cloud environment. Hence, federated learning was adopted to create the cloud auditing phase in the distributed cloud environment. Therefore, it gives flexibility in training all data types: audio, text, and images. Moreover, the prime performance parameters were calculated and compared with other traditional approaches to justify the efficiency of the proposed model. The attack was considered an event in this research. The performance rate of the proposed model was validated in the performance analysis. Subsequently, the case study was developed in this research to explain the working procedure of the proposed design, and the result of the proposed model was discussed.

An identity authentication and key agreement protocol for the internet of vehicles based on trusted cloud-edge-terminal architecture

The continuous progression in cloud computing, edge computing, and associated technologies has notably hastened the progress of vehicle networking technology. This advancement is increasingly assuming a crucial role in enhancing driving safety, optimizing traffic management, and revolutionizing traffic control methodologies. The principal aim of Internet of Vehicles (IoV) technology is to establish a secure, convenient, and efficient novel driving paradigm, enabling intelligent transportation through wireless communication connecting roadside units and vehicles. Nevertheless, this wireless communication method is susceptible to potential attacks, including remote control, information monitoring, and identity simulation. Given this situation, effective authentication is required to address this security concern. Thus, this study proposes an identity authentication and key negotiation protocol grounded in a trusted cloud-edge-terminal architecture. This protocol facilitates mutual authentication, generates secure session keys for communication, guarantees the security of vehicle communication, and supports functionalities including privacy protection and password alteration for vehicle users. Time tree technology is employed for managing the edge nodes, facilitating the sharing of vehicle certification information among these nodes, and enhancing certification efficiency. Formal security analysis and informal security analysis are conducted to demonstrate the security of the proposed protocol, evaluating its security and practicality. Theoretical comparisons and experimental results demonstrate the outstanding computational and communication performance of the proposed protocol.

NAVIGATING ECONOMIC UNCERTAINTY: CHALLENGES AND STRATEGIES FOR HUMAN RESOURCE MANAGEMENT IN THE IT SECTOR

This article explores the concept of economic uncertainty and its profound impact on human resource management, particularly within the IT sector. Economic uncertainty, arising from unpredictable changes in economic conditions, poses significant challenges to organizational stability, strategic planning, and employee well-being. The paper identifies typical sources of economic uncertainty, such as government policies, market fluctuations, and global events, and examines their effects on personnel management, including hiring practices, employee turnover, and cost-cutting measures. Furthermore, the psychological impact of economic instability on IT employees is analyzed, highlighting increased anxiety and concern for basic security. The study also addresses the heightened stress levels among employees and managers, detailing the physical, psychological, and behavioral consequences. Finally, the article suggests strategies to mitigate these effects, emphasizing the importance of transparent communication, learning and development programs, and promoting a strong sense of purpose and psychological safety within teams.