Gap In Security Research Articles

Bridging the Gap in AI Security: A Comprehensive Review and Future Directions for Chatbot Technologies

Bridging the Gap in AI Security: A Comprehensive Review and Future Directions for Chatbot Technologies

High Quality Development of Regional Inclusive Insurance in China: Based on Logistic Multiple Linear Regression Model

As one of the key projects of poverty alleviation, inclusive insurance has received much attention in recent years. In May 2022, the Banking and Insurance Regulatory Bureau issued a document entitled “Circular on further enriching the supply of Life Insurance products”, which is intended to encourage more commercial insurance companies to participate in the construction of an inclusive insurance system closely related to the protection of people's livelihood. First of all, from the background of the development of inclusive insurance, this paper finds that there is still a gap in basic medical security, and inclusive insurance will play an important role in the development of the “main artery”. The second part focuses on the profound connotation of inclusive insurance from the perspectives of the product characteristics, operation mode, development status of each region, protection scope and so on. The third part makes an in-depth analysis of the factors affecting the inclusive insurance participation rate based on the multiple linear regression model and finds that the opening of basic medical insurance to individual accounts is important factors affecting the inclusive insurance participation rate. Starting from the reality of inclusive insurance, the last part of this paper puts forward three suggestions for the future development of inclusive insurance based on the coverage, “product &service”, multi-channel of science and technology.

Addressing the gap in information security: an HR-centric and AI-driven framework for mitigating insider threats

PurposeDespite ongoing reports of insider-driven leakage of confidential data, both academic scholars and practitioners tend to focus on external threats and favour information technology (IT)-centric solutions to secure and strengthen their information security ecosystem. Unfortunately, they pay little attention to human resource management (HRM) solutions. This paper aims to address this gap and proposes an actionable human resource (HR)-centric and artificial intelligence (AI)-driven framework.Design/methodology/approachThe paper highlights the dangers posed by insider threats and presents key findings from a Leximancer-based analysis of a rapid literature review on the role, nature and contribution of HRM for information security, especially in addressing insider threats. The study also discusses the limitations of these solutions and proposes an HR-in-the-loop model, driven by AI and machine learning to mitigate these limitations.FindingsThe paper argues that AI promises to offer many HRM-centric opportunities to fortify the information security architecture if used strategically and intelligently. The HR-in-the-loop model can ensure that the human factors are considered when designing information security solutions. By combining AI and machine learning with human expertise, this model can provide an effective and comprehensive approach to addressing insider threats.Originality/valueThe paper fills the research gap on the critical role of HR in securing and strengthening information security. It makes further contribution in identifying the limitations of HRM solutions in info security and how AI and machine learning can be leveraged to address these limitations to some extent.

Securing Smart Farms: An Integrated IoT-Based Security System with Arduino and SIM Module

The rise of smart farming, driven by technologies like the Internet of Things (IoT), has opened up new possibilities in agriculture. However, it has also brought about significant security challenges. This study aims to address the need for a comprehensive security system designed specifically for smart farms. The authors developed this system using camera technology, Arduino microcontrollers, vibration sensors, and the SIM900A SIM module. It enhances intrusion detection, provides precise location tracking, and enables real-time incident reporting through Multimedia Messaging Service (MMS) alerts and Short Message Service (SMS). By capturing visual data and leveraging vibration sensors, it offers an effective means of identifying security threats. Importantly, the SIM900A module ensures swift communication, even in remote agricultural areas. This research helps fill the gap in smart farm security, offering a practical and scalable solution to protect assets and data in the evolving landscape of digital agriculture.

The income redistribution effect of social security in China

Based on the data collected by China Household Income Project (CHIP), this paper compares the redistribution effect of China's social security in 2013 and 2018, respectively. The results show that the overall redistribution effect of China's social security has been enhancing. First, social security transfer income has played a moderate role in narrowing income inequality, while social security contribution, especially the medical insurance contribution, has widened the income gap. Second, the redistribution effects of social security on rural residents and migrant workers have increased. Third, social security effectively offset the growing income gap due to market factors in eastern China but failed to realize it in central and western China. Fourth, the narrowing of the urban-rural-migrant gap and regional gap in social security has promoted the enhancement of the overall redistribution effect of social security. Fifth, compared with high-income countries, the redistribution effect of China's social security system operated on a much smaller scale. We conclude by putting forward some policy suggestions.

Gender Gap in Food Security and Nutrition: Special Case of India in the G20

The sustainability of food security and adequate nutrition is a global challenge. The G20 nations, representing two-thirds of the global population bear half of the world’s malnutrition burden in the form of either excessive or inadequate intake of nutrition. The general risks associated with food insecurity in terms of climate change, ecology and resource contraction vis-à-vis population are macro issues involving the global commons. This article which relates to the micro issue of the gender gap in household food security is based on public data and international indices on the status of food security and the gender gap. The study indicates the presence of a gender gap in nutrition for reasons more socio-cultural, than economic. The study is focused on G20 nations, especially India as it bears the largest burden of nutrition-related disease within the G20. Data from the Global Hunger Index and the Global Gender Gap Index of 2022 has portrayed India as a dismal performer despite the fact that India runs the largest public food security programme that has targeted nutrition programs for women beneficiaries. The present article identifies the common challenges faced by the G20 nations in the realm of food security and the steps taken for mitigation. The article also examines cooperation within G20 in realising the Sustainable Development Goals related to ending hunger and realising gender equity in food.

The Application of Artificial Intelligence Models for Food Security: A Review

Emerging technologies associated with Artificial Intelligence (AI) have enabled improvements in global food security situations. However, there is a limited understanding regarding the extent to which stakeholders are involved in AI modelling research for food security purposes. This study systematically reviews the existing literature to bridge the knowledge gap in AI and food security, focusing on software modelling perspectives. The study found the application of AI models to examine various indicators of food security across six continents, with most studies conducted in sub-Saharan Africa. While research organisations conducting AI modelling were predominantly based in Europe or the Americas, their study communities were in the Global South. External funders also supported AI modelling research on food security through international universities and research institutes, although some collaborations with local organisations and external partners were identified. The analysis revealed three patterns in the application of AI models for food security research: (1) the exclusive utilisation of AI models to assess food security situations, (2) stakeholder involvement in some aspects of the AI modelling process, and (3) stakeholder involvement in AI modelling for food security through an iterative process. Overall, studies on AI models for food security were primarily experimental and lacked real-life implementation of the results with stakeholders. Consequently, this study concluded that research on AI, which incorporates feedback and/or the implementation of research outcomes for stakeholders, can contribute to learning and enhance the validity of the models in addressing food security challenges.

Analyzing the Effects of Planting Date on the Uncertainty of CERES-Maize and Its Potential to Reduce Yield Gap in Arid and Mediterranean Climates

Decision support system tools such as crop models and considering the uncertainties associated with them are important for making an informed decision to fill the yield gap in farms and increase food security. This study’s objective was to identify and quantify the degree to which crop management practices, as well as climate and soil, affected the uncertainty of total biomass, evapotranspiration, and water productivity of silage maize by using a crop model and spatiotemporal input data. Using a calibrated crop model (DSSAT) and pSIMS platform, three planting dates by considering ten ensemble weather data and three soil profile data were simulated for the time period between 2002 and 2017 with a 2 km × 2 km resolution across maize production areas with arid and Mediterranean climates in Isfahan province, Iran. Additionally, the findings were used to determine the yield gap in the studied area to identify opportunities to boost food production. Our results showed larger uncertainty in Mediterranean climates than in arid climates, and it was more affected by planting date than weather parameters and soil profile. The accuracy of total biomass prediction by using pSIMS-CERES-Maize based on the spatiotemporal input data was 1.9% compared to field experimental data in the dry climate, and the yield gap based on the comparison of modified-pSIMS-CERES-Maize and reported biomass was 6.8 to 13 tons ha−1 in the arid and Mediterranean climate. Generally, all results represented the importance of using crop models and considering spatiotemporal data to increase reliability and accuracy, especially in Mediterranean climates, and their potential to increase food production in developing countries with limited water resources and poor agriculture management.

A comparative analysis of social vulnerability to household food security of Raya Kobo and Raya Alamata Weredas, Ethiopia

The study was conducted in Ethiopia’s neighboring weredas, Raya Alamata in Tigray and Raya Kobo in Amhara regional states, to determine the reasons for disparities in household food security in the two districts. The goal was to see if there was a link between social vulnerability in terms of access of communities to basic infrastructures and food security among households in the two districts. It employed both qualitative and quantitative methods using the Aloui Zouhaier governance model. Despite their near proximity, similar work habits, natural resource availability, and land size, the Raya Alamata and Raya Kobo communities have a substantial gap in food security and social vulnerability, with Raya Alamata reporting 84% food insecurity and Raya Kobo reporting 24%. The study examined the degree of social vulnerability and food security of households, with irrigation systems, agricultural input consumption, extension packages, and other support systems being the primary variables connected to disparities in food security between the two weredas. Differences in social vulnerability and food security between the two weredas were mostly attributable to households’ lack of access to irrigation systems, agricultural supplies, and extension services, which were exacerbated by Raya Alamata’s ostensibly, intended administrative marginalization. The study recommends that basic social infrastructure, such as irrigation infrastructure, extension services, agricultural supplies, roads, standard education, health infrastructure facilities, etc., be provided to households in the area, and that the federal government should hold the regional and local government bodies accountable for the failures to ensure the provision of the services that could realize a reduced level of social vulnerability to food insecurity.

Deep Learning and Machine Learning, Better Together Than Apart: A Review on Biometrics Mobile Authentication

Throughout the past several decades, mobile devices have evolved in capability and popularity at growing rates while improvement in security has fallen behind. As smartphones now hold mass quantities of sensitive information from millions of people around the world, addressing this gap in security is crucial. Recently, researchers have experimented with behavioral and physiological biometrics-based authentication to improve mobile device security. Continuing the previous work in this field, this study identifies popular dynamics in behavioral and physiological smartphone authentication and aims to provide a comprehensive review of their performance with various deep learning and machine learning algorithms. We found that utilizing hybrid schemes with deep learning features and deep learning/machine learning classification can improve authentication performance. Throughout this paper, the benefits, limitations, and recommendations for future work will be discussed.

Narrowing the equity gap in student food security: A student-led approach at the University of Wyoming

About 40% of U.S. college students experienced food insecurity even before the pandemic, when the numbers rose further (Rafferty et al., this issue). The burdens of the problem rest disproportion­ately on the shoulders of students whom our society already disadvantages, such as students of color and those from families who struggle with low income. Although most institutions of higher education have begun efforts to address food insecurity among students in general, fewer have built strategies that explicitly aim to tackle these stark disparities in which student groups face the highest rates of food insecurity. In this practice brief, we share experiences and practice recom­mendations from our shared work to narrow these gaps at the University of Wyoming (UW). . . .

Zero Trust Architecture: Trend and Impact on Information Security

Traditional-based security models are a threat to information security; they have been regarded as weak and ineffective to meet the dynamics of information system trust. An emerging framework, Zero Trust Architecture (ZTA) seeks to close the trust gap in information security through enforcing policies based on identity and continuous authentication and verification. This framework is built on several trust nodes and logical components that attempt to close the trust gap that exists in an information system. The adoption of this framework is still in its teething stage which is a result of several misleading deductions and assumptions. We attempt to explore the intricacies in the framework and close the existing knowledge gap. we surveyed the literature on ZTA and provided a foundational discussion on its implementation and effectiveness from prior studies. while we do not critique other models, this paper studied the strength and variables of the zero-trust security architecture and attempt to provide an overview of the model and close the knowledge gap on the effectiveness of adopting a Zero trust philosophy.

Evaluation of ecological security and ecological maintenance based on pressure-state-response (PSR) model, case study: Fuzhou city, China

Since the modern industrial revolution, the speed of urbanization has accelerated, resulting in a series of ecological environment problems. Fujian province will begin to implement the policy of strengthening the provincial capital in October 2021. Therefore, it is crucial to analyze the ecological maintenance status of Fuzhou and provide a relevant reference for Fuzhou to be built into a national central city. This article takes Fuzhou city as the research area, collects land use data in 2000, 2010, and 2020 from Globeland30, combines remote sensing image data, statistical yearbook data, and Digital Elevation Model (DEM) data, and based on Pressure-State-Responses (PSR) model. A total of 15 indicators were selected to construct the ecological security evaluation system of Fuzhou in 2000, 2010, and 2020, and the weight of each indicator was determined by analytic hierarchy process. Spatial autocorrelation was used to analyze the correlation degree of the land use ecological security index in Fuzhou. The concept of ecological maintenance was introduced to analyze the status of ecological maintenance in Fuzhou during the third period. Finally, the diagnostic model of obstacle factors was used to determine the main obstacle factors affecting ecological security in Fuzhou. The results showed that from 2000 to 2020, the area of cultivated land, grassland and bare land decreased year by year, and transferred 320.63 km2, 25.34 km2 and 9.85 km2 to the artificial surface respectively. The high-value ecological security indexes in 2000, 2010 and 2020 were 27.54%, 28.67% and 30.93% respectively. Therefore, the overall level of ecological security in Fuzhou has improved, but there is a large gap in local ecological security. The area with moderate ecological maintenance degree accounted for 60.15%. From the perspective of the whole city, the very poor ecological maintenance degree mainly concentrated in the downtown area and Pingtan Island, while the very good ecological maintenance degree accounted for very little, mainly distributed in the northwest of Fuzhou. The ecological security index showed the characteristics of high and low-value agglomeration, and the distribution of four clusters in three periods was similar. The main obstacle factors of ecological security in three periods are highway density, population density, surface temperature, intensity of human interference, landscape diversity, GDP per capita, and regional development degree. The ecological security evaluation and ecological maintenance of Fuzhou city is beneficial to provide an efficient decision support tool for the ecological environment protection of Fuzhou City.

Addressing the norms gap in international security through the India-US nuclear relationship

ABSTRACT While scholars (mainly from the Global North) in International Relations have been turning to a (critical) constructivist agenda in norms research, the field has increasingly become devoid of applying this area of research in understanding the nuclear behavior of deviant states from the Global South. The paper therefore attempts to bridge this research gap by using the case of the India-US nuclear relationship. To do so, the paper: i) identifies the probable convergences of the existing literature on nuclear policy and the research on constructivist norms, ii) highlights India’s racial treatment as a Southern nuclear state in academia and policy discourse, and iii) recognizes plausible avenues for the expansion of the Western dominated normative research agenda by analyzing India’s nuclear relationship with the US ― with a specific focus to norm contestation and normative change. In bringing together (critical) constructivists and scholars in nuclear politics to further our understanding of how we perceive security of non-western states, this work makes an epistemological and ontological contribution in the field of international security studies.

Bridging the Last-Mile Gap in Network Security via Generating Intrusion-Specific Detection Patterns through Machine Learning

With successful machine learning applications in many fields, researchers tried to introduce machine learning into intrusion detection systems for building classification models. Although experimental results showed that these classification models could produce higher accuracy in predicting network attacks on the offline datasets, compared with the operational intrusion detection systems, machine learning is rarely deployed in the real intrusion detection environment. This is what we call the last mile problem with the machine learning approach to network intrusion detection, the discrepancy between the strength and requirements of machine learning and network operational semantics. In this paper, we aim to bridge the aforementioned gap. In particular, an LCC-RF-RFEX feature selection approach is proposed to select optimal features of the specific type of attacks from dataset, and then, an intrusion-specific approach is introduced to convert them into detection patterns that can be used by the nonmachine-learning detector for the corresponding specific attack detection in the real-world network environment. To substantiate our approach, we take Snort, KDDCup’99 dataset, and Dos attacks as the experimental subjects to demonstrate how to close the last-mile gap. For the specific type of Dos attacks in the KDDCup’99 dataset, we use the LCC-RF-RFEX method to select optimal feature subset and utilize our intrusion-specific approach to generate new rules in Snort by using them. Comparing performance differences between the existing Snort rule set and our augmented Snort rule set with regard to Dos attacks, the experimental results showed that our approach expanded Snort’s detection capability of Dos attacks, on average, reduced up to 25.28% false-positive alerts for Teardrop attacks and Synflood attacks, and decreased up to 98.87% excessive alerts for Mail bomb attacks.

Policy Options to Reduce the Black‐White Gap in Retirement Security

AbstractThe racial gap in retirement security is profound. Black people aged 65 and older are four times as likely as their White peers to live in poverty, and on average hold only one‐fourth as much household wealth. This deficit has not changed much over the past two decades, but various policy options could help narrow the gap. Shoring up Social Security's finances, boosting Social Security payments to low‐income beneficiaries, and expanding Supplemental Security Income would improve financial well‐being for many older Black adults. Other policy options, including boosting employment and earnings, facilitating homeownership, and promoting savings could help many younger Black workers build wealth over their lifetime, leaving them more financial secure when they eventually retire.

Last Line of Defense: Reliability Through Inducing Cyber Threat Hunting With Deception in SCADA Networks

There exists a gap between existing security mechanisms and their ability to detect advancing threats. Antivirus and EDR (End Point Detection and Response) aim to detect and prevent threats; such security mechanisms are reactive. This approach did not prove to be effective in protecting against stealthy attacks. SCADA (Supervisory Control and Data Acquisition) security is crucial for any country. However, SCADA is always an easy target for adversaries due to a lack of security for heterogeneous devices. An attack on SCADA is mainly considered a national-level threat. Recent research on SCADA security has not considered "unknown threats," which has left a gap in security. The proactive approach, such as threat hunting, is the need of the hour. In this research, we investigated that threat hunting in conjunction with cyber deception and kill chain has countervailing effects on detecting SCADA threats and mitigating them. We have used the concept of "decoy farm" in the SCADA network, where all attacks are engaged. Moreover, we present a novel threat detection and prevention approach for SCADA, focusing on unknown threats. To test the effectiveness of approach, we emulated several Linux and Windows-based attacks on a simulated SCADA network. We have concluded that our approach detects and prevents the attacker before using the current reactive approach and security mechanism for SCADA with enhanced protection for heterogeneous devices. The results and experiments show that the proposed threat hunting approach has significantly improved the threat detection ability.

Assessing knowledge and skills gap for construction 4.0 in a developing economy

Most developing economies are plagued by a low supply of skilled human capital, which is a known driver of economic growth and development. The introduction of the fourth industrial revolution (4IR) concept to the construction industry referred to as Construction 4.0 has introduced new technologies that require an increase in knowledge and skills requirement for workers. This comes with a host of challenges for developing countries. To this end, the current study focuses on Nigeria, which is the largest economy in Africa and a fast developing country. The study therefore seeks to explore the knowledge and skills gap among professionals within the Nigerian construction industry. This was achieved via a structured survey research design to elicit information from professionals in the construction industry. A convenience sampling technique was used to select 136 respondents. Data collected from the questionnaire were analysed using mean score, Pearson chi‐square and t test of SPSS V23. The findings revealed a moderate knowledge level of Construction 4.0 technologies and a low level of training among professionals. Also, the result shows a high skills gap in human machine communication, data analytics and cyber security. The study recommends the promotion of human capital development to enhance the implementation of Construction 4.0. This can be achieved via a review and redesign of existing curricula in educational institutions and commitment on the part of management and professional bodies to training and re‐skilling of the workforce.

Traditionally fermented pickles: How the microbial diversity associated with their nutritional and health benefits?

Abstract Historically, pickling is one of the oldest preservation methods of several foodstuffs such as vegetables, fruits, fish, and meat. Pickling imparts unique and desirable changes in flavor, texture and color that take place over time in fermented pickles. Microorganisms (mainly lactic acid bacteria, Micrococcaceae, Bacilli, yeasts, and filamentous fungi) play a pivotal role in the pickling of foodstuffs while affecting the quality and safety of the final product. This review focuses on the common traditional fermented pickles and their nutritional, therapeutic, and economic potentials. Furthermore, the technological progress in screening microbial communities associated with the traditional pickles is summarized. Finally, this paper will tackle with the role of pickles in filing the gap in food security, the safety aspect of traditional pickles and biofortication as an interesting technique to improve the quality of traditional pickles.

Additive Manufacturing Cyber-Physical System: Supply Chain Cybersecurity and Risks

Additive Manufacturing (AM) methods have become increasingly efficient and industrially viable in the past ten years. These methods offer the freedom of complexity to the designers and choices of localized and pull-based production system to the managers. These propositions of AM have been enabling custom manufacturing and are catalysts for rapid growth of additive manufacturing (AM). This paper analyzes the general characteristics of AM supply chain and proposes three AM supply chain models based on the specific nature of the industry. Our description of the models emphasizes on adopting an holistic view of the AM supply chain and therefore includes raw material, printer hardware and the virtual supply chain. Throughout the product life cycle of additively manufactured products, the interlacing of the virtual supply chain ( digital thread ) with the physical supply chain and their operations fundamentally make the AM process a cyber-physical system (CPS). Therefore, the technology brings along with it benefits of a CPS as well as a new class of attack vectors. We discuss the possible attacks (printer, raw material and design level), risks (reverse engineering, counterfeiting and theft) and provide an enhanced risk classification scheme. We contend that the traditional cybersecurity methods need to evolve to address the new class of attack vectors that threaten the AM supply chain and also discuss the nature of existing solutions that help in addressing the risks and attack threats. In providing an holistic view of the AM supply chain the interdependencies of the processes in the AM supply chain are presented and we elucidate the effects of local attack vectors on the entire supply chain. Further, we discuss the existing security measures to mitigate the risk and identify the existing gap in AM security that needs to be bridged.