Vulnerable Threats Research Articles

Zoonotic Host Richness in the Global Wildland-Urban Interface.

Where human settlements abut or intermix with wildlands, people may encounter animals that host zoonotic pathogens which can spillover to cause human disease. Known as the wildland-urban interface (WUI), this zone occupies around 5% of the Earth's surface and is home to 3.5 billion people. The rapid spread of SARS-CoV-2 has demonstrated the importance of understanding risk factors for disease among an increasingly urbanized population. However, the contribution of the WUI to zoonotic disease risk is poorly understood. Here, we show that low-level host richness occurs throughout most of the global WUI, and 20% of the human WUI population live in zones of particularly high zoonotic potential, where more than 20 host species could occur. Zones of high zoonotic potential are concentrated in low-middle-income countries (LMICs) across equatorial Africa, Brazil, Central America, and Southeast Asia where vulnerability is further elevated by widespread poverty, inadequate housing, and lack of easily accessible healthcare. Three of four people living in WUIs with high host richness (520 million people) are in LMICs. Of this population, 35% (183 million) live in and around cities in West, East, and South Africa. This means that WUI-based populations of LMICs may face the double threat of high zoonotic potential and vulnerability to disease. Our results identify global priorities for monitoring exposure to zoonotic diseases in the rapidly expanding WUI.

Bird community structure and host plant in Sukamade Resort, Meru Betiri National Park, East Java, Indonesia

Abstract. Rohman A, Wahyudi D, Subchan W, Jumadiawan. 2024. Bird community structure and host plant in Sukamade Resort, Meru Betiri National Park, East Java, Indonesia. Biodiversitas 25: 3295-3308. Meru Betiri National Park (MBNP), East Java, Indonesia, is classified as a conservation area. Management of Biological Natural Resources (MBNR) refers to bird communities in a habitat. Damage to biological natural resources causes a high threat of bird vulnerability and extinction. Birds play a crucial role as indicators of the environment. This research aimed to determine the community structure of birds and host plants at the Sukamade resort. We recorded 49 bird species and 21 host plants. We are actively identifying location points according to habitat variations. Resort offices have the highest diversity (H ' = 2.774). The jungle track has the highest bird evenness index (E = 0.754). Parang Kulon has the highest dominance index (D = 0.162). Pycnonotus goiavier (pg) had the highest abundance found at each location. Pavo muticus (pm) is a protected species according to Indonesian national law. A total of 21 plants were documented as the dominant vegetation. Our research revealed that Ficus benjamina (FB) had the highest frequency of visits by pollinators, and the prevailing avian behavior observed was perching. The presence of Haliaeetus leucogaster (hl) nesting on Artocarpus elasticus (AE) trees was documented. Meanwhile, the Principal Component Analysis (PCA) results showed that four bird species, namely Merops leschenaulti (ml), Rhyticeros undulatus (ru), P. goiavier (pg), Pycnonotus simplex (ps), Anthracoceros albirostris (aa)) were correlated with two host plant species (A. elasticus (AE) and Albizia chinensis (AC)). Our results demonstrate a significant link between the presence of birds and host plants, underscoring their integral role in the ecology.

P-TIMA: a framework of T witter threat intelligence mining and analysis based on a prompt-learning NER model

Abstract Open-source information platforms such as Twitter continuously provide the latest threat intelligence, including new vulnerabilities and in-the-wild exploitations of advanced persistent threat (APT) groups. Automated extraction of threat intelligence from Twitter has become crucial for defenders to access up-to-date threat knowledge. However, existing studies mainly rely on supervised learning methods to extract threat intelligence knowledge, such as entities, which require a large amount of annotated data. This paper presents Threat Intelligence Mining and Analysis based on Prompt Learning (P-TIMA), a framework specifically crafted for extracting and analyzing threat intelligence from Twitter. P-TIMA employs our innovative few-shot entity recognition method, SecEntPrompt (SEP), built on prompt learning, to extract vulnerability intelligence from Twitter. Additionally, P-TIMA analyzes and profiles the overarching vulnerability intelligence obtained from Twitter, along with in-the-wild exploitation intelligence of APT groups. The SEP improves the average entity recognition F1 score by 3.62-4.40 compared with the best-performing comparison model and outperforms the method based on the large language model on recognition performance and inference time. To validate our framework, we apply P-TIMA to extract vulnerability-related threat intelligence from real Twitter data. Through case studies, we then analyze trends in vulnerability threats and the exploitation capabilities of APT groups. In conclusion, our framework provides a more efficient and accurate method for extracting threat intelligence from Twitter, enabling defenders to stay up-to-date with the latest threat trends and helping them improve their defense strategies against cyber attacks.

The Economic Impact of Water Vulnerability on Corporate Sustainability: A Perspective of Corporate Capital Cost

Studies have argued that water risk affects corporate sustainability, but few of them have fully explored whether or not and how water resources have a direct impact on corporate finance and strategy. This study takes the listed companies in the Chinese A-share market from 2019 to 2023 as a sample to understand the threat of water vulnerability to corporate sustainability from the perspective of capital cost. This study argues that water vulnerability positively relates to corporate capital cost by increasing corporate financing constraints. Meanwhile, this study also examines the role of water regulation and water investment in the relationship between water vulnerability and corporate capital cost. Water regulation brings legitimate pressure to corporations and increases the transformation risks faced by them, so it has a positive moderating effect. Water investment can alleviate the vulnerability of local water resources and reduce the physical water risk faced by corporations, so it has a negative moderating effect. The study finds that the two measures mainly play a significant moderating effect on the cost of debt. In addition, the study finds that the positive relationship between water vulnerability and capital cost has industrial and firm-level heterogeneity, while the moderating effect of government water governance has only industrial heterogeneity.

An improved three factor authentication protocol for wireless body area networks

Wireless Body Area Networks (WBAN) is a concrete solution for the healthcare system, and it is paramount to ensure the sensed data by the WBAN sensors is secure against vulnerable threats and inaccessible by unauthorized entities. In light of this, highly robust authentication schemes and solutions are essential for the successful adoption of WBANs. To this end, a plethora of secure authentication solutions have been recommended by investigators over the past few decades. However, the absence of cohesive research work in view of secure mutual authentication schemes does not light on the extensive purpose of offering a bird’s-eye solution to the domain. To address the security issues in the previous schemes, we approach them in the following manner: We reviewed the Ali et al. scheme, a recently proposed authentication scheme for wireless medical sensor networks for healthcare monitoring, asserting that their scheme can withstand various attacks. Despite this, we proved that their scheme still has significant security weaknesses and is vulnerable to various attacks through rigorous cryptanalysis. Afterward, we proposed our three-factor authentication scheme to overcome the vulnerable attacks found in the Ali et al. scheme. Thereafter, we performed an extensive informal and formal analysis using Burrows-Abadi-Needham (BAN) logic to verify the robustness of the proposed scheme. From the analysis, we proved that the proposed authentication scheme is able to withstand various attacks on WBAN. Finally, a critical performance evaluation is done on our scheme. From the results, we identified that our scheme is a robust and identical solution for feature-rich WBAN health-care applications.

The Community Resilience Measurement of Tourism Village in The Special Region of Yogyakarta against The COVID-19 Pandemic

This article or articles aims to measure the resilience index of tourist village communities in DIY, especially in independent and developed tourist villages, and find the factors that influence it. The problem is focused on the biggest impact of the COVID-19 pandemic faced by local communities that have a large dependence on tourism activities. This research is quantitative. Data was collected through a questionnaire survey conducted with the community of tourism village entrepreneurs in DIY. This research focuses the sample on independent and developed tourist villages that are more prepared to face the pandemic. A total of 130 samples were taken with a confidence level of 92% based on the Slovin formula. The first research objective was analyzed by adopting the HCRI (Household Climate Resilience Index) calculation and the second objective was analyzed using multiple linear regression analysis. HCRI calculations are analyzed with the help of tables, percentages, and HCRI models which are used to assess community resilience based on respondents' responses to 31 resilience indicators. Resilience weights of 1-5 are given to indicators classified on a Likert scale, namely very high (5), high (4), medium (3), low (2), and very low (1), which means household resilience increases as the calculated value increases. The final results of this calculation are classified into five categories starting from very low resilience to very high resilience. The results of the analysis show that communities in developed and independent tourist villages in DIY have relatively high resilience in facing the COVID-19 pandemic. This resilience is influenced by economic, institutional, and social capital factors. However, tourist villages in DIY still need to be aware of the threat of vulnerability that may still occur during the COVID-19 pandemic in 12 indicators related to the economic condition of the community. These results have implications for the need to increase community resilience in developed/independent tourist villages by considering vulnerability factors in this research. In this case, both the government and tourist village managers are advised to focus more on programs to improve the quality of tourism business actors' resources and also the ability to mitigate economic losses after the COVID-19 pandemic to maintain the stability and sustainability of the economy of tourist village communities.

Program Respon untuk Meminimalisir Kekerasan (Seksual) Pada Lokasi Pengungsian Merapi Di Kabupaten Sleman

This research aims to study Sleman Regency Gender’s Response Program best practice on how they can decrease the (sexual) violence risk to the vulnerable group in the disaster camps. The policy started by involving the Office of Women's Empowerment and Child Protection (DP3AP2KB) into the Disaster Command System Management led by Regional Agency for Disaster Management (BPBD) Sleman under the law: Keputusan Bupati Sleman Nomor 17 tahun 2020 tentang Renkontijensi Bencana Gunung Api Merapi. This research uses qualitative descriptive with case study method refer to Sleman practices. Data collecting from literature studies, observation and indepth interview to informans related. The efforts taken by Sleman Regency, succeed in reducing complains and eliminate bad news about (sexual) violence to the vulnerable in disaster camps. Best practice from Sleman regency hopefully can be an inspiration for a conceptual framework for another region in decreasing vulnerable threat and risk for all disaster kind.

Performance Evaluation of Machine Learning Approaches in Detecting IoT-Botnet Attacks

Botnets are today recognized as one of the most advanced vulnerability threats. Botnets control a huge percentage of network traffic and PCs. They have the ability to remotely control PCs (zombie machines) by their creator (BotMaster) via Command and Control (C&C) framework. They are the keys to a variety of Internet attacks such as spams, DDOS, and spreading malwares. This study proposes a number of machine learning techniques for detecting botnet assaults via IoT networks to help researchers in choosing the suitable ML algorithm for their applications. Using the BoT-IoT dataset, six different machine learning methods were evaluated: REPTree, RandomTree, RandomForest, J48, metaBagging, and Naive Bayes. Several measures, including accuracy, TPR, FPR, and many more, have been used to evaluate the algorithms’ performance. The six algorithms were evaluated using three different testing situations. Scenario-1 tested the algorithms utilizing all of the parameters presented in the BoT-IoT dataset, scenario-2 used the IG feature reduction approach, and scenario-3 used extracted features from the attacker’s received packets. The results revealed that the assessed algorithms performed well in all three cases with slight differences.

A Cryptosystem for Database Security Based on RC4 Algorithm

Because of vulnerable threats and attacks against database during transmission from sender to receiver, which is one of the most global security concerns of network users, a lightweight cryptosystem using Rivest Cipher 4 (RC4) algorithm is proposed. This cryptosystem maintains data privacy by performing encryption of data in cipher form and transfers it over the network and again performing decryption to original data. Hens, ciphers represent encapsulating system for database tables

GIS integrated RUSLE model-based soil loss estimation and watershed prioritization for land and water conservation aspects

Land degradation has become one of the major threats throughout the globe, affecting about 2.6 billion people in more than 100 countries. The highest rate of land degradation is in Asia, followed by Africa and Europe. Climate change coupled with anthropogenic activities have accelerated the rate of land degradation in developing nations. In India, land degradation has affected about 105.48 million hectares. Thus, modeling and mapping soil loss, and assessing the vulnerability threat of the active erosional processes in a region are the major challenges from the land and water conservation aspects. The present study attempted rigorous modeling to estimate soil loss from the Banas Basin of Rajasthan state, India, using GIS-integrated Revised Universal Soil Loss Equation (RUSLE) equation. Priority ranking was computed for different watersheds in terms of the degree of soil loss from their catchments, so that appropriate conservation measures can be implemented. The total area of Banas basin (68,207.82 km2) was systematically separated into 25 watersheds ranging in area from 113.0 to 7626.8 km2. Rainfall dataset of Indian Meteorological Department for 30 years (1990–2020), FAO based Soil map for soil characterization, ALOS PALSAR digital elevation model for topographic assessment, and Sentinal-2 based land use and land cover map were integrated for modeling and mapping soil erosion/loss risk assessment. The total annual soil loss in the Banas basin was recorded as 21,766,048.8 tons. The areas under very low (0–1 t ha-1 year-1), low (1–5 t ha-1 year-1), medium (5–10 t ha-1 year-1), high (10–50 t ha-1 year-1) and extreme (>50 t ha-1 year-1) soil loss categories were recorded as 24.2, 66.8, 7.3, 0.9, and 0.7%, respectively, whereas the respective average annual soil loss values were obtained as 0.8, 3.0, 6.0, 23.1, and 52.0 t ha-1 year-1. The average annual soil loss among different watersheds was recorded in the range of 1.1–84.9 t ha-1 year-1, being highest (84.9 t ha-1 year-1) in WS18, followed by WS10 (38.4 t ha-1 year-1), SW25 (34.7 t ha-1 year-1) and WS23 (17.9 t ha-1 year-1), whereas it was lowest for WS8 (1.1 t ha-1 year-1). Thus, WS18 obtained the highest/top priority rank in terms of the average annual soil loss (84.9 t ha-1 year-1) to be considered as the first priority for land and water conservation planning and implementation. The quantitative results of this study would be useful for implementation of land and water conservation measures in the problematic areas of the Banas basin for controlling soil loss through water erosion.

Secure Software Development Awareness: A Case Study of Undergraduate Developers

As ubiquitous computing becomes an increasingly inherent component of everyday life due to the rapid growth of communication technologies and globalization, threats against information systems have taken a more latent yet lethal dimension. This emergent digital security challenge has correspondingly motivated a proactive change in the software engineering process in recent decades. This change has inspired more intense research scrutiny on security as a crucial component of any software system. Moreover, in today’s virtual world of hyperconnectivity, the most significant vulnerabilities in modern information systems security are software centred. Nevertheless, research shows that software developers often lack the required knowledge and skills in secure software systems development (SSD). Such knowledge ensures that all the resultant software components of each development lifecycle are correctly implemented rather than merely following the SSD lifecycle. Also, the knowledge engenders software security consciousness as a professional attitude amongst developers. Therefore, investigating students’ awareness of SSD principles can generate insight into evolving the undergraduate software development curriculum – a path to building future career developers. The study used a voluntary online survey to recruit a sample of 76 undergraduate developers and employed a descriptive approach to data analysis. Among other findings, the study revealed that participants' perception of the threat of software vulnerability impacts their attitude towards security on online and mobile platforms. And that though over 90% of the undergraduate developers took software vulnerability threats either “serious” or “extremely serious”, this disposition did not reflect the depth of their knowledge and experience in SSD.

Issues and challenges for women in creating spaces at decision-making levels: A case study of Pakistan Muslim league-N

In many Western and Asian countries, women’s empowerment has increased significantly, yet women are still underrepresented in political participation and political decision-making. This study explores the political engagement of women in a society that discriminates against them on the basis of their sex, and in a political system that is heavily patriarchal. The study is aimed at exploring the issues and challenges that women confront in political participation and decision-making in the party political space. To achieve this goal, a qualitative approach has been adopted, in which data has been collected and processed in a qualitative manner. This research conducts a case study of Pakistan Muslim League (N) which served as a sample for data collection. Thematic analysis has been used to decipher the comments of important stakeholders interviewed for qualitative data, as described by Braun and Clarke. Although literature has studied the complex of multiple interlinked causes for women’s insufficient and ineffective political participation, this article presents a novel way to explore those issues and a thematic way to understand their complexity. However, the study's findings indicate that women's political engagement and decision-making are hampered for a variety of reasons. A male-dominated culture, lack of social acceptance, structural halts, gender-imbalanced political parties, societal vulnerability threats and an inability to empower women are a few of the main concerns. Numerous specific problems have been identified as well, such as the persistence of historical patterns and the opportunity gap as well as psychological satisfaction and religious excuses for the lack of political participation and the vulnerability of being scandalized, weak economic status and gender sensitivity in the workplace. In Pakistani society, these highlighted variables are so firmly ingrained in the political and social structure that they are mostly taken for granted and go unnoticed.

Modeling and Defense of Social Virtual Reality Attacks Inducing Cybersickness

Social Virtual Reality Learning Environments (VRLE) offer a new medium for flexible and immersive learning environments with geo-distributed users. Ensuring user safety in VRLE application domains such as education, flight simulations, military training is of utmost importance. Specifically, there is a need to study the impact of “immersion attacks” (e.g., chaperone attack, occlusion) and other types of attacks/faults (e.g., unauthorized access, network congestion) that may cause user safety issues (i.e., inducing of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cybersickness</i> ). In this article, we present a novel framework to quantify the security, privacy issues triggered via immersion attacks and other types of attacks/faults. By using a real-world social VRLE viz., vSocial and creating a novel attack-fault tree model, we show that such attacks can induce undesirable levels of cybersickness. Next, we convert these attack-fault trees into stochastic timed automata (STA) representations to perform statistical model checking for a given attacker profile. Using this model checking approach, we determine the most vulnerable threat scenarios that can trigger high occurrence cases of cybersickness for VRLE users. Lastly, we show the effectiveness of our attack-fault tree modeling by incorporating suitable design principles such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hardening</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">diversity</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">redundancy</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">principle of least privilege</i> to ensure user safety in a VRLE session.

The effects of target vulnerabilities on social movement outcomes: Wage campaigns in U.S. cities

How do target vulnerabilities affect social movement outcomes? Walsh (1986) and Jasper and Poulsen (1993) conceptualize target vulnerabilities as characteristics or practices of organizations that are inconsistent with relevant normative expectations (laws, professional codes, widely-held public expectations, etc.). Organizations' claims to legitimacy, and the access to resources that legitimacy bestows, are based on the presumption of adherence to those normative expectations, so credible threats to that presumption cannot be ignored. We draw on political process theories and the logic of policy change to analyze how target vulnerabilities affect movement outcomes in campaigns for living wages (1994–2003) and for increased minimum wages (2012–2017). For living wage campaigns, we model the effects of political elites’ vulnerability to the threat of popular delegitimation and vulnerability to non-participation by municipal workers on the likelihood of city councils 1) holding a vote (access outcome) and 2) adopting a living wage ordinance (advantages outcome). Using sequential regression analyses of these outcomes in 596 U.S. cities, we find that the effects of target vulnerabilities vary across living wage ordinance outcomes: delegitimation vulnerabilities affect the likelihood of access outcomes, whereas adoption advantages are more likely in the presence of non-participation vulnerabilities. We test the generalizability of our model and measures in an analysis of local minimum wage ordinance campaigns. We show that contentious politics at the local level differs from the national-level in ways that render city political elites potentially vulnerable to movement threats of delegitimation and non-participation.

SECURE SOFTWARE DEVELOPMENT AWARENESS: A CASE STUDY OF UNDERGRADUATE DEVELOPERS

As ubiquitous computing becomes an increasingly inherent component of everyday life due to the rapid growth of communication technologies and globalization, threats against information systems have taken a more latent yet lethal dimension. This emergent digital security challenge has correspondingly motivated a proactive change in the software engineering process in recent decades. This change has inspired more intense research scrutiny on security as a crucial component of any software system. Moreover, in today’s virtual world of hyperconnectivity, the most significant vulnerabilities in modern information systems security are software centred. Nevertheless, research shows that software developers often lack the required knowledge and skills in secure software systems development (SSD). Such knowledge ensures that all the resultant software components of each development lifecycle are correctly implemented rather than merely following the SSD lifecycle. Also, the knowledge engenders software security consciousness as a professional attitude amongst developers. Therefore, investigating students’ awareness of SSD principles can generate insight into evolving the undergraduate software development curriculum – a path to building future career developers. The study used a voluntary online survey to recruit a sample of 76 undergraduate developers and employed a descriptive approach to data analysis. Among other findings, the study revealed that participants’ perception of the threat of software vulnerability impacts their attitude towards security on online and mobile platforms. And that though over 90% of the undergraduate developers took software vulnerability threats either “serious” or “extremely serious”, this disposition did not reflect the depth of their knowledge and experience in SSD.

SCNTA

Real-time network inspection applications face a threat of vulnerability as high-speed networks continue to expand. For companies and ISPs, real-time traffic classification is an issue. The classifier monitor is made up of three modules: Capturing_of_Packets (CoP) and pre-processing, Reconciliation_of_Flow (RoF), and categorization of Machine Learning (ML). Based on parallel processing along with well-defined interfacing of data, the modules are framed, allowing each module to be modified and upgraded separately. The Reconciliation_of_Flow (RoF) mechanism becomes the output bottleneck in this pipeline. In this implementation, an optimal reconciliation process was used, resulting in an average delivery time of 0.62 seconds. In order to verify our method, we equated the results of the AdaBoost Ensemble Learning Algorithm (ABELA), Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN), and Flexible Naive Bayes (FNB) in the classification module. The architectural design of the run time CSNTA categorization (flow-based) scheme is presented in this paper.

Design Hands-on Lab Exercises for Cyber-physical Systems Security Education

The integration of cyber-physical systems (CPS) has been extremely advantageous to society, it merges the attention of cybersecurity for vehicles as a timely concern as a matter of public and individual. The failure of any vehicle system could have a serious impact on vehicle control and cause undesired consequences. With the growing demand for security in CPS, there are few hands-on labs/modules available for training current students, future engineers, or IT professionals to understand cybersecurity in CPS. This study describes the execution of a free security testbed to replicate a vehicle’s network system and the implementation of this testbed via hands-on lab designed to introduce concepts of vehicle control systems. The hands-on lab simulates insider threat scenarios where students had to use can-utils toolkits and SavvyCAN to send, modify, and capture the network packet and exploit the system vulnerability threats such as replay attacks and fuzzing attacks on the vehicle system. We conducted a case study with 21 university-level students, and all students completed the hands-on lab, pretest, posttest, and a satisfaction survey as part of a non-graded class assignment. The experimental results show that most students were not familiar with cyber-physical systems and vehicle control systems and never had the chance to do any hands-on lab in this field before. Furthermore, students reported that the hands-on lab helped them learn about CAN-bus and rated high scores for enjoyment. We discussed the design of an affordable tool to teach about vehicle control systems and proposed directions for future work.

Analysis Factors Of Affecting Community Stigma With Covid-19 Patient Based On Health Belief Model Theory

Introduction: The stigma that exists in society views people with COVID-19 as people who are avoided, the feared disease is very contagious so that stigma causes anxiety and prejudice against sufferers. Objective : To analyze the factors that influence the stigma of society in patients with COVID-19 based on the Health Belief Model (HBM) theory. Methods: Descriptive correlation using a cross-sectional approach. The research sample was 150 respondents with accidental sampling design. The independent variables of this study were knowledge, beliefs and perceptions of threat. The dependent variable was stigma. Data were collected using a questionnaire. Data analysis used multiple regression statistical test with a significance degree of &lt;0.05 (5%). Results: knowledge does not have a significant effect on beliefs (vulnerabilities, advantages, barriers, self-confidence with &gt; 0.05. Knowledge variable has an effect on seriousness &gt; 0.037. Confidence also does not have a significant effect on the threat of vulnerability (1,000), seriousness &gt; 0.999, advantage &gt; 1,000, barriers &gt; 1,000 and belief has an effect on self-confidence &gt; 0.000. For the threat variable does not have a significant effect on stigma &gt; 0.996. Conclusion: The stigma against Covid-19 patients is the perception of seriousness and the perception of threats to Covid-19 patients who are felt to threaten the community to be infected with the virus. Lack of information is a trigger for the stigma of society towards Covid-19 patients.

Exploit Prediction Scoring System (EPSS)

Despite the large investments in information security technologies and research over the past decades, the information security industry is still immature when it comes to vulnerability management. In particular, the prioritization of remediation efforts within vulnerability management programs predominantly relies on a mixture of subjective expert opinion and severity scores. Compounding the need for prioritization is the increase in the number of vulnerabilities the average enterprise has to remediate. This article describes the first open, data-driven framework for assessing vulnerability threat, that is, the probability that a vulnerability will be exploited in the wild within the first 12 months after public disclosure. This scoring system has been designed to be simple enough to be implemented by practitioners without specialized tools or software yet provides accurate estimates (ROC AUC = 0.838) of exploitation. Moreover, the implementation is flexible enough that it can be updated as more, and better, data becomes available. We call this system the Exploit Prediction Scoring System (EPSS).

Behavior Study of The Prevention of Covid-19 Transmission with Health Protocol at Transportation Service Office

The initial entry of Covid-19 in Muna was 8 cases with the highest number of cases in Southeast Sulawesi. This study aims to determine the behavior of preventing Covid-19 by implementing health protocols at the Transportation Service Office based on the theory of the health belief model. This study uses a qualitative design with a phenomenological approach. Data collected through in-depth interviews, observation and documentation. The results based on the Perceived suspectibility factor informants know that people who are susceptible to Covid-19 are people who are old, people who work in community services. Based on Perceived saverity, the informant said that Covid-19 is a pandemic so it is dangerous because of its transmission, and if infected with Covid-19 it can worsen the condition of people who have congenital diseases. Based on Perceived benefits, informants feel the perceived benefits of implementing health protocols in addition to preventing Covid-19 are also useful for daily life. For Perceived barriers, informants feel obstacles in implementing health protocols such as the use of masks because of the small amount of air they breathe. Meanwhile, the Threat factor, informants feel that when they do not apply the health protocol, they feel afraid or anxious. Self-efficacy informants feel confident in carrying out health protocols because they feel safe. Conclusions based on the theory of health belief model, it is concluded that perceived vulnerability factors, perceived benefits, perceived barriers, threats and self-efficacy indicate the behavior of implementing health protocols at the Transportation Service Office in preventing Covid-19.