Security Practices Research Articles

Securing Container Images through Automated Vulnerability Detection in Shift-Left CI/CD Pipelines

Integrating shift-left security practices and automated vulnerability detection in container images is imperative for modern software development, given the dynamics and vulnerability landscape. This crucial methodology emphasizes security from the initial stages of integration in container-based environments like Docker and Kubernetes. The paper examines containerization security challenges, including image vulnerabilities, insecure configurations, runtime risks, weak orchestration security, and supply chain weaknesses, while stressing compliance with regulatory rules. It explores how this automated approach leverages vulnerability detection methods integrated into Continuous Integration/Continuous Deployment (CI/CD) pipelines through static and dynamic analyses, vulnerability databases, and policy-enforcement mechanisms. Beyond identifying vulnerabilities in CI/CD pipelines, the paper outlines methods to avoid policy violations, mitigate vulnerable images, and prevent recurring practices. Importantly, it underscores the continuous enforcement and remediation of policies and security standards. Security teams must invest efforts in developing policies, automated executions, and remediation procedures, fostering cross-departmental collaboration. In essence, this proactive stance aims to enhance software security, reduce risks, and improve adherence in containerized ecosystems, making it an indispensable component of modern software development.

Bluetooth security analysis of general and intimate health IoT devices and apps: the case of FemTech

The number of digital health products is increasing faster than ever. These technologies (e.g. mobile apps and connected devices) collect massive amounts of data about their users, including health, medical, sex life, and other intimate data. In this paper, we study a set of 21 Internet of Things (IoT) devices advertised for general and intimate health purposes of female bodies (aka female-oriented technologies or FemTech). We focus on the security of the Bluetooth connection and communications between the IoT device and the mobile app. Our results highlight serious security issues in the current off-the-shelf FemTech devices. These include unencrypted Bluetooth traffic, unknown Bluetooth services and insecure Bluetooth authentication when connecting to the app. We implement Bluetooth attacks on the communication between these devices and apps, resulting in malfunctioning of the device and app. We discuss our results and provide recommendations for different stakeholders to improve the security practices of Bluetooth-enabled IoT devices in such a sensitive and intimate domain.

IoT-enabled fire detection for sustainable agriculture: A real-time system using flask and embedded technologies

In modern agriculture, the threat of fire poses significant economic and environmental risks. Additionally, traditional fire detection methods are inadequate and poorly integrated with advanced technologies. It is crucial to develop a more efficient and reliable fire detection system. Also, the lack of real-time and accurate monitoring in current systems compromises the resilience and sustainability of farming operations. This article details developing and implementing a real-time fire detection system tailored for smart agriculture. It integrates cutting-edge technologies, including the Internet of Things (IoT), embedded systems, and a Flask-based web application, fortified by cybersecurity measures such as login authentication and secure HTTP protocols. The system's primary aim is to monitor environmental conditions continuously in agricultural fields to detect signs of smoke or flame swiftly, facilitating preventive actions to safeguard crops and infrastructure. The architecture employs sensors distributed across fields to monitor conditions, a Raspberry Pi 3 B+ as the central processing unit for data acquisition and transmission, and a web interface developed using Flask, HTML, and CSS for secure and real-time visualization of detection data. Critical components like the MCP3208 analog-to-digital converter ensure the system's reliability and accuracy. Experimental results confirm the system's efficacy in early fire detection and real-time data visualization via the web browser. Enhanced security features, such as login authentication, protect sensitive information, while regular updates maintain data relevance. This study advances fire prevention and farm preservation efforts, offering a high-performance technological solution for proactive monitoring and quick response to fire risks, thereby supporting food security and sustainable agricultural practices.

Cybersecurity Assessment and Vulnerability Modelling of Networks and Web Services in Nigerian Colleges of Education

Cybersecurity threats are among the most significant risks facing organizations and government today, and administrative boards have now been held accountable. This is an experimental research activity conducted to perform a holistic cybersecurity assessment and vulnerability modelling on the Information and Communication Technology (ICT) infrastructure and services of Colleges of Education in the six geopolitical zones. The study adopts an integrated bi-modal threat modelling and assessment (IBTMA) method by combining assessment and modelling approaches, which involves mixed-methods, along with computer-based experimentation to comprehensively evaluate and model cybersecurity threats, identify vulnerabilities, and propose effective mitigation strategies. Logistic regression data analysis was used to model the relationship between dependent variables (e.g., presence or absence of vulnerabilities or threats) and independent variables (e.g., cybersecurity practices, system configurations, policies, and staff training programs). This cybersecurity assessment provides the initial understanding of the security landscape and practices. The next step involves using the Microsoft Threat Modeling tool on the assets to identify specific threats. These threats are then prioritized based on their potential impact and likelihood. Assessment result of the vulnerability exposure is supported by the threat modelling report, which shows several threats: tampering, elevation of privilege, denial of service, privilege escalation, information disclosure, and spoofing. Findings from the study indicate that colleges face critical network and web vulnerabilities that need holistic solution.

Enhancing financial security in Fintech: Advanced network protocols for modern inter-bank infrastructure

As financial technology (fintech) continues to evolve, ensuring robust security within inter-bank infrastructure has become increasingly critical. This review paper explores advanced network protocols for safeguarding financial transactions and data against modern cyber threats. The study examines key protocols such as Multiprotocol Label Switching (MPLS), Segment Routing, and Internet Protocol Security (IPSEC), highlighting their roles in enhancing the performance and security of financial networks. It addresses current security challenges, including sophisticated cyber-attacks, insider threats, and vulnerabilities associated with cloud services and third-party vendors. Furthermore, the paper outlines the steps for integrating these advanced protocols into existing systems, provides best practices for optimal performance and security, and discusses emerging trends and future directions in financial network security. Recommendations for fintech companies and financial institutions are provided to help them navigate the evolving security landscape effectively. The paper concludes with potential areas for future research, emphasizing the need for ongoing innovation in network security to address emerging threats. Keywords: Financial Security, Advanced Network Protocols, MPLS, Segment Routing, IPSEC.

A deep experimental analysis of energy‐proficient firewall policies and security practices for resource limited wireless networks

AbstractThe role of firewalls and security principles in resource‐limited wireless networks and Wireless Sensor Networks (WSN) is more expected than in any dedicated network environment. The advanced wireless technologies and emerging fashion of autonomous wireless network nodes are mostly expecting resilient firewall services with multi‐level security policies. Wireless networks are classified under sensor networks, Internet of Things (IoT) mobile networks, and so forth. According to the expectations, security frameworks are gradually invented around the communication platform using various ideologies. The novel ideology and respective implementation effort open better solutions against wireless network attacks. Anyhow, the minimal production of time complexity, energy complexity, and computation overhead from any novel security approach is always considered under the best practices. The complexity levels directly affect the wireless node's energy consumption ability and operational spans severely. The energy optimization techniques and load‐balancing techniques integrated into multi‐class firewall rules are extremely useful solutions for resource‐limited wireless networks. This article has been motivated to analyze the recent firewall techniques and secure data communication mechanisms used for securing wireless networks. Consequently, the practice of comparative literature analysis helps to improve the current limitations identified under the classified categories of security mechanisms such as energy‐optimized security principles and load‐balanced security principles. The establishment of secure and energy‐optimized multi‐class firewall rules in each wireless node assures a healthy focus on next‐generation networks. The experiment section shows the benefits of energy‐optimized secure network communication in terms of better accuracy and the benefits of load‐balanced secure network communication through minimal overhead in computing platforms.

Navigating perilous seas: unmasking online banking frauds, perceived usefulness, fear of cybercrime and distrust in online banking

PurposeThe purpose of this study was to examine the relationship between online banking fraud experience and fear of cybercrime and distrust of online banking services, and to understand how perceived usefulness of online banking moderates the relationship.Design/methodology/approachThe number of respondents involved in this study was 271 people from the Central Java region, Indonesia. Statistical analysis was performed using Jeffreys’s Amazing Statistics Program software to examine the relationships and interactions between the variables studied.FindingsExperience of online banking fraud is positively related to fear of cybercrime and distrust of online banking services. Perceived usefulness of online banking moderates the relationship between online banking fraud experience and fear of cybercrime and distrust of digital payments. Perceived usefulness is negatively related to the level of distrust of online banking services.Research limitations/implicationsOverall, the implications of this study underscore the importance of dealing with the risks of cybercrime in online banking services. By focusing on security, user awareness and the role of perceived usefulness, banking service providers can create a safer and more trusting environment for users of online banking services. This also contributes to the development of more innovative services and can increase customer satisfaction and trust.Practical implicationsThe practical application of these findings is important for financial institutions and online banking service providers. Companies must improve cybersecurity with the latest technology and provide education about online security practices. Transparent communication and better customer service will help overcome customer fears. Compliance with security regulations and technological innovation is also important to protect online banking services. With these steps, customer security and trust can be improved, and the adoption of online banking services will increase widely.Social implicationsThe social implications of this research are increasing public awareness about cybersecurity, consumer protection and strengthening trust in online banking services. With joint efforts, a safer and more trusting environment in using online banking services can be realized.Originality/valueThe originality of this research lies in the use of perceived usefulness of online banking as a moderating variable to reduce the negative impact of online banking fraud experience. With a focus on the psychological effects of customers experiencing fraud, this research seeks to rebuild trust and improve the security of online banking services.

Top Management Leadership in Combating Information Security Threats through Organizational Information Security Practices

Top Management Leadership in Combating Information Security Threats through Organizational Information Security Practices

One time pad for enhanced steganographic security using least significant bit with spiral pattern

Data is an important commodity in today’s digital era. Therefore, data needs to get adequate security to prevent misuse. A common data security practice in the transmission of information is cryptography. Another approach is steganography, which hides secret messages in other media that are not confidential and can be accessed by the public. In this study, the spiral pattern is used for data placement using the least significant bit (LSB) method. Modifications were made to the 2-bits LSB to increase the data capacity that can be hidden. In order to increase security, the data is first converted into a datastream using random numbers as one time pad (OTP). exclusive-OR (XOR) operation is performed on datastream and OTP to get encrypted data to be hidden. The results showed that the image quality of the steganography results at a capacity close to 100% was still fairly good, as indicated by a peak signal-to-noise ratio (PSNR) value greater than 46 dB. Visually, the steganographic image does not look different from the original one. Likewise, the use of random numbers as OTP succeeded in changing the hidden data significantly, as indicated by the avalanche effect value above 50%.

Cybersecurity preparedness of small-to-medium businesses: A Western Australia study with broader implications

This study was prompted by the scarcity of focused quantitative research on the cybersecurity of SMBs. Our research aimed to understand the factors influencing SMBs' approach to cybersecurity, their level of threat awareness and the importance placed on cybersecurity. It also explored the extent to which NIST CSF practices are implemented by SMBs while also detecting and ranking the prevalent challenges faced by SMBs. Additionally, resources that SMBs turn to for help and guidance were also evaluated. While the survey-based study was on Western Australian SMBs, the results are of more general and wider interest. Our study found the lack of funds to be the biggest hindrance to cybersecurity, along with a lack of knowledge on where to start implementing good security practices. SMBs also lacked familiarity with relevant regulations and frameworks. The study highlights areas for improvement, such as access control mechanisms, individual user accounts, formalised policies and procedures, and dedicated budgets. SMBs heavily rely on Google search for cybersecurity information, emphasising the need for optimised search results from authoritative sources. IT service providers and informal networks also emerge as important sources of cybersecurity guidance, while local universities could assist SMBs but remain underutilised in this regard. Interestingly, factors such as organisational size, industry sector, and revenue level did not significantly impact SMBs' perception of vulnerability to cyber threats. However, further investigation is needed to evaluate the effectiveness of different IT service models for SMBs' cybersecurity needs. Overall, the research provides valuable insights into the specific gaps and challenges faced by SMBs in the cybersecurity domain, as well as their preferred methods of seeking and consuming cybersecurity assistance. The findings can guide the development of targeted strategies and policies to enhance the cybersecurity posture of SMBs.

On the factors of students' cybersecurity behavior

The article is an exploratory research on the security awareness of university students in the light of various sociodemographic factors. Information technology, and information security in particular, plays a critical role in this context. Awareness is a fundamental component of security preparedness because it empowers individuals to recognize and respond to security threats and fosters a security culture. By educating students and teachers about the importance of security and best practices, educational organizations can significantly enhance their overall security posture. The article points out that raising awareness of information security is not a simple task and requires a systematic approach. In this respect, education has both the potential and the responsibility to develop an information security mindset. The results of the article show that although the average response value of 3.735 suggests a security-aware attitude, the significant difference between the minimum and maximum values indicates a high variability. Looking in more detail at information security factors, the article reveals that law students are more cautious about protecting personal data, while IT and engineering students are more open to online banking and shopping.

Security and Privacy Challenges in Software as a Service (SaaS)

This document In the rapidly evolving landscape of cloud computing, Software as a Service (SaaS) has emerged as a dominant model for delivering software applications over the internet. However, this model introduces significant security and privacy challenges. This research paper delves into these issues, highlighting the unique risks associated with SaaS platforms. The study explores common vulnerabilities such as data breaches, unauthorized access, data loss, and identity theft, which are exacerbated by the centralized nature of SaaS solutions where data is stored on remote servers managed by service providers. To address these challenges, the paper underscores the importance of robust security measures, including encryption, access controls, and secure software development practices. Additionally, it emphasizes the necessity for SaaS providers to adopt advanced privacy protection techniques like data anonymization and differential privacy to meet user expectations and comply with stringent legal regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Through a comprehensive review of literature and analysis of current SaaS security practices, the research identifies critical risks and provides strategic recommendations for enhancing security and privacy in SaaS environments. By integrating multiple layers of security throughout the software development lifecycle, enforcing strict access management protocols, and ensuring compliance with regulatory standards, SaaS providers can significantly mitigate risks and safeguard sensitive user data. The findings of this study highlight the imperative for continuous improvement in SaaS security strategies to keep pace with emerging threats and technological advancements. Ultimately, by prioritizing security and privacy, SaaS providers can not only protect their users but also gain a competitive edge in the increasingly crowded market of cloud services.

Unraveling the dynamics of password manager adoption: a deeper dive into critical factors

Purpose The purpose of this study is to comprehensively explore the password manager adoption landscape, delving into crucial factors such as performance, trust, social influence, self-efficacy, risk perception, security concerns, enjoyment and facilitating conditions. It also aims to contribute meaningful insights to security product research and practice. Design/methodology/approach A survey was used to investigate the characteristics of adoption intention for password managers. In total, 156 participants from a public university located in the Midwest region of the USA voluntarily completed the survey. Partial least squares structural equation modeling was used to estimate and validate causal relationships and the proposed research model. Findings Through empirical validation, this study demonstrates that constructs such as social influence, web-specific self-efficacy and perceived risk directly impact trust in password managers. Facilitating conditions and perceived security controls are identified as direct influencers on performance expectancy, deviating from the pathways of the traditional framework. Moreover, the model introduces novel elements crucial for comprehending password manager adoption, including “web-specific self-efficacy” and “perceived security control.” Originality/value The paper systematically reviews existing research on password managers, shedding light on crucial factors significantly influencing adoption behavior. By introducing deviations from conventional frameworks and theories, the study emphasizes the innovative nature of its model. It also formulates strategies to catalyze wider adoption and promote effective design of password managers, increasing user engagement rates.

Cyber Threats in the Health Sector in the DRC: Risks, Opportunities, Consequences and Preventive Measures

While hackers ravage the world, social media, banking and other accounts are hacked every day, but cyber threats in DRC are not taken very seriously. There is a total negligence on the part of the country's authorities and hospital managers and human lives are in danger. one of the large hospitals of Kinshasa HJ Hospital has always been faced with multiple problems concerning its system. Once I left to get tested, I found a crowd waiting for the results because their system was blocked. It took almost a week to find a solution to this problem. But no statement has been released regarding this issue. lives are in danger; personal data is not protected and no one is complaining. We encounter many diverse cases across the country. With a country of more than 100 million inhabitants, the country cannot escape this scourge. As in many other countries, healthcare facilities in the DRC are increasingly dependent on IT systems to manage patient records, appointments, medical prescriptions and other critical aspects of their operation. However, this increased reliance on information and communications technology (ICT) also exposes healthcare organizations to various online threats, such as cyberattacks, malware, ransomware, and data theft. These threats can have serious consequences, including disruption of healthcare services, disclosure of confidential patient data and even endangering lives. To combat these threats, it is crucial that healthcare facilities in the DRC implement robust cybersecurity measures. This includes raising staff awareness of IT security practices, implementing effective firewalls and antivirus software, regularly backing up data, and training and educating users on how to recognize and report malware. suspicious online activities. Additionally, close collaboration between health authorities, regulators, healthcare providers and cybersecurity experts is essential to develop effective policies and protocols to protect healthcare infrastructure and data from cyber threats. constantly evolving. 1 Published by Maxime Gautier, December 13 2023 We decided to put together this article to be able to enlighten the entire Congolese community, more specifically health establishments, and warn them about the dangers and risks of technology.

Detection of Threats to Farm Animals Using Deep Learning Models: A Comparative Study

The increasing global population and environmental changes pose significant challenges to food security and sustainable agricultural practices. To overcome these challenges, protecting farm animals and effectively detecting potential environmental threats is critical for economic and ecological sustainability. In this context, the current study examined the animal detection capabilities and efficiency of advanced deep learning models, such as YOLOv8, Yolo-NAS, and Fast-RNN, across a dataset of 2462 images encompassing various animal species that could pose a risk to farm animals. After converting the images into a standardized format, they were divided into three sets for training, validation, and testing, and each model was evaluated on this dataset during the analysis process. The findings indicated that the YOLOv8 model demonstrated superior performance, with 93% precision, 85.2% recall, and 93.1% mAP50 values, while Yolo-NAS was particularly noteworthy for its high recall value, indicating a remarkable detection ability. The Fast-RNN model also offered significant efficiency with balanced performance. The results reveal the considerable potential of deep learning-based object detection technologies in protecting farm animals and enhancing farm security. Additionally, this study provides valuable insights for future model optimization and customization research.

Evaluation and clustering of health security performance in Africa: A comparative analysis through the entropy-TOPSIS-K-means approach

ABSTRACT This study presents an analysis and comparison of the health security practices (HSP) of 47 African countries based on 2019 and 2021 data. Its objective is to assess, rank, and cluster the countries based on their HSP performance and progress over this period. A comprehensive Entropy-TOPSIS-K-means-based evaluation system was constructed for this purpose. Initially, the entropy method was utilised to analyze and compare investment priorities. Subsequently, the Entropy-TOPSIS was used to evaluate and compare the level of health security (HS) performance and the degree of improvements made by countries over the study period. Finally, the K-means cluster analysis method was applied to classify countries based on their comprehensive evaluation scores. Among the six identified criteria, prevention, detection, reporting, and health system practices were deemed crucial for HS decision-making in both cases. The study revealed significant differences in HSP levels and changes in countries’ improvement levels between their practices in 2019 and 2021. The study classifies the security levels of African countries into five clusters and provides a detailed analysis, implications, and recommendations to aid decision-makers in enhancing HS outcomes. Its results offer empirical evidence to support planning, resource allocation, and reallocation for strengthening HSP in the region.

Ensemble regression based Extra Tree Regressor for hybrid crop yield prediction system

ObjectiveThe worldwide economies are built on agriculture, and plans for food security, resource allocation, and agricultural practices are all heavily influenced by accurate crop production predictions. Predictive models are becoming indispensable tools for predicting crop prospects due to the development of technology based on data. LimitationA significant disadvantage of the ER-ETR for Hybrid Crop Yield Prediction System can involve overfitting, particularly in cases when the dataset is small or the model complexity is not well managed. Inaccurate forecasts based on unreported data and decreased generalization can result from approach. MethodInitially, the dataset is collected from the GitHub and preprocessed using the Standardscaler method. 70 % of the preprocessed data is used as the training set, and the remaining 30 % is used as the testing set. Kernel Principal Component Analysis (KPCA) is employed to extract the feature. The Least Absolute Shrinkage and Selection Operator (LESSO) Regression is used to feature selection.A reliable method for predicting hybrid crop productivity is provided by the suggested ensemble regression that makes use of feature ensemble regression using Extra Tree Regressor (ER-ETR). ResultA simple internet-based programme for immediate forecasting is created using the Python web framework, and the model that has been trained may be used to predict the resulting profitability. Mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE) and R2 were the testing metrics utilized to assess the classification model. With a 95 % accuracy rate, the suggested model is superior to existing models in terms of accuracy in crop production forecasting while still preserving the data's original distribution.Because of the intuitive online interface, stakeholders can forecast immediately and make well-informed decisions on the best use of resources from agriculture. ConclusionThe study creates a hybrid crop yield prediction system using the ER-ETR approach. Agricultural forecasting benefits greatly from its capacity to integrate several models and take advantage of each one's advantages, which improves prediction accuracy and dependability.

Adopting security practices in software development process: Security testing framework for sustainable smart cities

The dependence on smart city applications has expanded in recent years. Consequently, the number of cyberattack attempts to exploit smart application vulnerabilities significantly increases. Therefore, improving smart application security during the software development process is mandatory to ensure sustainable smart cities. But the challenge is how to adopt security practices in the software development process. There are Several established and mature security testing frameworks exist that consider security requirements and testing during Several already established and mature security testing frameworks exist that consider security requirements and testing during Software Development Life Cycle (SDLC), but there is a unique challenges posed by smart city applications and the need for a comprehensive approach to address the evolving threat landscape in this context. This paper proposed a framework that adopts security testing practices in all phases of the software development process. The proposed framework identifies several security activities and steps that can be applied in each phase of the software development process.

Leveraging Machine Learning to Improve Access Control Mechanisms in Data Warehousing

This paper delves into integrating Machine Learning (ML) algorithms into Access Control Mechanisms (ACM) within Data Warehouses (DW) to enhance both security and operational efficiency. Traditional ACMs, notably role-based access control (RBAC), often struggle with adapting to dynamic threats and complex access patterns. ML tackles these issues by improving access decision accuracy through real-time anomaly detection and adaptive control based on user behavior and contextual insights. The study examines the implementation and benefits of ML-enhanced ACMs in DW environments, highlighting their effectiveness in mitigating unauthorized access and insider threats. This research also addresses critical considerations, such as privacy protections and the interpretability of ML models, crucial for maintaining regulatory compliance and stakeholder trust. Ultimately, this study emphasizes ML's pivotal contribution to advancing security practices within Data Warehouses (DWs) and proposes future research directions to enhance adaptive security measures in evolving digital landscapes.

MHealth Apps for Dementia, Alzheimer Disease, and Other Neurocognitive Disorders: Systematic Search and Environmental Scan.

Lifestyle behaviors including exercise, sleep, diet, stress, mental stimulation, and social interaction significantly impact the likelihood of developing dementia. Mobile health (mHealth) apps have been valuable tools in addressing these lifestyle behaviors for general health and well-being, and there is growing recognition of their potential use for brain health and dementia prevention. Effective apps must be evidence-based and safeguard user data, addressing gaps in the current state of dementia-related mHealth apps. This study aims to describe the scope of available apps for dementia prevention and risk factors, highlighting gaps and suggesting a path forward for future development. A systematic search of mobile app stores, peer-reviewed literature, dementia and Alzheimer association websites, and browser searches was conducted from October 19, 2022, to November 2, 2022. A total of 1044 mHealth apps were retrieved. After screening, 152 apps met the inclusion criteria and were coded by paired, independent reviewers using an extraction framework. The framework was adapted from the Silberg scale, other scoping reviews of mHealth apps for similar populations, and background research on modifiable dementia risk factors. Coded elements included evidence-based and expert credibility, app features, lifestyle elements of focus, and privacy and security. Of the 152 apps that met the final selection criteria, 88 (57.9%) addressed modifiable lifestyle behaviors associated with reducing dementia risk. However, many of these apps (59/152, 38.8%) only addressed one lifestyle behavior, with mental stimulation being the most frequently addressed. More than half (84/152, 55.2%) scored 2 points out of 9 on the Silberg scale, with a mean score of 2.4 (SD 1.0) points. Most of the 152 apps did not disclose essential information: 120 (78.9%) did not disclose expert consultation, 125 (82.2%) did not disclose evidence-based information, 146 (96.1%) did not disclose author credentials, and 134 (88.2%) did not disclose their information sources. In addition, 105 (69.2%) apps did not disclose adherence to data privacy and security practices. There is an opportunity for mHealth apps to support individuals in engaging in behaviors linked to reducing dementia risk. While there is a market for these products, there is a lack of dementia-related apps focused on multiple lifestyle behaviors. Gaps in the rigor of app development regarding evidence base, credibility, and adherence to data privacy and security standards must be addressed. Following established and validated guidelines will be necessary for dementia-related apps to be effective and advance successfully.