The proposed research work encompasses implications for infrastructure particularly the cybersecurity as an essential in soft infrastructure, and policy making particularly on secure access management of infrastructure governance. In this study, we introduce a novel parameter focusing on the timestamp duration of password entry, enhancing the algorithm titled EPSBalgorithmv01 with seven parameters. The proposed parameter incorporates an analysis of the historical time spent by users entering their passwords, employing ARIMA for processing. To assess the efficacy of the updated algorithm, we developed a simulator and employed a multi-experimental approach. The evaluation utilized a test dataset comprising 617 authentic records from 111 individuals within a selected company spanning from 2017 to 2022. Our findings reveal significant advancements in EPSBalgorithmv01 compared to its predecessor namely EPSBalgorithmv00. While EPSBalgorithmv00 struggled with a recognition rate of 28.00% and a precision of 71.171, EPSBalgorithmv01 exhibited a recognition rate of 17% with a precision of 82.882%. Despite a decrease in recognition rate, EPSBalgorithmv01 demonstrates a notable improvement of approximately 14% over EPSBalgorithmv00.

The usage of cybersecurity is growing steadily because it is beneficial to us. When people use cybersecurity, they can easily protect their valuable data. Today, everyone is connected through the internet. It’s much easier for a thief to connect important data through cyber-attacks. Everyone needs cybersecurity to protect their precious personal data and sustainable infrastructure development in data science. However, systems protecting our data using the existing cybersecurity systems is difficult. There are different types of cybersecurity threats. It can be phishing, malware, ransomware, and so on. To prevent these attacks, people need advanced cybersecurity systems. Many software helps to prevent cyber-attacks. However, these are not able to early detect suspicious internet threat exchanges. This research used machine learning models in cybersecurity to enhance threat detection. Reducing cyberattacks internet and enhancing data protection; this system makes it possible to browse anywhere through the internet securely. The Kaggle dataset was collected to build technology to detect untrustworthy online threat exchanges early. To obtain better results and accuracy, a few pre-processing approaches were applied. Feature engineering is applied to the dataset to improve the quality of data. Ultimately, the random forest, gradient boosting, XGBoost, and Light GBM were used to achieve our goal. Random forest obtained 96% accuracy, which is the best and helpful to get a good outcome for the social development in the cybersecurity system.

The technological infrastructure is the basis for the successful implementation and operation of information systems in small and medium enterprises. The study aimed to demonstrate the impact of cybersecurity on entrepreneurship strategies in small and medium enterprises. Through technological infrastructure in Balqa Governorate. The study population consisted of small and medium enterprises in Balqa Governorate in Jordan. The study followed the descriptive analytical approach and relied on the questionnaire to collect data. The sample size was 360 individuals were randomly select. The Statistical Package for Social Sciences (SPSS) was use to analyze the data. The study reached a set of results, including that the management of small and medium enterprises is committed to continuous supervision and control of customer information. Dealing with reliable parties to ensure the confidentiality of information, following strict standards for disclosure and circulation of customer data and information based on legal texts. Maintaining the privacy of customers’ financial data, in addition to supporting the successes of individuals based on the personal efforts of employees, providing a suitable work environment for employees, sustaining excellence and achievement, and working to increase awareness among its employees of the importance of innovation and creativity in work. The study recommended that customer data confidentiality should be consider a top priority for small and medium enterprises. The data should be stored in more than one place at the same time, that project websites should follow a privacy policy, and that the customer’s identity should be verify before submitting his data and documents, by involving employees in small and medium enterprises in specialized courses and workshops to demonstrate the importance of data and information confidentiality.

Since the external environment on a global level is very unstable, recovering from various unexpected shocks becomes a challenging question for all countries. Thus, for each country it is necessary to understand its weaknesses and threats. Further, the preparation for any level of uncertainty in various fields must be imperative. Even for the most unpredictable shocks such as pandemic, cyberthreat, or even war. The aim of the article is to evaluate the state resilience of the Baltic States by creating the national resilience index. A state’s resilience is based on four pillars: economic, social, good governance, and defence. The methodology is based the SAW method, data has been collected from NATO and Eurostat databases. As the result of the study, resilience index has been estimated for each year from 2015 to 2022. Results revealed vulnerability and problematic areas of each country.

This study aims to identify key strategies and tactics necessary to effectively implement national social security in a democratic Indonesia. Indonesia established the Law on the National Social Security System in 2004. However, the national social security programs did not commence until 2014. The national social security implementation has faced significant obstacles. These challenges include recurring delays, legal disputes, appeals, judicial reviews, and deviations from the original policy objectives, all threatening the long-term viability of the national social security programs. This article applies a qualitative approach by critically analyzing regulations, government reports, and publicly available data and observing open public meetings and hearings concerning implementing national social security programs. Our findings indicate that implementing national social security policies in a democratic Indonesia depends on effectively managing the dynamic processes involved in policy formulation and adoption. We propose a risk-based decision-making model to assist policymakers in mitigating policy-related risks and enhance the effectiveness of future policy agendas in social security.

The low-carbon economy is the major objective of China’s economy, and its goal is to achieve sustainable economic development. The study enriches the literature on the relationship between digital Chinese yuan (E-CNY), low-carbon economy, AI trust concerns, and security intrusion. The rapid growth of Artificial Intelligence (AI) offered more ways to achieve a low-carbon economy. The digital Chinese yuan (E-CNY), based on the AI technique, has shown its nature and valid low-carbon characteristics in pilot cities of China, it will assume important responsibilities and become the key link. However, trust concerns about AI techniques result in a limitation of the scope and extent of E-CNY usage. The study conducts in-depth research from the perspective of AI trust concerns, explores the influence of E-CNY on the low-carbon economy, and discusses the moderating and mediating mechanisms of AI trust concerns in this process. The empirical data results showed that E-CNY positively affects China’s low-carbon economy, and AI trust concerns moderate the positive impact. When consumers with higher AI trust concerns use E-CNY, their feeling of security intrusion is also higher. It affects the growth of trading volume and scope of E-CNY usage. Still, it reduces the utility of China’s low-carbon economy. This study provides valuable management inspiration for China’s low-carbon economy.