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Machine learning is a technique that is widely employed in both the academic and industrial sectors all over the world. Machine learning algorithms that are intuitive can analyse risks and respond swiftly to breaches and security issues. It is crucial in offering a proactive security system in the field of cybersecurity. In real time, cybersecurity protects information, information systems, and networks from intruders. In the recent decade, several assessments on security and privacy estimates have noted a rapid growth in both the incidence and quantity of cybersecurity breaches. At an increasing rate, intruders are breaching information security. Anomaly detection, software vulnerability diagnosis, phishing page identification, denial of service assaults, and malware identification are the foremost cyber-security concerns that require efficient clarifications. Practitioners have tried a variety of approaches to address the present cybersecurity obstacles and concerns. In a similar vein, the goal of this research is to assess the idealness of machine learning-based intrusion detection systems under fuzzy conditions using a Multi-Criteria Decision Making (MCDM)-based Analytical Hierarchy Process (AHP) and a Technique for Order of Preference by Similarity to Ideal-Solutions (TOPSIS). Fuzzy sets are ideal for dealing with decision-making scenarios in which experts are unsure of the best course of action. The projected work would support practitioners in identifying, prioritising, and selecting cybersecurity-related attributes for intrusion detection systems, allowing them to design more optimal and effective intrusion detection systems. [ABSTRACT FROM AUTHOR]

The current cyber-attack environment has put even the most protected systems at risk as the hackers are now modifying technologies to exploit even the tiniest of weaknesses and infiltrate networks. In this situation, it's critical to design and construct software that is both secure and long-lasting. While security is the most well-defined aspect of health information software systems, it is equally significant to prioritise sustainability because any health information software system will be more effective if it provides both security and sustainability to the customers at the same time. In this league, it is crucial to determine those characteristics in the systems that can help in the accurate assessment of the sustainable-security of the health information software during the development stage. This research work employed the Fuzzy Analytic Network Process (Fuzzy ANP) to estimate the impact of the overall sustainable-security of health information software systems and their characteristics in order to achieve a high level of sustainable-security. Furthermore, the study validates the efficacy of the Fuzzy ANP procedure by testing it on five different versions of a health information software system through Fuzzy Technique for Order of Preference by Similarity to Ideal Solutions (Fuzzy TOPSIS). Despite the sensitivity of the health information software systems, this study employed multiple versions of health information software system. When compared with the existing procedures for testing the sustainable-security of health information software systems, the outcomes were conclusive and significantly more effective. Besides saving time and resources, the mechanism suggested in this research work aims to establish an outline that software practitioners can follow to enhance the sustainable-security of health information software systems. [ABSTRACT FROM AUTHOR]

Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. To preserve networks, information, and intelligence, malware must be detected as soon as feasible. This article compares various attribute extraction techniques with distinct machine learning algorithms for static malware classification and detection. The findings indicated that merging PCA attribute extraction and SVM classifier results in the highest correct rate with the fewest possible attributes, and this paper discusses sophisticated malware, their detection techniques, and how and where to defend systems and data from malware attacks. Overall, 96% the proposed method determines the malware more accurately than the existing methods. [ABSTRACT FROM AUTHOR]

The blockchain technology plays a significant role in the present era of information technology. In the last few years, this technology has been used effectively in several domains. It has already made significant differences in human life, as well as is intended to have noticeable impact in many other domains in the forthcoming years. The rapid growth in blockchain technology has created numerous new possibilities for use, especially for healthcare applications. The digital healthcare services require highly effective security methodologies that can integrate data security with the availablemanagement strategies. To test and understand this goal of security management in Saudi Arabian perspective, the authors performed a numerical analysis and simulation through a multi criteria decision making approach in this study. The authors adopted the fuzzy Analytical Hierarchy Process (AHP) for evaluating the effectiveness and then applied the fuzzy Technique forOrder of Preference by Similarity to Ideal Solution (TOPSIS) technique to simulate the validation of results. For eliciting highly corroborative and conclusive results, the study referred to a real time project of diabetes patients' management application of Kingdom of Saudi Arabia (KSA). The results discussed in this paper are scientifically proven and validated through various analysis approaches. Hence the present study can be a credible basis for other similar endeavours being undertaken in the domain of blockchain research. [ABSTRACT FROM AUTHOR]

Security is an important component in the process of developing healthcare web applications. We need to ensure security maintenance; therefore the analysis of healthcare web application's security risk is of utmost importance. Properties must be considered to minimise the security risk. Additionally, security risk management activities are revised, prepared, implemented, tracked, and regularly set up efficiently to design the security of healthcare web applications. Managing the security risk of a healthcare web application must be considered as the key component. Security is, in specific, seen as an add-on during the development process of healthcare web applications, but not as the key problem. Researchers must ensure that security is taken into account right from the earlier developmental stages of the healthcare web application. In this row, the authors of this study have used the hesitant fuzzy-based AHP-TOPSIS technique to estimate the risks of various healthcare web applications for improving security-durability. This approach would help to design and incorporate security features in healthcare web applications that would be able to battle threats on their own, and not depend solely on the external security of healthcare web applications. Furthermore, in terms of healthcare web application's security-durability, the security risk variable is measured, and vice versa. Hence, the findings of our study will also be useful in improving the durability of several web applications in healthcare. [ABSTRACT FROM AUTHOR]

Ever since its outbreak in the Wuhan city of China, COVID-19 pandemic has engulfed more than 211 countries in the world, leaving a trail of unprecedented fatalities. Even more debilitating than the infection itself, were the restrictions like lockdowns and quarantine measures taken to contain the spread of Coronavirus. Such enforced alienation affected both the mental and social condition of people significantly. Social interactions and congregations are not only integral part of work life but also formthe basis of human evolvement. However, COVID-19 brought all such communication to a grinding halt. Digital interactions have failed to enthuse the fervor that one enjoys in face-to-face meets. The pandemic has shoved the entire planet into an unstable state. The main focus and aim of the proposed study is to assess the impact of the pandemic on different aspects of the society in Saudi Arabia. To achieve this objective, the study analyzes two perspectives: the early approach, and the late approach of COVID-19 and the consequent effects on different aspects of the society. We used a Machine Learning based framework for the prediction of the impact of COVID-19 on the key aspects of society. Findings of this research study indicate that financial resources were the worst affected. Several countries are facing economic upheavals due to the pandemic and COVID-19 has had a considerable impact on the lives as well as the livelihoods of people. Yet the damage is not irretrievable and the world's societies can emerge out of this setback through concerted efforts in all facets of life. [ABSTRACT FROM AUTHOR]

Most of the security strategies today are primarily designed to provide security protection, rather than to solve one of the basic security issues related to adequate software product architecture. Several models, frameworks and methodologies have been introduced by the researchers for a secure and sustainable software development life cycle. Therefore it is important to assess the usability of the popular security requirements engineering (SRE) approaches.Asignificant factor in themanagement and handling of successful security requirements is the assessment of security requirements engineering method performance. This assessment will allow changes to the engineering process of security requirements. The consistency of security requirements depends heavily on the usability of security requirements engineering. Several SRE approaches are available for use and each approach takes into account several factors of usability but does not cover every element of usability. There seems to be no realistic implementation of such models because the concept of usability is not specific. This paper aims at specifying the different taxonomy of usability and design hierarchical usability model. The taxonomy takes into account the common quality assessment parameters that combine variables, attributes, and characteristics identified in different approaches used for security requirements engineering. The multiple-criteria decision-making (MCDM) model used in this paper for usability evaluation is called the fuzzy AHP-TOPSIS model which can conveniently be incorporated into the current approach of software engineering. Five significant usability criteria are identified and used to evaluate the six different alternatives. Such strategies are graded as per their expected values of usability. [ABSTRACT FROM AUTHOR]

Water scarcity is the biggest survival challenge for the current generation, and atmospheric water condensation can be a solution. This paper presents the results of a numerical and experimental evaluation of a novel thermoelectric air duct dehumidifier system (TE-ADD) installed on a test chamber. The subject system, made of twenty-four thermoelectric modules along with heat sinks and fans, was used to produce freshwater by extracting moisture from ambient air. The performance of the system was evaluated as a function of the input power and the airflow rate. The results show that the water condensate production increases and the optimal value is achieved for an input power to the TE-ADD system of 6 A at 5 V. A further increase of the input power adversely affects the performance of the system. The condensate production also depends on the flow rate of the air. The optimal flow rate of air at an input current of 5 A and 6 A is 0.011 kg/s. Thus, this system solves two critical environmental issues, i.e., decrease of the thermal load and freshwater production, simultaneously. [ABSTRACT FROM AUTHOR]

Supervisory Control and Data Acquisition (SCADA) systems play an important role in monitoring industrial processes such as electric power distribution, transport systems, water distribution, and wastewater collection systems. Such systems require a particular attention with regards to security aspects, as they deal with critical infrastructures that are crucial to organizations and countries. Protecting SCADA systems from intrusion is a very challenging task because they do not only inherit traditional IT security threats but they also include additional vulnerabilities related to field components (e.g., cyber-physical attacks). Many of the existing intrusion detection techniques rely on supervised learning that consists of algorithms that are first trained with reference inputs to learn specific information, and then tested on unseen inputs for classification purposes. This article surveys supervised learning from a specific security angle, namely SCADA-based intrusion detection. Based on a systematic review process, existing literature is categorized and evaluated according to SCADA-specific requirements. Additionally, this survey reports on well-known SCADA datasets and testbeds used with machine learning methods. Finally, we present key challenges and our recommendations for using specific supervised methods for SCADA systems. [ABSTRACT FROM AUTHOR]