Security Test Case Prioritization through Ant Colony Optimization Algorithm

Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software systems.One of the challenges in software security testing is test case prioritization,which aims to reduce redundancy in fault occurrences when executing test suites.By effectively applying test case prioritization,both the time and cost required for developing secure software can be reduced.This paper proposes a test case prioritization technique based on the Ant Colony Optimization(ACO)algorithm,a metaheuristic approach.The performance of the ACO-based technique is evaluated using the Average Percentage of Fault Detection(APFD)metric,comparing it with traditional techniques.It has been applied to a Mobile Payment Wallet application to validate the proposed approach.The results demonstrate that the proposed technique outperforms the traditional techniques in terms of the APFD metric.The ACO-based technique achieves an APFD of approximately 76%,two percent higher than the second-best optimal ordering technique.These findings suggest that metaheuristic-based prioritization techniques can effectively identify the best test cases,saving time and improving software security overall.

A Trailblazing Framework of Security Assessment for Traffic Data Management

Connected and autonomous vehicles are seeing their dawn at this moment.They provide numerous benefits to vehicle owners,manufacturers,vehicle service providers,insurance companies,etc.These vehicles generate a large amount of data,which makes privacy and security a major challenge to their success.The complicated machine-led mechanics of connected and autonomous vehicles increase the risks of privacy invasion and cyber security violations for their users by making them more susceptible to data exploitation and vulnerable to cyber-attacks than any of their predecessors.This could have a negative impact on how well-liked CAVs are with the general public,give them a poor name at this early stage of their development,put obstacles in the way of their adoption and expanded use,and complicate the economic models for their future operations.On the other hand,congestion is still a bottleneck for traffic management and planning.This research paper presents a blockchain-based framework that protects the privacy of vehicle owners and provides data security by storing vehicular data on the blockchain,which will be used further for congestion detection and mitigation.Numerous devices placed along the road are used to communicate with passing cars and collect their data.The collected data will be compiled periodically to find the average travel time of vehicles and traffic density on a particular road segment.Furthermore,this data will be stored in the memory pool,where other devices will also store their data.After a predetermined amount of time,the memory pool will be mined,and data will be uploaded to the blockchain in the form of blocks that will be used to store traffic statistics.The information is then used in two different ways.First,the blockchain’s final block will provide real-time traffic data,triggering an intelligent traffic signal system to reduce congestion.Secondly,the data stored on the blockchain will provide historical,statistical data that can facilitate the analysis of traffic conditions according to past behavior.

A Framework for Securing Saudi Arabian Hospital Industry: Vision-2030 Perspective

Recent transformation of Saudi Arabian healthcare sector into a reven-ue producing one has signaled several advancements in healthcare in the country.Transforming healthcare management into Smart hospital systems is one of them.Secure hospital management systems which are breach-proof only can be termed as effective smart hospital systems.Given the perspective of Saudi Vision-2030,many practitioners are trying to achieve a cost-effective hospital management sys-tem by using smart ideas.In this row,the proposed framework posits the main objectives for creating smart hospital management systems that can only be acknowledged by managing the security of healthcare data and medical practices.Further,the proposed framework will also be helpful in gaining satisfactory rev-enue from the healthcare sector by reducing the cost and time involved in mana-ging the smart hospital system.The framework is based on a hybrid approach of three key methods which include:employing the Internet of Medical Things(IoMT)and blockchain methodologies for maintaining the security and privacy of healthcare data and medical practices,and using big data analytics methodol-ogy for raising the funds and revenue by managing the bulk volume of healthcare data.Moreover,the framework will also be helpful for both the patients and the doctors,thus enabling the Kingdom of Saudi Arabia(KSA)to meet its goals of Vision-2030 by ensuring low cost,yet credible,healthcare services.

Integrating Blockchain Technology into Healthcare Through an Intelligent Computing Technique

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.

Usability Evaluation Through Fuzzy AHP-TOPSIS Approach: Security Requirement Perspective

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.A significant factor in the management 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.

Hesitant Fuzzy-Sets Based Decision-Making Model for Security Risk Assessment

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.

Estimating the Impact of COVID-19 Pandemic on the Research Community in the Kingdom of Saudi Arabia

Ever since its outbreak inWuhan,COVID-19 has cloaked the entireworld in a pall of despondency and uncertainty.The present study describes the exploratory analysis of all COVID cases in Saudi Arabia.Besides,the study has executed the forecastingmodel for predicting the possible number of COVID-19 cases in Saudi Arabia till a defined period.Towards this intent,the study analyzed different age groups of patients(child,adult,elderly)who were affected by COVID-19.The analysis was done city-wise and also included the number of recoveries recorded in different cities.Furthermore,the study also discusses the impact of COVID-19 on the economy.For conducting the stated analysis,the authors have created a list of factors that are known to cause the spread of COVID-19.As an effective countermeasure to contain the spread of Coronavirus in Saudi Arabia,this study also proposes to identify the most effective Computer Science technique that can be used by healthcare professionals.For this,the study employs the Fuzzy-Analytic Hierarchy Process integrated with the Technique for Order Performance by Similar to Ideal Solution(F.AHP.TOPSIS).After prioritizing the various Computer Science techniques,the ranking order that was obtained for the different techniques/tools to contain COVID-19 was:A4>A1>A2>A5>A3.Since the Blockchain technique obtained the highest priority,the study recommends that it must be used extensively as an efficacious and accurate means to combat COVID-19.

Impact Assessment of COVID-19 Pandemic Through Machine Learning Models

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 form the 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.

Cyber Security Analysis and Evaluation for Intrusion Detection Systems

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 cybersecurityrelated attributes for intrusion detection systems,allowing them to design more optimal and effective intrusion detection systems.

An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

The adoption of sustainable electronic healthcare infrastructure has revolutionized healthcare services and ensured that E-health technology caters efficiently and promptly to the needs of the stakeholders associated with healthcare.Despite the phenomenal advancement in the present healthcare services,the major obstacle that mars the success of E-health is the issue of ensuring the confidentiality and privacy of the patients’data.A thorough scan of several research studies reveals that healthcare data continues to be the most sought after entity by cyber invaders.Various approaches and methods have been practiced by researchers to secure healthcare digital services.However,there are very few from the Machine learning(ML)domain even though the technique has the proactive ability to detect suspicious accesses against Electronic Health Records(EHRs).The main aim of this work is to conduct a systematic analysis of the existing research studies that address healthcare data confidentiality issues through ML approaches.B.A.Kitchenham guidelines have been practiced as a manual to conduct this work.Seven well-known digital libraries namely IEEE Xplore,Science Direct,Springer Link,ACM Digital Library,Willey Online Library,PubMed(Medical and Bio-Science),and MDPI have been included to performan exhaustive search for the existing pertinent studies.Results of this study depict that machine learning provides a more robust security mechanism for sustainable management of the EHR systems in a proactive fashion,yet the specified area has not been fully explored by the researchers.K-nearest neighbor algorithm and KNIEM implementation tools are mostly used to conduct experiments on EHR systems’log data.Accuracy and performance measure of practiced techniques are not sufficiently outlined in the primary studies.This research endeavour depicts that there is a need to analyze the dynamic digital healthcare environment more comprehensively.Greater accuracy and effective implementation of ML-based models are the need of the day for ensuring the confidentiality of EHRs in a proactive fashion.

Blockchain and Artificial Intelligence Applications to Defeat COVID-19 Pandemic

The rapid emergence of novel virus named SARS-CoV2 and unchecked dissemination of this virus around the world ever since its outbreak in 2020,provide critical research criteria to assess the vulnerabilities of our current health system.The paper addresses our preparedness for the management of such acute health emergencies and the need to enhance awareness,about public health and healthcare mechanisms.In view of this unprecedented health crisis,distributed ledger and AI technology can be seen as one of the promising alternatives for fighting against such epidemics at the early stages,and with the higher efficacy.At the implementation level,blockchain integration,early detection and avoidance of an outbreak,identity protection and safety,and a secure drug supply chain can be realized.At the opposite end of the continuum,artificial intelligence methods are used to detect corona effects until they become too serious,avoiding costly drug processing.The paper explores the application of blockchain and artificial intelligence in order to fight with COVID-19 epidemic scenarios.This paper analyzes all possible newly emerging cases that are employing these two technologies for combating a pandemic like COVID-19 along with major challenges which cover all technological and motivational factors.This paper has also discusses the potential challenges and whether further production is required to establish a health monitoring system.

A Unified Decision-Making Technique for Analysing Treatments in Pandemic Context

The COVID-19 pandemic has triggered a global humanitarian disaster that has never been seen before.Medical experts,on the other hand,are undecided on the most valuable treatments of therapy because people ill with this infection exhibit a wide range of illness indications at different phases of infection.Further,this project aims to undertake an experimental investigation to determine which treatments for COVID-19 disease is the most effective and preferable.The research analysis is based on vast data gathered from professionals and research journals,making this study a comprehensive reference.To solve this challenging task,the researchers used the HF AHPTOPSIS Methodology,which is a well-known and highly effective MultiCriteria Decision Making(MCDM)technique.The technique assesses the many treatment options identified through various research papers and guidelines proposed by various countries,based on the recommendations of medical practitioners and professionals.The review process begins with a ranking of different treatments based on their effectiveness using the HF-AHP approach and then evaluates the results in five different hospitals chosen by the authors as alternatives.We also perform robustness analysis to validate the conclusions of our analysis.As a result,we obtained highly corroborative results that can be used as a reference.The results suggest that convalescent plasma has the greatest rank and priority in terms of effectiveness and demand,implying that convalescent plasma is the most effective treatment for SARS-CoV-2 in our opinion.Peepli also has the lowest priority in the estimation.

Evaluating the Impact of Software Security Tactics: A Design Perspective

Design architecture is the edifice that strengthens the functionalities as well as the security of web applications.In order to facilitate architectural security from the web application’s design phase itself,practitioners are now adopting the novel mechanism of security tactics.With the intent to conduct a research from the perspective of security tactics,the present study employs a hybrid multi-criteria decision-making approach named fuzzy analytic hierarchy process-technique for order preference by similarity ideal solution(AHP-TOPSIS)method for selecting and assessing multi-criteria decisions.The adopted methodology is a blend of fuzzy analytic hierarchy process(fuzzy AHP)and fuzzy technique for order preference by similarity ideal solution(fuzzy TOPSIS).To establish the efficacy of this methodology,the results are obtained after the evaluation have been tested on fifteen different web application projects(Online Quiz competition,Entrance Test,and others)of the Babasaheb Bhimrao Ambedkar University,Lucknow,India.The tabulated outcomes demonstrate that the methodology of the Multi-Level Fuzzy Hybrid system is highly effective in providing accurate estimation for strengthening the security of web applications.The proposed study will help experts and developers in developing and managing security from any web application design phase for better accuracy and higher security.

Sustainable-Security Assessment Through a Multi Perspective Benchmarking Framework

The current cyber-attack environment has put even the most protected systems at risk as the hackers are nowmodifying 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 employedmultiple 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 sustainablesecurity of health information software systems.

A Link Analysis Algorithm for Identification of Key Hidden Services

The Tor dark web network has been reported to provide a breeding ground for criminals and fraudsters who are exploiting the vulnerabilities in the network to carry out illicit and unethical activities.The network has unfortunately become a means to perpetuate crimes like illegal drugs and firearm trafficking,violence and terrorist activities among others.The government and law enforcement agencies are working relentlessly to control the misuse of Tor network.This is a study in the similar league,with an attempt to suggest a link-based ranking technique to rank and identify the influential hidden services in the Tor dark web.The proposed method considers the extent of connectivity to the surface web services and values of the centrality metrics of a hidden service in the web graph for ranking.The modified PageRank algorithm is used to obtain the overall rankings of the hidden services in the dataset.Several graph metrics were used to evaluate the effectiveness of the proposed technique with other commonly known ranking procedures in literature.The proposed ranking technique is shown to produce good results in identifying the influential domains in the tor network.

An Ensemble Approach to Identify Firearm Listing on Tor Hidden-Services

The ubiquitous nature of the internet has made it easier for criminals to carry out illegal activities online.The sale of illegal firearms and weaponry on dark web cryptomarkets is one such example of it.To aid the law enforcement agencies in curbing the illicit trade of firearms on cryptomarkets,this paper has proposed an automated technique employing ensemble machine learning models to detect the firearms listings on cryptomarkets.In this work,we have used partof-speech(PoS)tagged features in conjunction with n-gram models to construct the feature set for the ensemble model.We studied the effectiveness of the proposed features in the performance of the classification model and the relative change in the dimensionality of the feature set.The experiments and evaluations are performed on the data belonging to the three popular cryptomarkets on the Tor dark web from a publicly available dataset.The prediction of the classification model can be utilized to identify the key vendors in the ecosystem of the illegal trade of firearms.This information can then be used by law enforcement agencies to bust firearm trafficking on the dark web.

A Usability Management Framework for Securing Healthcare Information System

Transformation from conventional business management systems tosmart digital systems is a recurrent trend in the current era. This has led to digitalrevolution, and in this context, the hardwired technologies in the software industry play a significant role However, from the beginning, software security remainsa serious issue for all levels of stakeholders. Software vulnerabilities lead to intrusions that cause data breaches and result in disclosure of sensitive data, compromising the organizations’ reputation that translates into, financial losses andcompromising software usability as well. Most of the data breaches are financiallymotivated, especially in the healthcare sector. The cyber invaders continuouslypenetrate the E- Health data because of the high cost of the data on the darkweb. Therefore, security assessment of healthcare web-based applicationsdemands immediate intervention mechanisms to weed out the threats of cyberattacks for the sake of software usability. The proposed disclosure is a unique process of three phases that are combined by researchers in order to produce andmanage usability management framework for healthcare information system. Inthis most threatened time of digital era where, Healthcare data industry has bornethe brunt of the highest number of data breach episodes in the last few years. Thekey reason for this is attributed to the sensitivity of healthcare data and the highcosts entailed in trading the data over the dark web. Hence, usability managementof healthcare information systems is the need of hour as to identify the vulnerabilities and provide preventive measures as a shield against the breaches. The proposed unique developed model of usability management workflow is preparedby associating steps like learn;analyze and manage. All these steps gives an allin one package for the healthcare information management industry because thereis no systematic model available which associate identification to implementationsteps with different evaluation steps.

Evaluating the Impacts of Security-Durability Characteristic:Data Science Perspective

Since the beginning of web applications,security has been a critical study area.There has been a lot of research done to figure out how to define and identify security goals or issues.However,high-security web apps have been found to be less durable in recent years;thus reducing their business continuity.High security features of a web application are worthless unless they provide effective services to the user and meet the standards of commercial viability.Hence,there is a necessity to link in the gap between durability and security of the web application.Indeed,security mechanisms must be used to enhance durability as well as the security of the web application.Although durability and security are not related directly,some of their factors influence each other indirectly.Characteristics play an important role in reducing the void between durability and security.In this respect,the present study identifies key characteristics of security and durability that affect each other indirectly and directly,including confidentiality,integrity availability,human trust and trustworthiness.The importance of all the attributes in terms of their weight is essential for their influence on the whole security during the development procedure of web application.To estimate the efficacy of present study,authors employed the Hesitant Fuzzy Analytic Hierarchy Process(H-Fuzzy AHP).The outcomes of our investigations and conclusions will be a useful reference for the web application developers in achieving a more secure and durable web application.

Prediction of COVID-19 Pandemic Spread in Kingdom of Saudi Arabia

A significant increase in the number of coronavirus cases can easily be noticed in most of the countries around the world.Inspite of the consistent preventive initiatives being taken to contain the spread of this virus,the unabated increase in the cases is both alarming and intriguing.The role of mathematical models in predicting and estimating the spread of the virus,and identifying various preventive factors dependencies has been found important and effective in most of the previous pandemics like Severe Acute Respiratory Syndrome(SARS)2003.In this research work,authors have proposed the Susceptible-Infectected-Removed(SIR)model variation in order to forecast the pattern of coronavirus disease(COVID-19)spread for the upcoming eight weeks in perspective of Saudi Arabia.The study has been performed by using SIR model with a proposed simplification using average progression for further estimation ofβandγvalues for better curve fittings ratios.The predictive results of this study clearly show that under the current public health interventions,there will be an increase in the COVID-19 cases in Saudi Arabia in the next four weeks.Hence,a set of strong health primitives and precautionary measures are recommended in order to avoid and prevent the further spread of COVID-19 in Saudi Arabia.

Healthcare Device Security Assessment through Computational Methodology

The current study discusses the different methods used to secure healthcare devices and proposes a quantitative framework to list them in order of significances.The study uses the Hesitant Fuzzy(HF),Analytic Hierarchy Process(AHP)integrated with Fuzzy Technical for Order Preference by Similarities to Ideal Solution(TOPSIS)to classify the best alternatives to security techniques for healthcare devices to securing the devices.The technique is enlisted to rate the alternatives based on the degree of satisfaction of their weights.The ranks of the alternatives consequently decide the order of priority for the techniques.A1 was the most probable alternative of all the alternatives,according to the ranks of the alternatives acquired.This means that the security of A2 healthcare devices is the greatest of all the alternatives picked.A corroborative guide for the developers and the makers in quantitatively determining the security of healthcare devices to engineer efficacious devices will be the findings drawn up with the assistance of the proposed framework.The assessments performed using the proposed framework are systematic,precise,and definitive.Therefore,the results of the present empirical analysis are a stronger and accurate choice than the manual assessment of the device’s security.