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Identity Governance Framework for Privileged Users

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computer Systems Science & engineering 2022年第3期40卷 995-1005页

作者： Mansour Hammoud Alruwies Shailendra Mishra Mohammed Abdul Rahman AlShehri Department of information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of computer engineering College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia

information technology companies have grown in size and recognized the need to protect their valuable assets.As a result,each IT application has its authentication mechanism,and an employee needs a username and password.As the number of applications increased,as a result,it became increasingly complex to manage all identities like the number of usernames and passwords of an employee.All identities had to be retrieved by users.Both the identities and the access rights associated with those identities had to be protected by an administrator.Management couldn’t even capture such access rights because they couldn’t verify things like privacy and security.Identity management can help solve this problem.The concept behind identity management is to centralize identity management and manage access identity centrally rather than multiple applications with their authentication and authorization mechanisms.In this research work,we develop governance and an identity management framework for information and technology infrastructures with privileged access management,consisting of cybersecurity policies and strategies.The results show the efficiency of the framework compared to the existing information security components.The integrated identity and access management and privileged access management enable organizations to respond to incidents and facilitate compliance.It can automate use cases that manage privileged accounts in the real world.

关键词： Privileged access management(PAM) lightweight directory access protocol(LDAP) identity management access control cyber-attack

Web Security:Emerging Threats and Defense

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computer Systems Science & engineering 2022年第3期40卷 1233-1248页

作者： Abdulwahed Awad Almutairi Shailendra Mishra Mohammed AlShehri Department of information Technology College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia Department of computer engineering College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia

Web applications have become a widely accepted method to support the internet for the past decade.Since they have been successfully installed in the business activities and there is a requirement of advanced functionalities,the configuration is growing and becoming more complicated.The growing demand and complexity also make these web applications a preferred target for intruders on the internet.Even with the support of security specialists,they remain highly problematic for the complexity of penetration and code reviewing methods.It requires considering different testing patterns in both codes reviewing and penetration testing.As a result,the number of hacked websites is increasing day by day.Most of these vulnerabilities also occur due to incorrect input validation and lack of result validation for lousy programming practices or coding errors.Vulnerability scanners for web applications can detect a few vulnerabilities in a dynamic approach.These are quite easy to use;however,these often miss out on some of the unique critical vulnerabilities in a different and static approach.Although these are time-consuming,they can find complex vulnerabilities and improve developer knowledge in coding and best practices.Many scanners choose both dynamic and static approaches,and the developers can select them based on their requirements and conditions.This research explores and provides details of SQL injection,operating system command injection,path traversal,and cross-site scripting vulnerabilities through dynamic and static approaches.It also examines various security measures in web applications and selected five tools based on their features for scanning PHP,and JAVA code focuses on SQL injection,cross-site scripting,Path Traversal,operating system command.Moreover,this research discusses the approach of a cyber-security tester or a security developer finding out vulnerabilities through dynamic and static approaches using manual and automated web vulnerability scanners.

关键词： SQL injection attack cross-site scripting attack command injection attack path traversal attack

Impact of Human Vulnerabilities on Cybersecurity

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computer Systems Science & engineering 2022年第3期40卷 1153-1166页

作者： Maher Alsharif Shailendra Mishra Mohammed AlShehri Department of information Technology College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia Department of computer engineering College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia

Today,security is a major challenge linked with computer network companies that cannot defend against cyber-attacks.Numerous vulnerable factors increase security risks and cyber-attacks,including viruses,the internet,communications,and hackers.Internets of Things(IoT)devices are more effective,and the number of devices connected to the internet is constantly increasing,and governments and businesses are also using these technologies to perform business activities effectively.However,the increasing uses of technologies also increase risks,such as password attacks,social engineering,and phishing attacks.Humans play a major role in the field of cybersecurity.It is observed that more than 39%of security risks are related to the human factor,and 95%of successful cyber-attacks are caused by human error,with most of them being insider threats.The major human factor issue in cybersecurity is a lack of user awareness of cyber threats.This study focuses on the human factor by surveying the vulnerabilities and reducing the risk by focusing on human nature and reacting to different situations.This study highlighted that most of the participants are not experienced with cybersecurity threats and how to protect their personal information.Moreover,the lack of awareness of the top three vulnerabilities related to the human factor in cybersecurity,such as phishing attacks,passwords,attacks,and social engineering,are major problems that need to be addressed and reduced through proper awareness and training.

关键词： Cybersecurity phishing attack password attack social engineering cybersecurity awareness security risk

Dynamic Multi-Task Allocation Method for Passenger Diffusion in Mobile Crowd Sensing

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Chinese Journal of Electronics 2021年第5期30卷 940-946页

作者： JIANG Weijin LYU Sijian college of computer and information engineering Hunan University of Technology and Business

This paper aims to solve the problem of low efficiency, high cost and instability in opportunistic network transmission in the process of mobile group intelligence perception task allocation. Two multi-task dynamic distribution methods based on Lowest cost Participant selection algorithm(LC-PSA) based on user incentive cost and Least number Participant selection algorithm(LN-PSA) based on number of users are proposed respectively. Through these two algorithms,the goal of minimizing the number of people and moving distance required for the task and reducing the system’s incentive cost is achieved. Simulation experiments show that compared with similar algorithms, the number of participants in the task distribution scheme selected by the LN-PSA algorithm is reduced by 24.0%, and the system resource consumption is lower, which can provide stable services for the system when users are insufficient in emergencies. Compared with the traditional greedy heuristics algorithm, the LC-PSA algorithm reduces the total system cost by 37.74% and has better overall performance in the comparison experiment.

关键词： Mobile radio systems Optimisation techniques Mobile, ubiquitous and pervasive computing Mobile crowd sensing Multitask allocation Subway passengers Participant selection Internet of things

Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile AdHocNetworks(MANETS)

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computers, Materials & Continua 2024年第5期79卷 1903-1923页

作者： Ahmed Alhussen Arshiya SAnsari Department of computer engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

关键词： Mobile AdHocNetworks(MANET) urban traffic prediction artificial intelligence(AI) traffic congestion chaotic spatial fuzzy polynomial neural network(CSFPNN)

Automatic detection of sow estrus using a lightweight real-time detector and thermal images

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International Journal of Agricultural and Biological engineering 2023年第3期16卷 194-207页

作者： Haibo Zheng Hang Zhang Shuang Song Yue Wang Tonghai Liu college of computer and information engineering Tianjin Agricultural UniversityTianjin 300392China

Determination of ovulation time is one of the most important tasks in sow reproduction management.Temperature variation in the vulva of the sows can be used as a predictor of ovulation time.However,the skin temperatures of sows in existing studies are obtained manually from infrared thermal images,posing an obstacle to the automatic prediction of ovulation time.In this study,an improved YOLO-V5s detector based on feature fusion and dilated convolution(FDYOLOV5s)was proposed for the automatic extraction of the vulva temperature of sows based on infrared thermal images.For the purpose of reducing the model complexity,the depthwise separable convolution and the modified lightweight ShuffleNet-V2 module were introduced in the backbone.Meanwhile,the feature fusion network structure of the model was simplified for efficiency,and a mixed dilated convolutional module was designed to obtain global features.The experimental results show that FD-YOLOV5s outperformed the other nine methods,with a mean average precision(mAP)of 99.1%,an average frame rate of 156.25 fps,and a model size of only 3.86 MB,indicating that the method effectively simplifies the model while ensuring detection accuracy.Using a linear regression between manual extraction and the results extracted using this method in randomly selected thermal images,the coefficients of determination for maximum and average vulvar temperatures reached 99.5%and 99.3%,respectively.The continuous vulva temperature of sows was obtained by the target detection algorithm,and the sow estrus detection was performed by the temperature trend and compared with the manually detected estrus results.The results showed that the sensitivity,specificity,and error rate of the estrus detection algorithm were 89.3%,94.5%,and 5.8%,respectively.The method achieves real-time and accurate extraction of sow vulva temperature and can be used for the automatic detection of sow estrus,which could be helpful for the automatic prediction of ovulation time.

关键词： automatic estrus detection thermal images real-time detector vulva temperature mixed dilated convolutional

Underwater Sea Cucumber Target Detection Based on Edge-Enhanced Scaling YOLOv4

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Journal of Beijing Institute of Technology 2023年第3期32卷 328-340页

作者： Ziting Zhang Hang Zhang Yue Wang Tonghai Liu Yuxiang He Yunchen Tian college of computer and information engineering Tianjin Agricultural UniversityTianjin 300380China

Sea cucumber detection is widely recognized as the key to automatic culture.The underwater light environment is complex and easily obscured by mud,sand,reefs,and other underwater organisms.To date,research on sea cucumber detection has mostly concentrated on the distinction between prospective objects and the background.However,the key to proper distinction is the effective extraction of sea cucumber feature information.In this study,the edge-enhanced scaling You Only Look Once-v4(YOLOv4)(ESYv4)was proposed for sea cucumber detection.By emphasizing the target features in a way that reduced the impact of different hues and brightness values underwater on the misjudgment of sea cucumbers,a bidirectional cascade network(BDCN)was used to extract the overall edge greyscale image in the image and add up the original RGB image as the detected input.Meanwhile,the YOLOv4 model for backbone detection is scaled,and the number of parameters is reduced to 48%of the original number of parameters.Validation results of 783images indicated that the detection precision of positive sea cucumber samples reached 0.941.This improvement reflects that the algorithm is more effective to improve the edge feature information of the target.It thus contributes to the automatic multi-objective detection of underwater sea cucumbers.

关键词： sea cucumber edge extraction feature enhancement edge-enhanced scaling You Only Look Once-v4(YOLOv4)(ESYv4) model scaling

A Novel Energy and Communication Aware Scheduling on Green Cloud Computing

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computers, Materials & Continua 2023年第12期77卷 2791-2811页

作者： Laila Almutairi Shabnam Mohamed Aslam Department of computer engineering Computer Science and Information Technology CollegeMajmaah UniversityAl Majmaah11952Saudi Arabia Department of information Technology Computer Science and Information Technology CollegeMajmaah UniversityAl Majmaah11952Saudi Arabia

The rapid growth of service-oriented and cloud computing has created large-scale data centres worldwide.Modern data centres’operating costs mostly come from back-end cloud infrastructure and energy consumption.In cloud computing,extensive communication resources are required.Moreover,cloud applications require more bandwidth to transfer large amounts of data to satisfy end-user requirements.It is also essential that no communication source can cause congestion or bag loss owing to unnecessary switching buffers.This paper proposes a novel Energy and Communication(EC)aware scheduling(EC-scheduler)algorithm for green cloud computing,which optimizes data centre energy consumption and traffic load.The primary goal of the proposed EC-scheduler is to assign user applications to cloud data centre resources with minimal utilization of data centres.We first introduce a Multi-Objective Leader Salp Swarm(MLSS)algorithm for task sorting,which ensures traffic load balancing,and then an Emotional Artificial Neural Network(EANN)for efficient resource allocation.EC-scheduler schedules cloud user requirements to the cloud server by optimizing both energy and communication delay,which supports the lower emission of carbon dioxide by the cloud server system,enabling a green,unalloyed environment.We tested the proposed plan and existing cloud scheduling methods using the GreenCloud simulator to analyze the efficiency of optimizing data centre energy and other scheduler metrics.The EC-scheduler parameters Power Usage Effectiveness(PUE),Data Centre Energy Productivity(DCEP),Throughput,Average Execution Time(AET),Energy Consumption,and Makespan showed up to 26.738%,37.59%,50%,4.34%,34.2%,and 33.54%higher efficiency,respectively,than existing state of the art schedulers concerning number of user applications and number of user requests.

关键词： EC-scheduler green cloud energy efficiency task scheduling task sorting resource allocation

Prediction Model for Coronavirus Pandemic Using Deep Learning

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computer Systems Science & engineering 2022年第3期40卷 947-961页

作者： Mamoona Humayun Ahmed Alsayat Department of information Systems College of Computer and Information SciencesJouf UniversityKSA Department of computer Science College of Computer and Information SciencesJouf UniversityKSA

The recent global outbreak of COVID-19 damaged the world health systems,human health,economy,and daily life badly.None of the countries was ready to face this emerging health challenge.Health professionals were not able to predict its rise and next move,as well as the future curve and impact on lives in case of a similar pandemic situation happened.This created huge chaos globally,for longer and the world is still struggling to come up with any suitable solution.Here the better use of advanced technologies,such as artificial intelligence and deep learning,may aid healthcare practitioners in making reliable COVID-19 diagnoses.The proposed research would provide a prediction model that would use Artificial Intelligence and Deep Learning to improve the diagnostic process by reducing unreliable diagnostic interpretation of chest CT scans and allowing clinicians to accurately discriminate between patients who are sick with COVID-19 or pneumonia,and also empowering health professionals to distinguish chest CT scans of healthy people.The efforts done by the Saudi government for the management and control of COVID-19 are remarkable,however;there is a need to improve the diagnostics process for better perception.We used a data set from Saudi regions to build a prediction model that can help distinguish between COVID-19 cases and regular cases from CT scans.The proposed methodology was compared to current models and found to be more accurate(93 percent)than the existing methods.

关键词： Artificial Intelligence(AI) deep learning(DL) COVID-19 pandemic