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Hybrid malware Variant Detection model with Extreme Gradient Boosting and Artificial Neural Network Classifiers

Computers, materials & Continua 2023年第9期76卷 3483-3498页

作者： Asma A.Alhashmi Abdulbasit A.Darem Sultan m.Alanazi abdullah m.alashjaee Bader Aldughayfiq Fuad A.Ghaleb Shouki A.Ebad majed A.Alanazi Department of Computer Science Northern Border UniversityArar9280Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Information Systems College of Computer Science and InformationJouf UniversitySakakaAljoufSaudi Arabia School of Computing University Teknologi Malaysia81310 UTM Johor BahruJohor81310Malaysia Department of Computer and Electronic Engineering Sana’a Community CollegeSana’a5695Yemen

In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining organizations.This paper presents an innovative Application Programmable Interface(API)-based hybrid model designed to enhance the detection performance of malware variants.This model integrates eXtreme Gradient Boosting(XGBoost)and an Artificial Neural Network(ANN)classifier,offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware authors.The model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features,providing a holistic and comprehensive view of malware behavior.From these features,we construct two XGBoost predictors,each of which contributes a valuable perspective on the malicious activities under scrutiny.The outputs of these predictors,interpreted as malicious scores,are then fed into an ANN-based classifier,which processes this data to derive a final decision.The strength of the proposed model lies in its capacity to leverage behavioral and signature-based features,and most importantly,in its ability to extract and analyze the hidden relations between these two types of features.The efficacy of our proposed APIbased hybrid model is evident in its performance metrics.It outperformed other models in our tests,achieving an impressive accuracy of 95%and an F-measure of 93%.This significantly improved the detection performance of malware variants,underscoring the value and potential of our approach in the challenging field of cybersecurity.

关键词： API-based hybrid malware detection model static and dynamic analysis malware detection

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Leveraging User-Generated Comments and Fused BiLSTm models to Detect and Predict Issues with mobile Apps

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Computers, materials & Continua 2024年第4期79卷 735-759页

作者： Wael m.S.Yafooz abdullah Alsaeedi Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMedina42353Saudi Arabia

In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user comments.However, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-Termmemory (LSTm), and Convolutional Neural Network Long Short-Term memory (CNN-LSTm) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.

关键词： mobile apps issues play store user comments deep learning LSTm bidirectional LSTm

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Discoid meniscus:Treatment considerations and updates

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World Journal of Orthopedics 2024年第6期15卷 520-528页

作者： Waleed Albishi Amjad Albaroudi Abdulrahman m Alaseem Sarah Aljasser Ibrahim Alshaygy abdullah Addar Department of Orthopedic Surgery College of MedicineKing Saud UniversityRiyadh 11362Saudi Arabia

A discoid meniscus is a morphological abnormality wherein the meniscus loses its normal‘C’shape.Although most patients are asymptomatic,patients might still present with symptoms such as locking,pain,swelling,or giving way.magnetic resonance imaging is usually needed for confirmation of diagnosis.Based on a constellation of factors,including clinical and radiological,different approaches are chosen for the management of discoid meniscus.The purpose of this review is to outline the treatment of discoid meniscus,starting from conservative approach,to the different surgical options for this condition.The Pubmed and Google Scholar databases were used for this review.Studies discussing the treatment of discoid meniscus from 2018 to 2023 were searched.Initially there were 369 studies retrieved,and after removal of studies using the exclusion criteria,26 studies were included in this review.Factors such as stability,presence of tear,and morphology can help with surgical planning.many approaches have been used to treat discoid meniscus,where the choice is tailored for each patient individually.Postoperatively,factors that may positively impact patient outcomes include male sex,body mass indexmptom onsetmptomsmonths.The conventional approach is partial meniscectomy with or without repair;however,recently,there has been an increased emphasis on discoid-preserving techniques such as meniscoplasty,meniscopexy,and meniscal allograft transplantation.

关键词： Discoid meniscus Saucerization Partial meniscectomy meniscoplasty meniscopexy meniscal allograft transplantation Arthroscopy Knee

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Influence of cerium ions on structure-dependent magnetic properties of Ba-Sr m-type hexaferrite nanocrystals

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Journal of Rare Earths 2024年第2期42卷 364-372,I0005页

作者： Ram A.Pawar Sunil m.Patange P.m.Dighe Sher Singh meena abdullah GAl-Sehemi mehboobali Pannipara Santosh S.Jadhav Department of Physics ArtsCommerce and Science CollegeAhmednagar413711MaharashtraIndia Department of Physics Shri Krishna MahavidyalayaGunjotiDistOsmanabad413606MaharashtraIndia Padmashri Vikhe Patil College of Arts Science and CommercePravaranagarA/P.Loni(Kd)Tal.RahataDistAhmednagar413713MaharashtraIndia Solid State Physics Division Bhabha Atomic Research CentreMumbai400085MaharashtraIndia Department of Chemistry King Khalid UniversityAbha61413Saudi Arabia Department of Physics D.S.M's ArtsCommerce and Science CollegeJintur431509MaharashtraIndia

The present study is a systematic effort to investigate the structure-sensitive magnetic parameters of Ce^(3+) substituted Ba-Sr hexaferrite nanocrystals chemically formulated as Ba_(0.5)Sr_(0.5)Ce_(x)Fe_(12-x)O_(19) where x=0.0-0.2 with Δx=0.05.The hexaferrite powders were prepared using the sol-gel self-ignition route and structurally characterized by means of powder X-ray diffraction and Fourier transform infrared spectroscopy.The creation of the m-type hexaferrite phase within the synthesized samples was revealed from the Rietveld refinement of the X-ray diffractograms.The occurrence of a secondary phase of CeO_(2) was revealed within the hexaferrites for the substitution,x> 0.The refined X-ray diffraction data were utilized to compute the lattice parameters,X-ray density,and lattice parameter ratio.The crystal structure plotted from the refined XRD data reveals the occupancy of the ions at different lattice sites.The XPS data of the hexaferrite were analyzed to confirm the oxidation states of the constituent elements.The nanocrystalline nature of the hexaferrites was revealed from the crystallite sizes calculated using Scherer's formula.The analysis of FTIR spectra confirms that only a fraction of Ce^(3+)accommodated in the lattice and the remaining Ce content reside in the form of the CeO_(2) phase.The morphology of the hexaferrites was analyzed from the FESEm profiles of the ferrite samples.The magnetic behavior study was performed by analyzing the Curie temperature,hysteresis loops,and hyperfine interactions by means of susceptibility,V.S.m,and mossbauer spectroscopy,respectively.The hexaferrites with increasing coercivity,decreasing saturation magnetization,and decreasing Curie temperature are reported in the study.The substituted Ce^(3+)ions inhibit the grain growth and create lattice imperfections giving rise to hexaferrites with tuned magnetic parameters suitable for different applications.

关键词： Ba-Sr hexaferrite Ce^(3+)substitution XPS Saturation magnetization Coercivity m?ssbauer spectroscopy

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Intelligent Solution System for Cloud Security Based on Equity Distribution:model and Algorithms

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Computers, materials & Continua 2024年第1期78卷 1461-1479页

作者： Sarah mustafa Eljack mahdi Jemmali mohsen Denden mutasim Al Sadig abdullah m.Algashami Sadok Turki Department of Computer Science and Information College of Science at ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Computing and InformaticsUniversity of SharjahSharjahUnited Arab Emirates Higher Institute of Computer Science and mathematics University of MonastirMonastir5000Tunisia mars Laboratory University of SousseSousseTunisia Department of Logistic and maintenance UFR MIM at MetzUniversity of LorraineMetzFrance Department of Computer and Information Technologies College of Telecommunicationand Information Riyadh CTITechnical and Vocational Training Corporation TVTCRiyadh12464Saudi Arabia Department of Computer Science Higher Institute of Applied Sciences of SousseSousse UniversitySousse4000Tunisia

In the cloud environment,ensuring a high level of data security is in high demand.Data planning storage optimization is part of the whole security process in the cloud environment.It enables data security by avoiding the risk of data loss and data overlapping.The development of data flow scheduling approaches in the cloud environment taking security parameters into account is insufficient.In our work,we propose a data scheduling model for the cloud environment.Themodel is made up of three parts that together help dispatch user data flow to the appropriate cloudVms.The first component is the Collector Agent whichmust periodically collect information on the state of the network links.The second one is the monitoring agent which must then analyze,classify,and make a decision on the state of the link and finally transmit this information to the scheduler.The third one is the scheduler who must consider previous information to transfer user data,including fair distribution and reliable paths.It should be noted that each part of the proposedmodel requires the development of its algorithms.In this article,we are interested in the development of data transfer algorithms,including fairness distribution with the consideration of a stable link state.These algorithms are based on the grouping of transmitted files and the iterative method.The proposed algorithms showthe performances to obtain an approximate solution to the studied problem which is an NP-hard(Non-Polynomial solution)problem.The experimental results show that the best algorithm is the half-grouped minimum excluding(HmE),with a percentage of 91.3%,an average deviation of 0.042,and an execution time of 0.001 s.

关键词： Cyber-security cloud computing cloud security algorithms heuristics

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DNBP-CCA:A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

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Computers, materials & Continua 2024年第5期79卷 2851-2878页

作者： Abdulwadood Alawadhi mohd.Hasbullah Omar abdullah Almogahed Noradila Nordin Salman A.Alqahtani Atif m.Alamri InterNetWorks Research Laboratory School of ComputingUniversiti Utara MalaysiaSintok06010Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaParit RajaJohor86400Malaysia School of Games&Creative Technology University for the Creative ArtsFarnhamGU97DSUK Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The increased adoption of Internet of medical Things (IomT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

关键词： Internet of medical Things body area networks backoff period tsk fuzzy model clear channel assessment media access control

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Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

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Computer modeling in Engineering & Sciences 2024年第8期140卷 1539-1562页

作者： Samar m.Alqhtani Toufique A.Soomro Faisal Bin Ubaid Ahmed Ali muhammad Irfan abdullah A.Asiri Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Electronic Engineering The University of LarkanoLarkanaSindh75660Pakistan Computer Science Department Sukkur IBA UniversitySukkurSindh65200Pakistan Eletrical Engineering Department Sukkur IBA UniversitySukkurSindh65200Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries.magnetic resonance imaging(mRI)and computed tomography(CT)are utilized to capture brain images.mRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders.Typically,manual assessment of mRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention.However,early diagnosis of brain tumors is intricate,necessitating the use of computerized methods.This research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain tumors.The proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-means(FCm)clustering technique in the third step.The final step involves classification using the Support Vector machine(SVm)classifier.The classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced magnetic Resonance Imaging(CE-mRI)database.The proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of 0.957.Additionally,this method exhibits a shorter processing time of 0.44 s compared to existing approaches.The performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and DSC.To enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classification method.

关键词： Brain tumor magnetic resonance imaging principal component analysis fuzzy c-clustering support vector machine

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Comprehensive analysis of glacier recession(2000-2020)in the Nun-Kun Group of Glaciers,Northwestern Himalaya

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Journal of mountain Science 2024年第3期21卷 768-785页

作者： Shakil Ahmad ROmSHOO Ummer AmEEN mustafa Hameed BHAT Tariq abdullah Department of Geoinformatics University of KashmirSrinagarKashmir 190006India Islamic University of Science and Technology AwantiporaKashmir 192122India Centre of Excellence for Glacier Studies in the Western Himalaya University of KashmirSrinagarKashmir 190006India

Himalayan glaciers are shrinking rapidly,especially after 2000.Glacier shrinkage,however,shows a differential pattern in space and time,emphasizing the need to monitor and assess glacier changes at a larger scale.In this study,changes of 48 glaciers situated around the twin peaks of the Nun and Kun mountains in the northwestern Himalaya,hereafter referred to as Nun-Kun Group of Glaciers(NKGG),were investigated using Landsat satellite data during 2000-2020.Changes in glacier area,snout position,Equilibrium Line Altitude(ELA),surface thickness and glacier velocity were assessed using remote sensing data supplemented by field observations.The study revealed that the NKGG glaciers have experienced a recession of 4.5%±3.4%and their snouts have retreated at the rate of 6.4±1.6 m·a^(-1).Additionally,there was a 41%increase observed in the debris cover area during the observation period.Using the geodetic approach,an average glacier elevation change of-1.4±0.4 m·a^(-1)was observed between 2000 and 2012.The observed mass loss of the NKGG has resulted in the deceleration of glacier velocity from 27.0±3.7 m·a^(-1)in 2000 to 21.2±2.2 m·a^(-1)in 2020.The ELA has shifted upwards by 83.0±22 m during the period.Glacier morphological and topographic factors showed a strong influence on glacier recession.Furthermore,a higher recession of 12.9%±3.2%was observed in small glaciers,compared to 2.7%±3.1%in larger glaciers.The debris-covered glaciers showed lower shrinkage(2.8%±1.1%)compared to the clean glaciers(9.3%±5%).The glacier depletion recorded in the NKGG during the last two decades,if continued,would severely diminish glacial volume and capacity to store water,thus jeopardizing the sustainability of water resources in the basin.

关键词： Nun-Kun range Glacier recession Glacier snout Remote Sensing Himalaya Glacier velocity

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Quantitative and Qualitative Responses of Hydroponic Tomato Production to Different Levels of Salinity

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Phyton-International Journal of Experimental Botany 2024年第6期93卷 1311-1323页

作者： Khalid A.Al-Gaadi Ahmed m.Zeyada ElKamil Tola abdullah m.Alhamdan Khalid A.m.Ahmed Rangaswamy madugundu mohamed K.Edrris Department of Agricultural Engineering College of Food and Agriculture SciencesKing Saud UniversityRiyadh11451Saudi Arabia Precision Agriculture Research Chair Deanship of Scientific ResearchKing Saud UniversityRiyadh11451Saudi Arabia Chair of Dates Industry&Technology Deanship of Scientific ResearchKing Saud UniversityRiyadh11451Saudi Arabia Agricultural Research Centre Agricultural Engineering Research Institute(AEnRI)Giza12619Egypt

From economic and nutritional points of view,tomato is,historically,considered one of the most important crops.Without signiﬁcant yield reduction,most commercial cultivars of tomato crops are sensitive to moderate levels of salinity.However,high levels of salt stress can negatively affect the yield and quality of tomato fruits.Therefore,this study was conducted to evaluate the yield and fruit quality of three tomato cultivars(Forester,Ghandowra-F1,and Feisty-Red)cultivated hydroponically,under three different levels of nutrient solution salinity.Evaluation of tomato fruits was performed based on quantity(number and weight of fruits,and total fruit yield),physical quality(color andﬁrmness),and chemical quality(refractometric index“Brix”,pH,EC,K^(+),Na^(+),and NO_(3)-).Experiments were conducted using three salinity levels of the nutrient solution with electrical conductivity values(dSm^(-1))of 2.5(control),6.0,and 9.5.Results showed that the studied tomato cultivars were signiﬁcantly inﬂuenced by high salinity(9.5 dSm^(-1))in comparison to the low(2.5 dSm^(-1))and medium(6.0 dSm^(-1))levels of salinity.On average,the highest fruit weight per plant of 1944.84 g and total fruit yield of 4.42 kgm^(-2) were observed at the low salinity level;however,no signiﬁcant differences were obtained in the two yield factors(single fruit weight and total fruit yield)for the low and medium salinity levels.On the other hand,a signiﬁcant reduction in tomato yield(31%)was associated with the high salinity level compared to the yield at low and medium salinity levels.Results of physical quality parameters showed highly signiﬁcant differences among all salinity levels.On average,the maximum value of color change(1.72)was associated with the medium salinity level,and the maximum value ofﬁrmness(9.61 Ncm^(-1))was recorded at the high salinity level.Salinity levels and tomato cultivars introduced signiﬁcant differences in chemical quality parameters;however,no signiﬁcant differences in these parameters were attributed to the low and mediu

关键词： Quality cultivars yield color ﬁrmness brix

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An Efficient Approach Based on Remora Optimization Algorithm and Levy Flight for Intrusion Detection

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Intelligent Automation & Soft Computing 2023年第7期37卷 235-254页

作者： abdullah mujawib alashjaee Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Kingdom of Saudi Arabia

With the recent increase in network attacks by threats,malware,and other sources,machine learning techniques have gained special attention for intrusion detection due to their ability to classify hundreds of features into normal system behavior or an attack attempt.However,feature selection is a vital preprocessing stage in machine learning approaches.This paper presents a novel feature selection-based approach,Remora Optimization Algorithm-Levy Flight(ROA-LF),to improve intrusion detection by boosting the ROA performance with LF.The developed ROA-LF is assessed using several evaluation measures on five publicly available datasets for intrusion detection:Knowledge discovery and data mining tools competition,network security laboratory knowledge discovery and data mining,intrusion detection evaluation dataset,block out traffic network,Canadian institute of cybersecu-rity and three engineering problems:Cantilever beam design,three-bar truss design,and pressure vessel design.A comparative analysis between developed ROA-LF,particle swarm optimization,salp swarm algorithm,snake opti-mizer,and the original ROA methods is also presented.The results show that the developed ROA-LF is more efficient and superior to other feature selection methods and the three tested engineering problems for intrusion detection.

关键词： Feature selection metaheuristic algorithms intrusion detection Remora optimization algorithm Levy flight

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