检索结果-南通市图书馆

Digital Text Document Watermarking Based Tampering Attack Detection via Internet

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2024年第3期48卷 759-771页

作者： manal Abdullah alohali Muna Elsadig Fahd N.al-Wesabi Mesfer al Duhayyim Anwer Mustafa Hilal Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62217Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16733Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Owing to the rapid increase in the interchange of text information through internet networks,the reliability and security of digital content are becoming a major research *** detection,Content authentication,and integrity verification of digital content interchanged through the Internet were utilized to solve a major concern in information and communication *** authors’difficulties were tampering detection,authentication,and integrity verification of the digital *** study develops an Automated Data Mining based Digital Text Document Watermarking for Tampering Attack Detection(ADMDTW-TAD)via the *** DM concept is exploited in the presented ADMDTW-TAD technique to identify the document’s appropriate characteristics to embed larger watermark *** presented secure watermarking scheme intends to transmit digital text documents over the Internet *** the watermark is embedded with no damage to the original document,it is then shared with the *** watermark extraction process is performed to get the original document *** experimental validation of the ADMDTW-TAD technique is carried out under varying levels of attack volumes,and the outcomes were inspected in terms of different *** simulation values indicated that the ADMDTW-TAD technique improved performance over other models.

关键词： Content authentication tampering attacks detection model security digital watermarking

Securing Arabic Contents algorithm for Smart Detecting of Illegal Tampering Attacks

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第2期70卷 2879-2894页

作者： Mesfer al Duhayyim manal Abdullah alohali Fahd N.al-Wesabi Anwer Mustafa Hilal Mohammad Medani Manar Ahmed Hamza Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Themost common digital media exchanged via the Internet is in text *** Arabic language is considered one of themost sensitive languages of content modification due to the presence of diacritics that can cause a change in the *** this paper,an intelligent scheme is proposed for improving the reliability and security of the text exchanged via the *** core mechanism of the proposed scheme depends on integrating the hidden Markov model and zero text watermarking *** watermark key will be generated by utilizing the extracted features of the text analysis process using the third order and word level of the Markov *** Embedding and detection processes of the proposed scheme will be performed logically without the effect of the original *** proposed scheme is implemented using PHP with VS code *** simulation results,using varying sizes of standard datasets,show that the proposed scheme can obtain high reliability and provide better accuracy of the common illegal tampering *** results with other baseline techniques show the added value of the proposed scheme.

关键词： Tampering detection zero-watermarking soft computing text analysis hidden Markov model

Optimal Deep Learning Based Ransomware Detection and Classification in the Internet of Things Environment

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2023年第9期46卷 3087-3102页

作者： manal Abdullah alohali Muna Elsadig Fahd N.al-Wesabi Mesfer al Duhayyim Anwer Mustafa Hilal Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

With the advent of the Internet of Things(IoT),several devices like sensors nowadays can interact and easily share *** the IoT model is prone to security concerns as several attackers try to hit the network and make it *** such scenarios,security concern is the most *** models were intended to address these security problems;still,several emergent variants of botnet attacks like Bashlite,Mirai,and Persirai use security *** malware classification and detection in the IoT model is still a problem,as the adversary reliably generates a new variant of IoT malware and actively searches for compromise on the victim *** article develops a Sine Cosine algorithm with Deep Learning based Ransomware Detection and Classification(SCADL-RWDC)method in an IoT *** the presented SCADL-RWDCtechnique,the major intention exists in recognizing and classifying ransomware attacks in the IoT *** SCADL-RWDC technique uses the SCA feature selection(SCA-FS)model to improve the detection ***,the SCADL-RWDC technique exploits the hybrid grey wolf optimizer(HGWO)with a gated recurrent unit(GRU)model for ransomware classification.A widespread experimental analysis is performed to exhibit the enhanced ransomware detection outcomes of the SCADL-RWDC *** comparison study reported the enhancement of the SCADL-RWDC technique over other models.

关键词： Security IoT network ransomware attack deep learning metaheuristics

Association of E-cadherin (CDH1) gene polymorphisms and gastric cancer risk

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

World Journal of Gastroenterology 2010年第27期16卷 34332-3436页

作者： Mansour S al-Moundhri manal al-Khanbashi Mohammed al-Kindi Maryam al-Nabhani Ikram A Burney abdulaziz al-Farsi Bassim al-Bahrani Medical Oncology Unit Department of MedicineCollege of Medicine and Health SciencesSultan Qaboos University Department of Clinical Genetics College of Medicine and Health SciencesSultan Qaboos University Department of Biochemistry College of Medicine and Health SciencesSultan Qaboos University National Oncology Center Royal Hospital

AIM:To investigate the associations between CDH1 gene polymorphisms and gastric cancer(GC) risk ***:We analyzed four CDH1 polymorphisms(+54 T>C,-160 C>A,-616 G>C,-3159 T>C) in an Omani population,by extraction of genomic DNA from the peripheral blood of 192 patients with GC and 170 control participants and performed CDH1 genotyping using DNA ***:CDH1-160-AA genotype was associated with an increased risk of GC(OR = 3.6,95% CI:1.1-11.8)(P = 0.03).There was no significant association between the other polymorphisms and GC *** haplotype analysis of +54 T>C,-160 C>A,-616 G>C,-3159 T>C genotypes revealed that the OR of CCGC and CAGC haplotypes was 1.5(95% CI:0.7-3.5) and 1.5(95% CI:0.2-3.0),but did not reach statistical ***:The current study suggests that the-160 AA genotype was associated with an increased risk of GC in Oman.

关键词： Gastric cancer Polymorphism CDH1

Structural,magnetic,electrical and microwave properties of spinel ferrites

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Rare Earths 2022年第3期40卷 443-450,I0003页

作者： Salma Aman Naseeb Ahmad M.H.alhossainy Hind albalawi manal Morsi Tahani I.al-Muhimeed Abeer A.alObaid Department of Physics Khwaja Fareed University of Engineering and Information TechnologyAbu Dhabi RoadRahim Yar Khan64200Pakistan Department of Physics Faculty of ScienceTaibah UniversityMedinaSaudi Arabia Department of Physics College of SciencePrincess Nourah bint Abdulrahman University11671RiyadhSaudi Arabia Department of Physics College of Arts and SciencePrince Sattam Bin Abdulaziz UniversityRiyadhWadi AddawasirSaudi Arabia Department of Chemistry College of ScienceKing Saud UniversityP.O.Box 22452Riyadh11495Saudi Arabia

The ferrite series having formulation SrYb_(y)Fe_(2-y)O_(4)(y=0.00 to 0.10)were synthesized via the sol-gel *** involvement of the yttrium in the base produces several changes in the structural,magnetic,and electrical performance of the *** of structural parameters was done through X-ray *** samples have cubic structures confirmed from X-ray *** coercivity of sample enlarges with the involvement of Yb-ions while their saturation and remanence magnetization values *** the concentration of Yb-ion increases in the lattice,the real and imaginary values of permittivity *** increased content of Yb enhances the impedance *** analysis of the M vs H loop was finished under the fixed range of−2 to+2 *** using 0.5-12 GHz frequency,the microwave measurements of all the sample were *** dispersion of frequency of ferrites is responsible for the natural magnetic resonance phenomenon and the domain wall *** evaluated features of synthesized sample show their appropriateness for microwave absorption appliances.

关键词： Microwave absorption Dielectric constant Conductivity Magnetization Impedance Rare earths

Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第7期72卷 141-156页

作者： R.Gopi Mahantesh Mathapati B.Prasad Sultan Ahmad Fahd N.al-Wesabi manal Abdullah alohali Anwer Mustafa Hilal Department of Computer Science and Engineering Dhanalakshmi Srinivasan Engineering CollegePerambalur621212India Department of Computer Science and Engineering RajaRajeswari College of EngineeringBengaluru560074India Department of Information Technology Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Computer Science College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia&Faculty of Computer and ITSana’a UniversitySana’a15347Yemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

VehicularAd hoc Network(VANET)has become an integral part of Intelligent Transportation Systems(ITS)in today’s *** is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real *** is susceptible to security issues,particularly DoS attacks,owing to maximum unpredictability in ***,effective identification and the classification of attacks have become the major requirements for secure data transmission in *** the same time,congestion control is also one of the key research problems in VANET which aims at minimizing the time expended on roads and calculating travel time as well as waiting time at intersections,for a *** this motivation,the current research paper presents an intelligent DoS attack detection with Congestion Control(IDoS-CC)technique for *** presented IDoSCC technique involves two-stage processes namely,Teaching and Learning Based Optimization(TLBO)-based Congestion Control(TLBO-CC)and Gated Recurrent Unit(GRU)-based DoS detection(GRU-DoSD).The goal of IDoS-CC technique is to reduce the level of congestion and detect the attacks that exist in the *** algorithm is also involved in IDoS-CC technique for optimization of the routes taken by vehicles via traffic signals and to minimize the congestion on a particular route instantaneously so as to assure minimal fuel *** is applied to avoid congestion on ***,GRU-DoSD model is employed as a classification model to effectively discriminate the compromised and genuine vehicles in the *** outcomes from a series of simulation analyses highlight the supremacy of the proposed IDoS-CC technique as it reduced the congestion and successfully identified the DoS attacks in network.

关键词： VANET intelligent transportation systems congestion control attack detection dos attack deep learning

Blockchain Assisted Optimal Machine Learning Based Cyberattack Detection and Classification Scheme

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2023年第9期46卷 3583-3598页

作者： manal Abdullah alohali Muna Elsadig Fahd N.al-Wesabi Mesfer al Duhayyim Anwer Mustafa Hilal Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj 16278Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz University Al-Kharj16278Saudi Arabia

With recent advancements in information and communication technology,a huge volume of corporate and sensitive user data was shared consistently across the network,making it vulnerable to an attack that may be brought some factors under risk:data availability,confidentiality,and *** Detection Systems(IDS)were mostly exploited in various networks to help promptly recognize ***,blockchain(BC)technology has received much more interest as a means to share data without needing a trusted third ***,this study designs a new Blockchain Assisted Optimal Machine Learning based Cyberattack Detection and Classification(BAOML-CADC)*** the BAOML-CADC technique,the major focus lies in identifying *** do so,the presented BAOML-CADC technique applies a thermal equilibrium algorithm-based feature selection(TEA-FS)method for the optimal choice of *** BAOML-CADC technique uses an extreme learning machine(ELM)model for cyberattack *** addition,a BC-based integrity verification technique is developed to defend against the misrouting attack,showing the innovation of the *** experimental validation of BAOML-CADC algorithm is tested on a benchmark cyberattack *** obtained values implied the improved performance of the BAOML-CADC algorithm over other techniques.

关键词： Cyberattack machine learning blockchain thermal equilibrium algorithm feature selection

Optimal Deep Belief Network Enabled Cybersecurity Phishing Email Classification

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2023年第3期44卷 2701-2713页

作者： Ashit Kumar Dutta T.Meyyappan Basit Qureshi Majed alsanea Anas Waleed Abulfaraj manal M.al Faraj Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Science Alagappa UniversityKaraikudi630003India Department of Computer Science Prince Sultan UniversityRiyadh11586Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah23613Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Recently,developments of Internet and cloud technologies have resulted in a considerable rise in utilization of online media for day to day *** results in illegal access to users’private data and compromises *** is a popular attack which tricked the user into accessing malicious data and gaining the *** identification of phishing emails can be treated as an essential process in the domain of *** article focuses on the design of bio-geography based optimization with deep learning for Phishing Email detection and classification(BBODL-PEDC)*** major intention of the BBODL-PEDC model is to distinguish emails between legitimate and *** BBODL-PEDC model initially performs data pre-processing in three levels namely email cleaning,tokenization,and stop word ***,TF-IDF model is applied for the extraction of useful feature ***,optimal deep belief network(DBN)model is used for the email classification and its efficacy can be boosted by the BBO based hyperparameter tuning *** performance validation of the BBODL-PEDC model can be performed using benchmark dataset and the results are assessed under several *** comparative studies reported the superior outcomes of the BBODL-PEDC model over the recent approaches.

关键词： Cybersecurity phishing email data classification deep learning biogeography based optimization hyperparameter tuning

An Automated Deep Learning Based Muscular Dystrophy Detection and Classification Model

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第4期71卷 305-320页

作者： T.Gopalakrishnan Periakaruppan Sudhakaran K.C.Ramya K.Sathesh Kumar Fahd N.al-Wesabi manal Abdullah alohali Anwer Mustafa Hilal School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering SRM TRP Engineering CollegeTiruchirappalli621105India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such asmuscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging(MRI). Among these techniques, Muscle MRI recommends the diagnosis ofmuscular dystrophy through identification of the patterns that exist in musclefatty replacement. But the patterns overlap among various diseases whereasthere is a lack of knowledge prevalent with regards to disease-specific ***, artificial intelligence techniques can be used in the diagnosis ofmuscular dystrophies, which enables us to analyze, learn, and predict forthe future. In this scenario, the current research article presents an automated muscular dystrophy detection and classification model using SynergicDeep Learning (SDL) method with extreme Gradient Boosting (XGBoost),called SDL-XGBoost. SDL-XGBoost model has been proposed to act as anautomated deep learning (DL) model that examines the muscle MRI dataand diagnose muscular dystrophies. SDL-XGBoost model employs Kapur’sentropy based Region of Interest (RoI) for detection purposes. Besides, SDLbased feature extraction process is applied to derive a useful set of featurevectors. Finally, XGBoost model is employed as a classification approach todetermine proper class labels for muscle MRI data. The researcher conductedextensive set of simulations to showcase the superior performance of SDLXGBoost model. The obtained experimental values highlighted the supremacyof SDL-XGBoost model over other methods in terms of high accuracy being96.18% and 94.25% classification performance upon DMD and BMD respectively. Therefore, SDL-XGBoost model can help physicians in the diagnosis of muscular dystrophies by identifying the patterns of muscle fatty replacementin muscle MRI.

关键词： Muscle magnetic resonance imaging XGBoost synergic deep learning roI detection kapur’s entropy muscular dystrophies