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Artificial Intelligence Techniques Based Learner Authentication in cybersecurity Higher Education Institutions

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Computers, Materials & Continua 2022年第8期72卷 3131-3144页

作者： Abdullah Saad AL-Malaise AL-Ghamdi Mahmoud Ragab Information Systems Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt

Education 4.0 is being authorized more and more by the design of artificial intelligence(AI)*** education institutions(HEI)have started to utilize Internet technologies to improve the quality of the service and boost *** to the unavailability of information technology(IT)infrastructures,HEI is vulnerable to *** authentication can be used to authenticate a person based on biological features such as face,fingerprint,iris,and so *** study designs a novel search and rescue optimization with deep learning based learning authentication technique for cybersecurity in higher education institutions,named SRODLLAC *** proposed SRODL-LAC technique aims to authenticate the learner/student in HEI using fingerprint ***,the SRODLLACtechnique designs a median filtering(MF)based preprocessing approach to improving the quality of the *** addition,the Densely Connected Networks(DenseNet-77)model is applied for the extraction of ***,search and rescue optimization(SRO)algorithm with deep neural network(DNN)model is utilized for the classification ***,template matching process is done for fingerprint identification.A wide range of simulation analyses is carried out and the results are inspected under several *** experimental results reported the effective performance of the SRODL-LAC technique over the other methodologies.

关键词： cybersecurity authentication biometrics security higher education institution deep learning

Enhanced Artificial Intelligence-based cybersecurity Intrusion Detection for Higher Education Institutions

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Computers, Materials & Continua 2022年第8期72卷 2895-2907页

作者： Abdullah S.AL-Malaise AL-Ghamdi Mahmoud Ragab Maha Farouk S.Sabir Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt

As higher education institutions(HEIs)go online,several benefits are attained,and also it is vulnerable to several kinds of *** accomplish security,this paper presents artificial intelligence based cybersecurity intrusion detection models to accomplish *** incorporation of the strategies into business is a tendency among several distinct industries,comprising education,have recognized as game ***,the HEIs are highly related to the requirement and knowledge of the learner,making the education procedure highly ***,artificial intelligence(AI)and machine learning(ML)models have shown significant interest in *** study designs a novel Artificial Intelligence based cybersecurity Intrusion Detection Model for Higher Education Institutions named AICIDHEI *** goal of the AICID-HEI technique is to determine the occurrence of distinct kinds of intrusions in higher education *** AICID-HEI technique encompassesmin-max normalization approach to preprocess the ***,the AICID-HEI technique involves the design of improved differential evolution algorithm based feature selection(IDEA-FS)technique is applied to choose the feature ***,the bidirectional long short-term memory(BiLSTM)model is utilized for the detection and classification of intrusions in the ***,the Adam optimizer is applied for hyperparameter tuning to properly adjust the hyperparameters in higher educational *** order to validate the experimental results of the proposed AICID-HEI technique,the simulation results of the AICIDHEI technique take place by the use of benchmark *** experimental results reported the betterment of the AICID-HEI technique over the other methods interms of different measures.

关键词： Higher education institutions deep learning machine learning cybersecurity intrusion detection

An end-to-end text spotter with text relation networks

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cybersecurity 2021年第1期4卷 91-103页

作者： Jianguo Jiang Baole Wei Min Yu Gang Li Boquan Li Chao Liu Min Li Weiqing Huang Institute of Information Engineering Chinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of ScienceBeijingChina centre for Cyber Security Research and Innovation Deakin UniversityGeelongAustralia

Reading text in images automatically has become an attractive research topic in computer ***,end-to-end spotting of scene text has attracted significant research attention,and relatively ideal accuracy has been achieved on several ***,most of the existing works overlooked the semantic connection between the scene text instances,and had limitations in situations such as occlusion,blurring,and unseen characters,which result in some semantic information lost in the text *** relevance between texts generally lies in the scene *** the perspective of cognitive psychology,humans often combine the nearby easy-to-recognize texts to infer the unidentifiable *** this paper,we propose a novel graph-based method for intermediate semantic features enhancement,called Text Relation ***,we model the co-occurrence relationship of scene texts as a *** nodes in the graph represent the text instances in a scene image,and the corresponding semantic features are defined as representations of the *** relative positions between text instances are measured as the weights of edges in the established ***,a convolution operation is performed on the graph to aggregate semantic information and enhance the intermediate features corresponding to text *** evaluate the proposed method through comprehensive experiments on several mainstream benchmarks,and get highly competitive *** example,on the SCUT-CTW1500,our method surpasses the previous top works by 2.1%on the word spotting task.

关键词： Scene text spotting Graph convolutional network Visual reasoning

Optimized Artificial Neural Network Techniques to Improve cybersecurity of Higher Education Institution

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Computers, Materials & Continua 2022年第8期72卷 3385-3399页

作者： Abdullah Saad AL-Malaise AL-Ghamdi Mahmoud Ragab Maha Farouk S.Sabir Ahmed Elhassanein Ashraf A.Gouda Information Systems Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Mathematics Department College of ScienceUniversity of BishaBishaSaudi Arabia Mathematics Department Faculty of ScienceDamanhour UniversityDamanhourEgypt

Education acts as an important part of economic growth and improvement in human *** educational sectors have transformed a lot in recent days,and Information and Communication Technology(ICT)is an effective part of the education *** every action in university and college,right from the process fromcounselling to admissions and fee deposits has been *** records,quiz,evaluation,mark,and grade submissions involved the utilization of the ***,security is essential to accomplish cybersecurity in higher security institutions(HEIs).In this view,this study develops an Automated Outlier Detection for cybersecurity in Higher Education Institutions(AOD-CSHEI)*** AOD-CSHEI technique intends to determine the presence of intrusions or attacks in the *** AOD-CSHEI technique initially performs data pre-processing in two stages namely data conversion and class *** addition,the Adaptive Synthetic(ADASYN)technique is exploited for the removal of outliers in the ***,the sparrow search algorithm(SSA)with deep neural network(DNN)model is used for the classification of data into the existence or absence of intrusions in the HEIs ***,the SSA is utilized to effectually adjust the hyper parameters of the DNN *** order to showcase the enhanced performance of the AOD-CSHEI technique,a set of simulations take place on three benchmark datasets and the results reported the enhanced efficiency of the AOD-CSHEI technique over its compared methods with higher accuracy of 0.9997.

关键词： Higher security institutions intrusion detection system artificial intelligence deep neural network hyperparameter tuning deep learning

Automated Machine Learning Enabled cybersecurity Threat Detection in Internet of Things Environment

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Computer Systems Science & Engineering 2023年第4期45卷 687-700页

作者： Fadwa Alrowais Sami Althahabi Saud S.Alotaibi Abdullah Mohamed Manar Ahmed Hamza Radwa Marzouk Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Research centre Future University in EgyptNew Cairo11745Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Recently,Internet of Things(IoT)devices produces massive quantity of data from distinct sources that get transmitted over public *** becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be *** development of automated tools for cyber threat detection and classification using machine learning(ML)and artificial intelligence(AI)tools become essential to accomplish security in the IoT *** is needed to minimize security issues related to IoT gadgets ***,this article introduces a new Mayfly optimization(MFO)with regularized extreme learning machine(RELM)model,named MFO-RELM for cybersecurity Threat Detection and classification in IoT *** presented MFORELM technique accomplishes the effectual identification of cybersecurity threats that exist in the IoT *** accomplishing this,the MFO-RELM model pre-processes the actual IoT data into a meaningful *** addition,the RELM model receives the pre-processed data and carries out the classification *** order to boost the performance of the RELM model,the MFO algorithm has been employed to *** performance validation of the MFO-RELM model is tested using standard datasets and the results highlighted the better outcomes of the MFO-RELM model under distinct aspects.

关键词： cybersecurity threats classification internet of things machine learning parameter optimization

cybersecurity for Allied Future Submarines

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World Journal of Engineering and Technology 2018年第4期6卷 696-712页

作者： Keith F. Joiner Simon Reay Atkinson Pete Christensen Elena Sitnikova Systems Capability centre University of New South Wales Canberra at Australian Defence Force Academy Canberra Australia centre for International Security Studies and Sydney Cyber Security Network University of Sydney Sydney Australia The MITRE Corporation McLean VA US Australian centre for Cyber Security Canberra University of New South Wales Canberra at Australian Defence Force Academy Canberra Australia

Cyber has become a supposedly cheap first-strike weapon of political choice by potential adversaries in a milieu placing insurgency, terrorism, international crime and state-based influences in close un-regulated proximity. The merging of electronic and cyber warfare means that not even submarines, however unconnected or firewalled they may be, are immune. The quantum attack surface of submarines is as much in their past, as they are in their designs today and their operations tomorrow: they must survive to be credible and ideally they should even be a contemporary offensive cyber deterrent. Such critical defensive systems require robust security systems engineering and cybersecurity test and evaluation to build and sustain their cyber-resilience. This paper uses Australia’s future submarine program [1]1 to outline key facets needed in a submarine program to achieve cyber resilience, including how to adapt U.S. Department of Defense (DoD) best practices to engineer, test and sustain cyber-resilient submarine systems. Strategies are needed that provision sovereign-owned and operated land-based test sites to design, build, demonstrate and sustain critical submarine systems. This work is most relevant to countries allied to the U.S. and importing submarine capabilities, such as within lesser European powers and also in the Indo-Pacific where both cyber warfare and submarines are proliferating.

关键词： Cyber-Resilience Future Submarine Design Land-Based Test Sites Quantum Attack Surface Test and Evaluation

Search and Rescue Optimization with Machine Learning Enabled cybersecurity Model

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Computer Systems Science & Engineering 2023年第5期45卷 1393-1407页

作者： Hanan Abdullah Mengash Jaber S.Alzahrani Majdy M.Eltahir Fahd N.Al-Wesabi Abdullah Mohamed Manar Ahmed Hamza Radwa Marzouk Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Research centre Future University in EgyptNew Cairo11745Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Presently,smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping,e-learning,ehealthcare,*** the benefits of advanced technologies,issues are also existed from the transformation of the physical word into digital word,particularly in online social networks(OSN).Cyberbullying(CB)is a major problem in OSN which needs to be addressed by the use of automated natural language processing(NLP)and machine learning(ML)*** article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks,named SRO-MLCOSN *** presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking *** SRO-MLCOSN model initially employs Glove technique for word embedding ***,a multiclass-weighted kernel extreme learning machine(M-WKELM)model is utilized for effectual identification and categorization of ***,Search and Rescue Optimization(SRO)algorithm is exploited to fine tune the parameters involved in the M-WKELM *** experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision,recall,and F1-score of 96.24%,98.71%,and 97.46%respectively.

关键词： cybersecurity cyberbullying social networking machine learning search and rescue optimization

Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media

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Computers, Materials & Continua 2022年第12期73卷 5011-5024页

作者： Mesfer Al Duhayyim Heba G.Mohamed Saud S.Alotaibi Hany Mahgoub Abdullah Mohamed Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Research centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl KharjSaudi Arabia

Cyberbullying(CB)is a challenging issue in social media and it becomes important to effectively identify the occurrence of *** recently developed deep learning(DL)models pave the way to design CB classifier models with maximum *** the same time,optimal hyperparameter tuning process plays a vital role to enhance overall *** study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification(TLGODL-CBC)model in Social *** proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media ***,the input data is cleaned and pre-processed to make it compatible for further *** by,independent recurrent autoencoder(IRAE)model is utilized for the recognition and classification of ***,the TLGO algorithm is used to optimally adjust the parameters related to the IRAE model and shows the novelty of the *** assuring the improved outcomes of the TLGODLCBC approach,a wide range of simulations are executed and the outcomes are investigated under several *** simulation outcomes make sure the improvements of the TLGODL-CBC model over recent approaches.

关键词： Social media deep learning cyberbullying cybersecurity hyperparameter optimization

Spotted Hyena Optimizer with Deep Learning Driven cybersecurity for Social Networks

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Computer Systems Science & Engineering 2023年第5期45卷 2033-2047页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Lubna A.Alharbi Mohamed K.Nour Abdullah Mohamed Ahmed S.Almasoud Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk47512Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Research centre Future University in EgyptNew Cairo11845Egypt Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia

Recent developments on Internet and social networking have led to the growth of aggressive language and hate *** provocation,abuses,and attacks are widely termed cyberbullying(CB).The massive quantity of user generated content makes it difficult to recognize *** advancements in machine learning(ML),deep learning(DL),and natural language processing(NLP)tools enable to detect and classify CB in social *** this view,this study introduces a spotted hyena optimizer with deep learning driven cybersecurity(SHODLCS)model for *** presented SHODLCS model intends to accomplish cybersecurity from the identification of CB in the *** achieving this,the SHODLCS model involves data pre-processing and TF-IDF based feature *** addition,the cascaded recurrent neural network(CRNN)model is applied for the identification and classification of ***,the SHO algorithm is exploited to optimally tune the hyperparameters involved in the CRNN model and thereby results in enhanced classifier *** experimental validation of the SHODLCS model on the benchmark dataset portrayed the better outcomes of the SHODLCS model over the recent approaches.

关键词： cybersecurity cyberbullying online social network deep learning spotted hyena optimizer