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Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

Computers, Materials & Continua 2023年第2期74卷 2677-2693页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

关键词： Feature selection grey wolf optimization dipper throated optimization intrusion detection internet-of-things(IoT)

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GA-based on-line optimization of CAN message scheduling

GA-based on-line optimization of CAN message scheduling

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2007 Mediterranean Conference on control and Automation, MED

作者： Duzanec, D. Stare, Z. Kovacic, Z. Ziegler d.o.o. Construction and Development Department Zagreb Croatia University of Zagreb Faculty of EE and C Department of Electronic Systems and Information Processing Croatia University of Zagreb Faculty of EE and C Department of Control and Computing Engineering Zagreb Croatia

ISBN: (纸本)142441282X

In this paper we describe a design and implementation of an on-line scheduling algorithm for controller Area Network (CAN) messages that exploits properties of genetic algorithms (GA) to achieve global message throughput optimization. The use of GA-based scheduler has been found effective even in time-critical distributed control systems, such as the considered control system that encompasses several control subsystems at the fire fighting vehicle. The experimental results show that the GA-optimized CAN message scheduler considerably decreased the average message queuing time. ©2007 IEEE.

关键词： Genetic algorithms

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Comparative analysis of 3D face recognition algorithms using range image and curvature-based representations

Comparative analysis of 3D face recognition algorithms using...

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2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced computing systems, IDAACS 2013

作者： Tonchev, Krasimir Manolova, Agata Paliy, Ihor Faculty of Telecommunications Technical University of Sofia 8 Kl. Ohridski Str 1000 Sofia Bulgaria Department of Information Computing Systems and Control Ternopil National Economic University 3 Peremoga Square 46020 Ternopil Ukraine

ISBN: (纸本)9781479914265

In this paper we present a comparative analysis of two algorithms for image representation with application to recognition of 3D face scans with the presence of facial expressions. We begin with processing of the input point cloud based on curvature analysis and range image representation to achieve a unique representation of the face features. Then, subspace projection using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are performed. Finally classification with different classifiers will be performed. © 2013 IEEE.

关键词： Principal component analysis

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Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

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Computers, Materials & Continua 2023年第2期74卷 3183-3198页

作者： Doaa Sami Khafaga Faten Khalid Karim Abdelaziz A.Abdelhamid El-Sayed M.El-kenawy Hend K.Alkahtani Nima Khodadadi Mohammed Hadwan Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt The Department of Civil and Environmental Engineering Florida International UniversityMiamiFLUSA Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human *** attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote *** attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are *** paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic *** employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA *** proposed voting classifier categorizes the network intrusions robustly and *** assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack *** dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and *** achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

关键词： Voting classifier whale optimization algorithm dipper throated optimization intrusion detection internet-of-things

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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Computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

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Computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： Metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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Conception of Ontology for Computer Methods of Paintings Analysis

Conception of Ontology for Computer Methods of Paintings Ana...

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2019 International Multi-Conference on Engineering, Computer and information Sciences, SIBIRCON 2019

作者： Beloozerov, Viktor Murashov, Dmitrij Trusova, Yuliya Berezin, Aleksej Ivanova, Ekaterina All-Russia Institute for Scientific and Technical Information Department for Classification Systems Development Moscow Russia Institute of Educational Informatics Federal Research Center 'Information and Control' Moscow Russia A. A. Dorodnicyn Computing Center Federal Research Center 'Information and Control' Moscow Russia State Historical Muzeum Restoration Department Moscow Russia Ilya Glazuvov Russian Academy of Painting Sculpture and Architecture Department of Painting Restoration Moscow Russia

ISBN: (纸本)9781728144016

This paper describes the procedure of developing the 'OntArt' ontology for the subject area 'Attribution and restoration of works of easel oil painting'. Special attention is paid to the domain of computer methods application. The basis of the work is a previously developed thesaurus of this domain. The conceptual shell of the original thesaurus descriptors is the standard CIDOC CRM framework, which binds domain concepts to abstract philosophical categories. In order to connect particular concepts with abstract categories, it is proposed to include 21 intermediate-level conceptual classes in the CIDOC CRM structure. Thus, we have built a formalized structure of concepts for the area of attribution and restoration of painting, ranging from the highest philosophical abstractions to specialized methods of computer analysis of picture images. This structure includes about 1300 terms in Russian. The English part has about 500 terms and is replenished as the interlanguage equivalence of the terms is agreed. In the part of vocabulary of computer methods, we have a full bilingual ontology with one-To-one correspondence of the Russian and English versions. The current version of the ontology is implemented as a TiddlyWiki software module and is publicly available. © 2019 IEEE.

关键词： Ontology

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Optimized Weighted Ensemble Using Dipper Throated Optimization Algorithm in Metamaterial Antenna

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Computers, Materials & Continua 2022年第12期73卷 5771-5788页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz AAbdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

Metamaterial Antennas are a type of antenna that uses metamaterial to enhance *** bandwidth restriction associated with small antennas can be solved using metamaterial *** learning is gaining popularity as a way to improve solutions in a range of *** learning approaches are currently a big part of current research,and they’re likely to be huge in the *** model utilized determines the accuracy of the prediction in large *** goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna’s bandwidth and *** basic models employed in the developed ensemble are Support Vector Regression(SVR),K-NearestRegression(KNR),Multi-Layer Perceptron(MLP),Decision Trees(DT),and Random Forest(RF).The percentages of contribution of these models in the ensemble model are weighted and optimized using the dipper throated optimization(DTO)*** choose the best features from the dataset,the binary(bDTO)algorithm is *** proposed ensemble model is compared to the base models and results are recorded and analyzed *** addition,two other ensembles are incorporated in the conducted experiments for *** ensembles are average ensemble and K-nearest neighbors(KNN)-based *** comparison is performed in terms of eleven evaluation *** evaluation results confirmed the superiority of the proposed model when compared with the basic models and the other ensemble models.

关键词： Metamaterial antenna dipper throated optimization feature selection parameters prediction

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An Aircraft Identification System Using Convolution Neural Networks 12

An Aircraft Identification System Using Convolution Neural N...

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12th IEEE International Conference on Intelligent Data Acquisition and Advanced computing systems: Technology and Applications, IDAACS 2023

作者： Turchenko, Viktor Turchenko, Iryna Vasylkiv, Nadiia West Ukrainian National University Department for Information Computer Systems and Control Ternopil46000 Ukraine McMaster University Department of Computing and Software HamiltonL8S 4L8 Canada Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine

ISBN: (纸本)9798350358056

The task of aircraft identification using their registration number is important as visual identification remains the only method of their identification in case of air traffic control systems or radar failure. In this paper, an analysis of known solutions was carried out and an aircraft identification system was developed using a pre-trained YOLO (You Only Look Once) deep convolutional neural network and Optical Character Recognition (OCR) technology. The system consists of three modules: the M1 module is designed for the detection of region of interest-tail, the M2 module is designed for the detection of region of interest-registration number, the M3 module is designed for the classification of the registration number. 2309 aircraft images were used for training, 426 for validation and 408 for testing from the Fine-Grained Visual Classification of Aircraft (FGVC-Aircraft) dataset. Our results show that the mean average precision for the M1 and M2 YOLOv7 models on the test set were 0.92 and 0.85 respectively, and for the module M3 OCR technology the classification error was 0.2. © 2023 IEEE.

关键词： aircraft convolutional neural networks deep learning detection transfer learning YOLOv7

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Finite-horizon estimation of randomly occurring faults for discrete time-varying systems

Finite-horizon estimation of randomly occurring faults for d...

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19th International Conference on Automation and computing, ICAC 2013

作者： Dong, Hongli Wang, Zidong Shen, Bo College of Electrical and Information Engineering Northeast Petroleum University Daqing 163318 China Department of Information Systems and Computing Brunel University Uxbridge Middlesex UB8 3PH United Kingdom School of Information Science and Technology Donghua University Shanghai 200051 China Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg 47057 Germany

ISBN: (纸本)9781908549082

This paper deals with the finite-horizon estimation of randomly occurring faults for a class of linear discrete time-varying systems. All the system parameters are time-varying and the failures are assumed to occur in a random way, and two sets of Bernoulli distributed white sequences are introduced to govern the failures probability. By using the completing squares method and stochastic analysis techniques, necessary and sufficient conditions for the existence of the finite-horizon Hoo fault estimator are derived, and then the time-varying fault estimator parameters are obtained by solving coupled backward recursive Riccati difference equations (RDEs). A simulation example is utilized to illustrate the usefulness of the proposed fault estimation method. © 2013 Chinese Automation and computing Society in the UK - CACS.

关键词： Riccati equations

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