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Hybrid of Distributed Cumulative Histograms and Classification Model for Attack Detection

Computer Systems Science & Engineering 2023年第5期45卷 2235-2247页

作者： Mostafa Nassar Anas M.Ali walid El-Shafai Adel Saleeb Fathi E.Abd El-Samie Naglaa F.soliman Hussah Nasser AlEisa Hossam Eldin H.Ahmed Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Department of Computer Science Security Engineering LaboratoryPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Traditional security systems are exposed to many various attacks,which represents a major challenge for the spread of the Internet in the *** techniques have been suggested for detecting attacks using machine learning and deep *** significant advantage of deep learning is that it is highly efficient,but it needs a large training time with a lot of ***,in this paper,we present a new feature reduction strategy based on Distributed Cumulative Histograms(DCH)to distinguish between dataset features to locate the most effective *** histograms assess the dataset instance patterns of the applied features to identify the most effective attributes that can significantly impact the classification *** different models for detecting attacks using Convolutional Neural Network(CNN)and Long Short-Term Memory Network(LSTM)are also *** accuracy test of attack detection using the hybrid model was 98.96%on the UNSW-NP15 *** proposed model is compared with wrapper-based and filter-based Feature Selection(FS)*** proposed model reduced classification time and increased detection accuracy.

关键词： Feature selection DCH LSTM CNN security systems

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A novel hybrid cryptosystem based on DQFrFT watermarking and 3D-CLM encryption for healthcare services

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Frontiers of Information Technology & Electronic Engineering 2023年第7期24卷 1045-1061页

作者： Fatma KHALLAF walid EL-SHAFAI El-Sayed M.EL-RABAIE Naglaa F.soliman Fathi EAbd E.L-SAMIE Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Electrical Engineering Faculty of EngineeringAhram Canadian University6^(th)October CityGiza 12451Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh I1586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia

Quaternion algebra has been used to apply the fractional Fourier transform(FrFT)to color images in a comprehensive ***,the discrete fractional random transform(DFRNT)with adequate basic randomness remains to be *** paper presents a novel multistage privacy system for color medical images based on discrete quaternion fractional Fourier transform(DQFrFT)watermarking and three-dimensional chaotic logistic map(3D-CLM)***,we describe quaternion DFRNT(QDFRNT),which generalizes DFRNT to handle quaternion signals effectively,and then use QDFRNT to perform color medical image adaptive *** efficiently evaluate QDFRNT,this study derives the relationship between the QDFRNT of a quaternion signal and the four components of the DFRNT ***,it uses the human vision system's(HVS)masking qualities of edge,texture,and color tone immediately from the color host image to adaptively modify the watermark strength for each block in the color medical image using the QDFRNT-based adaptive watermarking and support vector machine(SVM)*** limitations of watermark embedding are also explained to conserve watermarking ***,3D-CLM encryption is employed to improve the system's security and efficiency,allowing it to be used as a multistage privacy *** proposed security system is effective against many types of channel noise attacks,according to simulation results.

关键词： Color medical image Quaternion Adaptive watermarking Encryption Fractional transform Three-dimensional chaotic logistic map(3D-CLM)

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Evaluation of Surface Roughness and <i>Streptococcus mutans</i>Adhesion to Bulk-Fill Resin Composites Polished with Different Systems

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Advances in Microbiology 2019年第1期9卷 87-101页

作者： Wafaa E. soliman Ashraf I. Ali walid F. Elkhatib Department of Microbiology and Biotechnology Faculty of Pharmacy Delta University for Science and Technology Cairo Egypt Faculty of Dentistry Mansoura University Mansoura Egypt Department of Microbiology and Immunology Faculty of Pharmacy Ain Shams University African Union Organization St. Cairo Egypt Department of Microbiology and Immunology School of Pharmacy & Pharmaceutical Industries Badr University in Cairo (BUC) Cairo Egypt

Purpose: Bacterial adhesion represents the initial step in biofilm formation, dental caries and decay. This study aimed to evaluate and compare surface roughness and bacterial adhesion to bulk fill resin composites polished with different systems. Methods: Filtek Z350 XT (Incremental-fill resin composite), Filtek Bulk-fill Posterior (Bulk-fill resin composite), and Tetric N Ceram (Bulk-fill resin composite) were used as resin composites. The polishing systems used in this study were Sof-Lex multi-step, PoGo one step, and Mylar strip. Scanning electron microscope (SEM) was used to examine the surface roughness and adhesion of Streptococcus mutans ATCC 25175 standard strain to bulk-fill resin composites. Results: The type of restorative materials did not affect the surface roughness or bacterial adhesion (p > 0.05) but the polishing systems were significant (p R = 0.943) between surface roughness and bacterial adhesion to the tested surfaces. Conclusion: Regardless of the restorative material, Mylar polishing system revealed the smoothest surface and the lowest adhesion of S. mutans as compared to Pogo one step and Sof-Lex multi-step polishing systems.

关键词： Bacterial Adhesion Surface Roughness Streptococcus mutans Polishing Systems

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An Improved Convolutional Neural Network Model for DNA Classification

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Computers, Materials & Continua 2022年第3期70卷 5907-5927页

作者： Naglaa.F.soliman Samia M.Abd-Alhalem walid El-Shafai Salah Eldin S.E.Abdulrahman N.Ismaiel El-Sayed M.El-Rabaie Abeer D.Algarni Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt

Recently,deep learning(DL)became one of the essential tools in bioinformatics.A modified convolutional neural network(CNN)is employed in this paper for building an integratedmodel for deoxyribonucleic acid(DNA)*** any CNN model,convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features.A novel method based on downsampling and CNNs is introduced for feature *** downsampling is an improved form of the existing pooling layer to obtain better classification *** two-dimensional discrete transform(2D DT)and two-dimensional random projection(2D RP)methods are applied for *** convert the high-dimensional data to low-dimensional data and transform the data to the most significant feature ***,there are parameters which directly affect how a CNN model is *** this paper,some issues concerned with the training of CNNs have been *** CNNs are examined by changing some hyperparameters such as the learning rate,size of minibatch,and the number of *** and assessment of the performance of CNNs are carried out on 16S rRNA bacterial *** results indicate that the utilization of a CNN based on wavelet subsampling yields the best trade-off between processing time and accuracy with a learning rate equal to 0.0001,a size of minibatch equal to 64,and a number of epochs equal to 20.

关键词： DNA classification CNN downsampling hyperparameters DL 2D DT 2D RP

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Efficient Hardware Design of a Secure Cancellable Biometric Cryptosystem

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Intelligent Automation & Soft Computing 2023年第4期36卷 929-955页

作者： Lamiaa A.Abou Elazm walid El-Shafai Sameh Ibrahim Mohamed G.Egila H.Shawkey Mohamed K.H.Elsaid Naglaa F.soliman Hussah Nasser AlEisa Fathi E.Abd El-Samie Department of Microelectronics Electronics Research InstituteAlnozhaEgypt Department of Electronics and Electrical Communications Engineering Ain Shams UniversityCairoEgypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh 11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Biometric security is a growing trend,as it supports the authentication of persons using conﬁdential biometric *** of the transmitted data in multi-media systems are susceptible to attacks,which affect the security of these *** systems provide sufﬁcient protection and privacy for *** recently-introduced cancellable biometric recognition systems have not been investigated in the presence of different types of *** addition,they have not been studied on different and large biometric *** point that deserves consideration is the hardware implementation of cancellable biometric recognition *** paper presents a suggested hybrid cancellable biometric recognition system based on a 3D chaotic *** rationale behind the utilization of the 3D chaotic cryptosystem is to guarantee strong encryption of biometric templates,and hence enhance the security and privacy of *** suggested cryptosystem adds signiﬁcant permutation and diffusion to the encrypted biometric *** introduce some sort of attack analysis in this paper to prove the robustness of the proposed cryptosystem against *** addition,a Field Programmable Gate Array(FPGA)implementation of the pro-posed system is *** obtained results with the proposed cryptosystem are compared with those of the traditional encryption schemes,such as Double Random Phase Encoding(DRPE)to reveal superiority,and hence high recogni-tion performance of the proposed cancellable biometric recognition *** obtained results prove that the proposed cryptosystem enhances the security and leads to better efﬁciency of the cancellable biometric recognition system in the presence of different types of attacks.

关键词： Information security cancellable biometric recognition systems cryptanalysis 3D chaotic map encryption FPGA

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Gauss Gradient and SURF Features for Landmine Detection from GPR Images

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Computers, Materials & Continua 2022年第6期71卷 4457-4486页

作者： Fatma M.El-Ghamry walid El-Shafai Mahmouad I.Abdalla Ghada M.El-Banby Abeer D.Algarni Moawad I.Dessouky Adel S.Elfishawy Fathi E.Abd El-Samie Naglaa F.soliman Department of Electronics and Communications Engineering Faculty of EngineeringZagazig UniversityZagazigSharqia44519Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Recently,ground-penetrating radar(GPR)has been extended as a well-known area to investigate the subsurface ***,its output has a low resolution,and it needs more processing for more *** paper presents two algorithms for landmine detection from GPR *** first algorithm depends on a multi-scale technique.A Gaussian kernel with a particular scale is convolved with the image,and after that,two gradients are estimated;horizontal and vertical ***,histogram and cumulative histogram are estimated for the overall gradient *** bin values on the cumulative histogram are used for discrimination between images with and without ***,a neural classifier is used to classify images with cumulative histograms as feature *** second algorithm is based on scale-space analysis with the number of speeded-up robust feature(SURF)points as the key parameter for *** addition,this paper presents a framework for size reduction of GPR images based on decimation for efficient *** further classification steps can be performed on images after *** sensitivity of classification accuracy to the interpolation process is studied in detail.

关键词： GPR images cumulative histogram gradient image neural classifier SURF

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Hybrid Segmentation Approach for Different Medical Image Modalities

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Computers, Materials & Continua 2022年第11期73卷 3454-3471页

作者： walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Naglaa F.soliman Amel A.Alhussan Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

The segmentation process requires separating the image region into sub-regions of similar *** sub-region has a group of pixels having the same characteristics,such as texture or *** paper suggests an efficient hybrid segmentation approach for different medical image modalities based on particle swarm optimization(PSO)and improved fast fuzzy C-means clustering(IFFCM)*** extensive comparative study on different medical images is presented between the proposed approach and other different previous segmentation *** existing medical image segmentation techniques incorporate clustering,thresholding,graph-based,edge-based,active contour,region-based,and watershed *** paper extensively analyzes and summarizes the comparative investigation of these ***,a prediction of the improvement involves the combination of these techniques is *** obtained results demonstrate that the proposed hybrid medical image segmentation approach provides superior outcomes in terms of the examined evaluation metrics compared to the preceding segmentation techniques.

关键词： Image segmentation ultrasonic images X-ray images CT images PET images MR images fuzzy c-mean morphological operations active contour

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Efficient Segmentation Approach for Different Medical Image Modalities

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Computers, Materials & Continua 2022年第11期73卷 3119-3135页

作者： walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Naglaa F.soliman Amel A.Alhussan Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

This paper presents a study of the segmentation of medical *** paper provides a solid introduction to image enhancement along with image segmentation *** the first step,the morphological operations are employed to ensure image detail protection and *** objective of using morphological operations is to remove the defects in the texture of the ***,the Fuzzy C-Means(FCM)clustering algorithm is used to modify membership function based only on the spatial neighbors instead of the distance between pixels within local spatial neighbors and cluster *** proposed technique is very simple to implement and significantly fast since it is not necessary to compute the distance between the neighboring pixels and the cluster *** is also efficient when dealing with noisy images because of its ability to efficiently improve the membership partition *** results are performed on different medical image ***(Us),X-ray(Mammogram),Computed Tomography(CT),Positron Emission Tomography(PET),and Magnetic Resonance(MR)images are the main medical image modalities used in this *** obtained results illustrate that the proposed technique can achieve good results with a short time and efficient image *** results on different image modalities show that the proposed technique can achieve segmentation accuracies of 98.83%,99.71%,99.83%,99.85%,and 99.74%for Us,Mammogram,CT,PET,and MRI images,respectively.

关键词： Image segmentation ultrasonic mammogram CT PET MRI morphological operations FCM active contours

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Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification

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Computer Systems Science & Engineering 2023年第12期47卷 2791-2814页

作者： Firas Abedi Hayder M.A.Ghanimi Abeer D.Algarni Naglaa F.soliman walid El-Shafai Ali Hashim Abbas Zahraa H.Kareem Hussein Muhi Hariz Ahmed Alkhayyat Department of Mathematics College of EducationAl-Zahraa University for WomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity ofWarith Al-AnbiyaaKarbalaIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt College of Information Technology Imam Jaafar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Engineering Department Mazaya University CollegeDhi QarIraq College of Technical Engineering the Islamic UniversityNajafIraq

Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and *** rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within *** the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information *** paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)***,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant *** by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for ***,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute *** propose a COA-based feature selection approach for medical data classification using machine learning *** approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature *** evaluate the performance of our approach on various medical datasets employing diverse machine learning *** results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification *** study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various *** provide further validation,we conduct detailed experiments on a benchmark

关键词： Association rule mining data classification healthcare data machine learning parameter tuning data mining feature selection MLARMC-HDMS COA stochastic gradient descent Apriori algorithm

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Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment

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Computer Systems Science & Engineering 2023年第12期47卷 3127-3144页

作者： Firas Abedi Hayder M.A.Ghanimi Abeer D.Algarni Naglaa F.soliman walid El-Shafai Ali Hashim Abbas Zahraa H.Kareem Hussein Muhi Hariz Ahmed Alkhayyat Department of Mathematics College of EducationAl-Zahraa University forWomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity ofWarith Al-AnbiyaaKarbalaIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt College of Information Technology Imam Jaafar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Engineering Department Mazaya University CollegeDhi QarIraq College of Technical Engineering The Islamic UniversityNajafIraq

Computational intelligence(CI)is a group of nature-simulated computationalmodels and processes for addressing difficult real-life *** CI is useful in the UAV domain as it produces efficient,precise,and rapid ***,unmanned aerial vehicles(UAV)developed a hot research topic in the smart city *** the benefits of UAVs,security remains a major challenging *** addition,deep learning(DL)enabled image classification is useful for several applications such as land cover classification,smart buildings,*** paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification(MDLS-UAVIC)model in a smart city *** purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class *** proposedMDLS-UAVIC model follows a two-stage process:encryption and image *** encryption technique for image encryption effectively encrypts the UAV ***,the image classification process involves anXception-based deep convolutional neural network for the feature extraction ***,shuffled shepherd optimization(SSO)with a recurrent neural network(RNN)model is applied for UAV image classification,showing the novelty of the *** experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset,and the outcomes are examined in various *** achieved a high accuracy of 98%.

关键词： Computational intelligence unmanned aerial vehicles deep learning metaheuristics smart city image encryption image classification

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