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Enhanced Metaheuristics with Machine Learning Enabled Cyberattack Detection Model

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Intelligent Automation & soft Computing 2023年第9期37卷 2849-2863页

作者： ahmed s.almasoud Department of Information systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia

The Internet of Things(IoT)is considered the next-gen connection network and is ubiquitous since it is based on the Internet.Intrusion Detection system(IDs)determines the intrusion performance of terminal equipment and IoT communication procedures from IoT environments after taking equivalent defence measures based on the identified behaviour.In this back-ground,the current study develops an Enhanced Metaheuristics with Machine Learning enabled Cyberattack Detection and Classification(EMML-CADC)model in an IoT environment.The aim of the presented EMML-CADC model is to detect cyberattacks in IoT environments with enhanced efficiency.To attain this,the EMML-CADC model primarily employs a data preprocessing stage to normalize the data into a uniform format.In addition,Enhanced Cat swarm Optimization based Feature selection(ECsO-Fs)approach is followed to choose the optimal feature subsets.Besides,Mayfly Optimization(MFO)with Twin support Vector Machine(TsVM),called the MFO-TsVM model,is utilized for the detection and classification of cyberattacks.Here,the MFO model has been exploited to fine-tune the TsVM variables for enhanced results.The performance of the proposed EMML-CADC model was validated using a benchmark dataset,and the results were inspected under several measures.The comparative study concluded that the EMML-CADC model is superior to other models under different measures.

关键词： Metaheuristics cyberattack detection machine learning cat swarm optimization security

Deep Learning with Image Classification Based secure CPs for Healthcare sector

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Computers, Materials & Continua 2022年第8期72卷 2633-2648页

作者： ahmed s.almasoud Abdelzahir Abdelmaboud Faisal s.Alsubaei Manar ahmed Hamza Ishfaq Yaseen Mohammed Abaker Abdelwahed Motwakel Mohammed Rizwanullah Department of Information systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia Department of Information systems College of Science and ArtsKing Khalid UniversityMahayil Asir62529Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Computer science Community CollegeKing Khalid UniversityMahayil Asir62529Saudi Arabia

Cyber-Physical system(CPs)involves the combination of physical processes with computation and communication systems.The recent advancementsmade in cloud computing,Wireless sensor Network(WsN),healthcare sensors,etc.tend to develop CPs as a proficient model for healthcare applications especially,home patient care.Though several techniques have been proposed earlier related to CPs structures,only a handful of studies has focused on the design of CPs models for health care sector.so,the proposal for a dedicated CPs model for healthcare sector necessitates a significant interest to ensure data privacy.To overcome the challenges,the current research paper designs a Deep Learning-based Intrusion Detection and Image Classification for secure CPs(DLIDIC-sCPs)model for healthcare sector.The aim of the proposed DLIDIC-sCPs model is to achieve secure image transmission and image classification process for CPs in healthcare sector.Primarily,data acquisition takes place with the help of sensors and detection of intrusions is performed using Fuzzy Deep Neural Network(FDNN)technique.Besides,Multiple share Creation(MsC)approach is used to create several shares of medical image so as to accomplish security.Also,blockchain is employed as a distributed data storage entity to create a ledger that provides access to the client.For image classification,Inception v3 with Fuzzy Wavelet Neural Network(FWNN)is utilized that diagnose the disease from the applied medical image.Finally,salp swarm Algorithm(ssA)is utilized to fine tune the parameters involved in WNN model,thereby boosting its classification performance.A wide range of simulations was carried out to highlight the superiority of the proposed DLIDIC-sCPs technique.The simulation outcomes confirm that DLIDIC-sCPs approach demonstrates promising results in terms of security,privacy,and image classification outcomes over recent state-of-the-art techniques.

关键词： Cyber Physical systems healthcare cybersecurity deep learning share creation image classification

Coyote Optimization Using Fuzzy system for Energy Efficiency in WsN

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Computers, Materials & Continua 2022年第8期72卷 3269-3281页

作者： ahmed s.almasoud Taiseer Abdalla Elfadil Eisa Marwa Obayya Abdelzahir Abdelmaboud Mesfer Al Duhayyim Ishfaq Yaseen Manar ahmed Hamza Abdelwahed Motwakel Department of Information systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Information systems-Girls section King Khalid UniversityMahayil62529Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information systems College of Science and Artsat MahayilKing Khalid UniversitySaudi Arabia Department of Natural and Applied sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

In recent days,internet of things is widely implemented in Wireless sensor Network(WsN).It comprises of sensor hubs associated together through the WsNs.The WsNis generally affected by the power in battery due to the linked sensor nodes.In order to extend the lifespan of WsN,clustering techniques are used for the improvement of energy consumption.Clustering methods divide the nodes in WsN and form a cluster.Moreover,it consists of unique Cluster Head(CH)in each cluster.In the existing system,soft-K means clustering techniques are used in energy consumption in WsN.The soft-k means algorithm does not work with the large-scale wireless sensor networks,therefore it causes reliability and energy consumption problems.To overcome this,the proposed Load-Balanced Clustering conjunction with Coyote Optimization with Fuzzy Logic(LBC-COFL)algorithm is used.The main objective is to perform the lifespan by balancing the gateways with the load of less energy.The proposed algorithm is evaluated using the metrics such as energy consumption,throughput,central tendency,network lifespan,and total energy utilization.

关键词： Clustering wireless sensor networks fuzzy logic energy efficient optimization

Parkinson’s Detection Using RNN-Graph-LsTM with Optimization Based on speech signals

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Computers, Materials & Continua 2022年第7期72卷 871-886页

作者： ahmed s.almasoud Taiseer Abdalla Elfadil Eisa Fahd N.Al-Wesabi Abubakar Elsafi Mesfer Al Duhayyim Ishfaq Yaseen Manar ahmed Hamza Abdelwahed Motwakel Department of Information systems College of Computer and Information SciencesPrince Sultan UniversityMahayil62529Saudi Arabia Department of Information systems-Girls section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer science College of Science and Artsat MahayilKing Khalid UniversityMahayil62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of software Engineering College of Computer Science and EngineeringUniversity of JeddahSaudi Arabia Department of Natural and Applied sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityMahayil62529Saudi Arabia Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj62529Saudi Arabia

Early detection of Parkinson’s Disease(PD)using the PD patients’voice changes would avoid the intervention before the identification of physical symptoms.Various machine learning algorithms were developed to detect PD detection.Nevertheless,these ML methods are lack in generalization and reduced classification performance due to subject overlap.To overcome these issues,this proposed work apply graph long short term memory(GLsTM)model to classify the dynamic features of the PD patient speech signal.The proposed classification model has been further improved by implementing the recurrent neural network(RNN)in batch normalization layer of GLsTM and optimized with adaptive moment estimation(ADAM)on network hidden layer.To consider the importance of feature engineering,this proposed system use Linear Discriminant analysis(LDA)for dimensionality reduction and sparseAuto-Encoder(sAE)for extracting the dynamic speech features.Based on the computation of energy content transited from unvoiced to voice(onset)and voice to voiceless(offset),dynamic features are measured.The PD datasets is evaluated under 10 fold cross validation without sample overlap.The proposed smart PD detection method called RNN-GLsTM-ADAM is numerically experimented with persistent phonations in terms of accuracy,sensitivity,and specificity andMatthew correlation coefficient.The evaluated result of RNN-GLsTM-ADAM extremely improves the PD detection accuracy than static feature based conventional ML and DL approaches.

关键词： Dimensionality reduction LDA feature extraction auto encoder classification recurrent neural network LsTM and optimization

Artificial Intelligence Based Data Offloading Technique for secure MEC systems

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Computers, Materials & Continua 2022年第8期72卷 2783-2795页

作者： Fadwa Alrowais ahmed s.almasoud Radwa Marzouk Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammed Rizwanullah Abdelwahed Motwakel Ishfaq Yaseen Department of Computer sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information systems College of Computer and Information SciencesPrince Sultan UniversityRafha StreetRiyadh11586Saudi Arabia Department of Information systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer science College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information systems Faculty of Computer and Information TechnologySana’a University61101Yemen Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Mobile edge computing(MEC)provides effective cloud services and functionality at the edge device,to improve the quality of service(Qos)of end users by offloading the high computation tasks.Currently,the introduction of deep learning(DL)and hardware technologies paves amethod in detecting the current traffic status,data offloading,and cyberattacks in MEC.This study introduces an artificial intelligence with metaheuristic based data offloading technique for secure MEC(AIMDO-sMEC)systems.The proposed AIMDO-sMEC technique incorporates an effective traffic prediction module using siamese Neural Networks(sNN)to determine the traffic status in the MEC system.Also,an adaptive sampling cross entropy(AsCE)technique is utilized for data offloading in MEC systems.Moreover,the modified salp swarm algorithm(MssA)with extreme gradient boosting(XGBoost)technique was implemented to identification and classification of cyberattack that exist in the MEC systems.For examining the enhanced outcomes of the AIMDO-sMEC technique,a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDOsMEC technique with the minimal completion time of tasks(CTT)of 0.680.

关键词： Data offloading mobile edge computing security machine learning artificial intelligence XGBoost salp swarm algorithm

Metaheuristic Based Data Gathering scheme for Clustered UAVs in 6G Communication Network

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Computers, Materials & Continua 2022年第6期71卷 5311-5325页

作者： ahmed s.almasoud siwar Ben Haj Hassine Nadhem NEMRI Fahd N.Al-Wesabi Manar ahmed Hamza Anwer Mustafa Hilal Abdelwahed Motwakel Mesfer Al Duhayyim Department of Information systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer science College of Science&Arts at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

The sixth-generation(6G)wireless communication networks are anticipated in integrating aerial,terrestrial,and maritime communication into a robust system to accomplish trustworthy,quick,and low latency needs.It enables to achieve maximum throughput and delay for several applications.Besides,the evolution of 6G leads to the design of unmanned aerial vehicles(UAVs)in providing inexpensive and effective solutions in various application areas such as healthcare,environment monitoring,and so on.In the UAV network,effective data collection with restricted energy capacity poses a major issue to achieving high quality network communication.It can be addressed by the use of clustering techniques forUAVs in 6G networks.In this aspect,this study develops a novel metaheuristic based energy efficient data gathering scheme for clustered unmanned aerial vehicles(MEEDG-CUAV).The proposed MEEDG-CUAV technique intends in partitioning the UAV networks into various clusters and assign a cluster head(CH)to reduce the overall energy utilization.Besides,the quantum chaotic butterfly optimization algorithm(QCBOA)with a fitness function is derived to choose CHs and construct clusters.The experimental validation of the MEEDG-CUAV technique occurs utilizing benchmark dataset and the experimental results highlighted the better performance over the other state of art techniques interms of different measures.

关键词： 6G network mobile communication uav networks energy efficiency clustering metaheuristics

Automated Multi-Document Biomedical Text summarization Using Deep Learning Model

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Computers, Materials & Continua 2022年第6期71卷 5799-5815页

作者： ahmed s.almasoud siwar Ben Haj Hassine Fahd N.Al-Wesabi Mohamed K.Nour Anwer Mustafa Hilal Mesfer Al Duhayyim Manar ahmed Hamza Abdelwahed Motwakel Department of Information systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer science College of Science and Arts at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Due to the advanced developments of the Internet and information technologies,a massive quantity of electronic data in the biomedical sector has been exponentially increased.To handle the huge amount of biomedical data,automated multi-document biomedical text summarization becomes an effective and robust approach of accessing the increased amount of technical and medical literature in the biomedical sector through the summarization of multiple source documents by retaining the significantly informative data.so,multi-document biomedical text summarization acts as a vital role to alleviate the issue of accessing precise and updated information.This paper presents a Deep Learning based Attention Long short Term Memory(DLALsTM)Model for Multi-document Biomedical Text summarization.The proposed DL-ALsTM model initially performs data preprocessing to convert the available medical data into a compatible format for further processing.Then,the DL-ALsTM model gets executed to summarize the contents from the multiple biomedical documents.In order to tune the summarization performance of the DL-ALsTM model,chaotic glowworm swarm optimization(CGsO)algorithm is employed.Extensive experimentation analysis is performed to ensure the betterment of the DL-ALsTM model and the results are investigated using the PubMed dataset.Comprehensive comparative result analysis is carried out to showcase the efficiency of the proposed DL-ALsTM model with the recently presented models.

关键词： Biomedical text summarization healthcare deep learning lstm parameter tuning

Optimal Deep Learning Enabled Communication system for Unmanned Aerial Vehicles

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Computer systems science & Engineering 2023年第4期45卷 955-969页

作者： Anwer Mustafa Hilal Jaber s.Alzahrani Dalia H.Elkamchouchi Majdy M.Eltahir ahmed s.almasoud Abdelwahed Motwakel Abu sarwar Zamani Ishfaq Yaseen Department of Computer and self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Information systems College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia

Recently,unmanned aerial vehicles(UAV)or drones are widely employed for several application areas such as surveillance,disaster management,etc.since UAVs are limited to energy,efficient coordination between them becomes essential to optimally utilize the resources and effective communication among them and base station(Bs).Therefore,clustering can be employed as an effective way of accomplishing smart communication systems among multiple UAVs.In this aspect,this paper presents a group teaching optimization algorithm with deep learning enabled smart communication system(GTOADL-sCs)technique for UAV networks.The proposed GTOADL-sCs model encompasses a two stage process namely clustering and classification.At the initial stage,the GTOADL-sCs model includes a GTOA based clustering scheme to elect cluster heads(CHs)and organize clusters.Besides,the GTOADL-sCs model develops a fitness function containing three input parameters as residual energy of UAVs,average neighoring distance,and UAV degree.For classification process,the GTOADLsCs model applies pre-trained densely connected network(DenseNet201)feature extractor with gated recurrent unit(GRU)classifier.For ensuring the enhanced performance of the GTOADL-sCs model,a widespread simulation analysis is performed and the comparative study reported the significant outcomes over the existing approaches with maximum packet delivery ratio(PDR)of 92.60%.

关键词： Unmanned aerial vehicles energy efficiency smart communication system deep learning

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 speech.Online provocation,abuses,and attacks are widely termed cyberbullying(CB).The massive quantity of user generated content makes it difficult to recognize CB.Current advancements in machine learning(ML),deep learning(DL),and natural language processing(NLP)tools enable to detect and classify CB in social networks.In this view,this study introduces a spotted hyena optimizer with deep learning driven cybersecurity(sHODLCs)model for OsN.The presented sHODLCs model intends to accomplish cybersecurity from the identification of CB in the OsN.For achieving this,the sHODLCs model involves data pre-processing and TF-IDF based feature extraction.In addition,the cascaded recurrent neural network(CRNN)model is applied for the identification and classification of CB.Finally,the sHO algorithm is exploited to optimally tune the hyperparameters involved in the CRNN model and thereby results in enhanced classifier performance.The 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