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Intrusion Detection in 5G Cellular Network Using Machine Learning

computer systems science & Engineering 2023年第11期47卷 2439-2453页

作者： Ishtiaque Mahmood Tahir Alyas Sagheer Abbas Tariq Shahzad Qaiser Abbas Khmaies Ouahada Knowledge Unit of Systems and Technology UMT Sialkot CampusSialkot51040Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Faculty of Computer Science National College of Business Administration and EconomicsLahore54660Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351Saudi Arabia Department of Computer Science&IT University of SargodhaSargodha40100Pakistan Department of Electrical and Electronic Engineering Science University of JohannesburgP.O Box 524Auckland ParkJohannesburg2006South Africa

Attacks on fully integrated servers,apps,and communication networks via the Internet of Things(IoT)are growing *** devices’effectiveness harms end users,increases cyber threats and identity theft,raises costs,and negatively impacts income as problems brought on by the Internet of Things network go unnoticed for extended *** on Internet of Things interfaces must be closely monitored in real time for effective safety and *** the 1,2,3,and 4G cellular networks,the 5th generation wireless 5G network is indeed the great invasion of mankind and is known as the global advancement of cellular *** to this day,experts are working on the evolution’s sixth generation(6G).It offers amazing capabilities for connecting everything,including gadgets and machines,with wavelengths ranging from 1 to 10 mm and frequencies ranging from 300 MHz to 3 *** gives you the most recent *** countries have already established this technology within their *** is the most crucial aspect of using a 5G *** of the absence of study and network deployment,new technology first introduces new gaps for attackers and *** Protocol(IP)attacks and intrusion will become more prevalent in this *** efficient approach to detect intrusion in the 5G network using a Machine Learning algorithm will be provided in this *** research will highlight the high accuracy rate by validating it for unidentified and suspicious circumstances in the 5G network,such as intruder hackers/*** applying different machine learning algorithms,obtained the best result on Linear Regression Algorithm’s implementation on the dataset results in 92.12%on test data and 92.13%on train data with 92%precision.

关键词： Intrusion detection system machine learning confidentiality integrity availability

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Evolutionary Algorithsm with Machine Learning Based Epileptic Seizure Detection Model

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computers, Materials & Continua 2022年第9期72卷 4541-4555页

作者： Manar Ahmed Hamza Noha Negm Shaha Al-Otaibi Amel AAlhussan Mesfer Al Duhayyim Fuad Ali Mohammed Al-Yarimi Mohammed Rizwanullah Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz University16278AlKharjSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayilAsir62529Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz University16278Saudi Arabia

Machine learning (ML) becomes a familiar topic among decisionmakers in several domains, particularly healthcare. Effective design of MLmodels assists to detect and classify the occurrence of diseases using healthcaredata. Besides, the parameter tuning of the ML models is also essentialto accomplish effective classification results. This article develops a novelred colobuses monkey optimization with kernel extreme learning machine(RCMO-KELM) technique for epileptic seizure detection and *** proposed RCMO-KELM technique initially extracts the chaotic, time,and frequency domain features in the actual EEG signals. In addition, the minmax normalization approach is employed for the pre-processing of the EEGsignals. Moreover, KELM model is used for the detection and classificationof epileptic seizures utilizing EEG signal. Furthermore, the RCMO techniquewas utilized for the optimal parameter tuning of the KELM technique insuch a way that the overall detection outcomes can be considerably *** experimental result analysis of the RCMO-KELM technique has beenexamined using benchmark dataset and the results are inspected under severalaspects. The comparative result analysis reported the better outcomes of theRCMO-KELM technique over the recent approaches with the accuy of 0.956.

关键词： Epileptic seizures eeg signals machine learning kelm parameter tuning rcmo algorithm

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Machine Learning Models with Neutrosophic Numbers for Network Anomaly Detection and Security Defense Technology

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Neutrosophic Sets and systems 2025年 83卷 50-64页

作者： Al-Khazraji, Hussein S. Alkhamees, Ahmed M. Al-Doori, Humam M. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Salama, Ahmed S. Nagm, Ahmad M. Department of Electrical Power Engineering Technologies Al-Hussein University College Karbala Iraq College of Health and Medical Technologies Department of Anesthesia Technologies Ahl Al Bayt University Karbala Iraq Department of Computer Engineering Techniques Al-Yarmok University College Diyala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information Systems and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

In the dynamic world of cybersecurity, strong solutions are necessary to safeguard intricate network systems. By looking at network anomaly detection and security protection, this study investigates how machine learning (ML) might increase digital infrastructure security. We assess how well critical ML approaches, such as ensemble approaches and supervised learning, identify anomalies and lessen risks. The examination of ML-based systems integration into comprehensive security frameworks places a strong emphasis on real-time monitoring and adaptive responses. Examples from real-world situations highlight how crucial ML is to improving network security. After, we apply different ML models to the real-world dataset. Then we use the single-valued Neutrosophic numbers (SVNNs) methodology to evaluate these ML models and select the best one. We use the multi-criteria decision-making (MCDM) approach to obtain the criteria weights and rank the ML models using the EDAS method. The results show that the random forest model is the best ML model under different evaluation matrices. © 2025, University of New Mexico. All rights reserved.

关键词： Risk assessment

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Reference Tracking Control of a Nonlinear Epidemiological Model with State Estimation 9

Reference Tracking Control of a Nonlinear Epidemiological Mo...

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9th International Conference on Control, Decision and information Technologies, CoDIT 2023

作者： Csutak, Balázs Szederkényi, Gábor Pazmany Peter Catholic University Faculty of Information Technology and Bionics Budapest Hungary Institute for Computer Science and Control Systems and Control Laboratory Kende u. 13-17 BudapestH-1111 Hungary

ISBN: (纸本)9798350311402

In this paper, a reference tracking controller for an 8-compartment epidemic model is proposed. The dynamical model describing the disease spread and progression is given in nonlinear input-affine form. The manipulable input is the transmission rate, while the output to be tracked is the number of infected people. The model parameters correspond to the COVID-19 pandemic. The control design uses a simple SEIR model and it is based on feedback linearization combined with extended Kalman filter for state estimation. Simulation results show good tracking performance even with model mismatch and significant parameter uncertainty. © 2023 IEEE.

关键词： COVID-19

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Hunger Search Optimization with Hybrid Deep Learning Enabled Phishing Detection and Classification Model

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computers, Materials & Continua 2022年第12期73卷 6425-6441页

作者： Hadil Shaiba Jaber S.Alzahrani Majdy M.Eltahir Radwa Marzouk Heba Mohsen Manar Ahmed Hamza Department of Computer Sciences 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 Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords,account identifiers,bank details,*** general,these kinds of cyberattacks are made at users through phone calls,emails,or instant *** anti-phishing techniques,currently under use,aremainly based on source code features that need to scrape the webpage *** third party services,these techniques check the classification procedure of phishing Uniform Resource Locators(URLs).Even thoughMachine Learning(ML)techniques have been lately utilized in the identification of phishing,they still need to undergo feature engineering since the techniques are not well-versed in identifying phishing *** tremendous growth and evolution of Deep Learning(DL)techniques paved the way for increasing the accuracy of classification *** this background,the current research article presents a Hunger Search Optimization with Hybrid Deep Learning enabled Phishing Detection and Classification(HSOHDL-PDC)*** presented HSOHDL-PDC model focuses on effective recognition and classification of phishing based on website *** addition,SOHDL-PDC model uses character-level embedding instead of word-level embedding since the URLs generally utilize words with no ***,a hybrid Convolutional Neural Network-Long Short Term Memory(HCNN-LSTM)technique is also applied for identification and classification of *** hyperparameters involved in HCNN-LSTM model are optimized with the help of HSO algorithm which in turn produced improved *** performance of the proposed HSOHDL-PDC model was validated using different datasets and the outcomes confirmed the supremacy of the proposed model over other recent approaches.

关键词： Uniform resource locators phishing cyberattacks machine learning deep learning hyperparameter optimization

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GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images

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Mathematical Biosciences and Engineering 2024年第8期21卷 6847-6869页

作者： Aggarwal, Sonam Gupta, Isha Kumar, Ashok Kautish, Sandeep Almazyad, Abdulaziz S. Mohamed, Ali Wagdy Werner, Frank Shokouhifar, Mohammad Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Model Institute of Engineering and Technology J&K Jammu India Chandigarh University Punjab Mohali140413 India Department of Computer Engineering College of Computer and Information Sciences King Saud University P.O. Box 51178 Riyadh11543 Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza12613 Egypt Applied Science Research Center Applied Science Private University Amman11931 Jordan Faculty of Mathematics Otto-von-Guericke University Magdeburg39016 Germany Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam

Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equivalent to or exceeding those of medical specialists. This capability is particularly crucial in the realm of gastrointestinal disorders, where even experienced gastroenterologists find the automatic diagnosis of such conditions using endoscopic pictures to be a challenging endeavor. Currently, gastrointestinal findings in medical diagnosis are primarily determined by manual inspection by competent gastrointestinal endoscopists. This evaluation procedure is labor-intensive, time-consuming, and frequently results in high variability between laboratories. To address these challenges, we introduced a specialized CNN-based architecture called GastroFuse-Net, designed to recognize human gastrointestinal diseases from endoscopic images. GastroFuse-Net was developed by combining features extracted from two different CNN models with different numbers of layers, integrating shallow and deep representations to capture diverse aspects of the abnormalities. The Kvasir dataset was used to thoroughly test the proposed deep learning model. This dataset contained images that were classified according to structures (cecum, z-line, pylorus), diseases (ulcerative colitis, esophagitis, polyps), or surgical operations (dyed resection margins, dyed lifted polyps). The proposed model was evaluated using various measures, including specificity, recall, precision, F1-score, Mathew’s Correlation Coefficient (MCC), and accuracy. The proposed model GastroFuse-Net exhibited exceptional performance, achieving a precision of 0.985, recall of 0.985, specificity of 0.984, F1-score of 0.997, MCC of 0.982, and an accuracy of 98.5%. ©2024 the Author(s), licensee AIMS Press.

关键词： Endoscopy

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Aspect-Based Sentiment Analysis on Amharic Text for Evaluating Ethio-Telecom Services 2

Aspect-Based Sentiment Analysis on Amharic Text for Evaluati...

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2nd International Conference on Emerging Trends in information Technology and Engineering, ic-ETITE 2024

作者： Tesfa, Tarikwa Belete, Befikadu Abera, Samuel Mohapatra, Sudhir Kumar Das, Tapan Kumar Addis Ababa Science and Technology University Department of Software Engineering Addis Ababa Ethiopia Ethio Telecom Services Addis Ababa Ethiopia Sri Sri University Faculty of Emerging Technologies Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

People are increasingly expressing their views and opinions about a company's goods or service on social media these days. For all types of businesses and organizations, sentiment analysis in text can be used to find prospective customers and obtain quick feedback on the services or goods they provided. As a result, the organization has a better chance and opportunity to meet the needs of the consumer. Using sentiment analysis, we may extract important information from the thoughts and emotions of an organization's supporters and clients. It examines natural language content produced about services and goods. Detailed opinion mining at the aspect/feature level to serve consumer and organizational needs. In this study, three different machine learning algorithms, these are Naïve Bayes (NB), Support Vector Machine (SVM), and K- Nearest Neighbor (KNN) are used to build the proposed aspect-based sentiment analysis model from the customer comments in Amharic text which is collected from Ethio-telecom official Facebook page. The collected data is annotated into sentiment polarity and aspect-based sentiment classes by the help of five annotators. After data annotation, different preprocessing tasks were implemented. Bag of words and TF-IDF word representation techniques were used to represent each unique word to their vector representation. The experiment was conducted on those three models and the result was validated. The labeled aspects were not balanced so that a SMOTE class imbalance handling technique was employed to the dataset. Finally, SVM model trained with TF-IDF word representation technique with SMOTE oversampled dataset has achieved the highest accuracy result of 98% as compared to NB and KNN. © 2024 IEEE.

关键词： Sentiment analysis

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Abstractive Arabic Text Summarization Using Hyperparameter Tuned Denoising Deep Neural Network

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Intelligent Automation & Soft Computing 2023年第11期38卷 153-168页

作者： Ibrahim M.Alwayle Hala J.Alshahrani Saud S.Alotaibi Khaled M.Alalayah Amira Sayed A.Aziz Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Manar Ahmed Hamza Department of Computer Science College of Science and ArtsSharurahNajran UniversitySharurahSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversityMakkahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)*** presented AATS-HTDDNN technique aims to generate summaries of Arabic *** the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the *** study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization *** phase shows the novelty of the current *** validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was *** comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.

关键词： Text summarization deep learning denoising deep neural networks hyperparameter tuning Arabic language

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Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning

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computers, Materials & Continua 2023年第6期75卷 5307-5319页

作者： Khalid M.O.Nahar Mohammad Alauthman Saud Yonbawi Ammar Almomani Computer Science Department Yarmouk UniversityIrbidJordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan Software Engineering Department University of JeddahJeddahKSA Research and Innovation Department Skyline University CollegeP.O.Box 1797SharjahUAE IT Department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan

Social media networks are becoming essential to our daily activities,and many issues are due to this great involvement in our *** is a social media network issue,a global crisis affecting the victims and society as a *** results from a misunderstanding regarding freedom of *** this work,we proposed a methodology for detecting such behaviors(bullying,harassment,and hate-related texts)using supervised machine learning algo-rithms(SVM,Naïve Bayes,Logistic regression,and random forest)and for predicting a topic associated with these text data using unsupervised natural language processing,such as latent Dirichlet *** addition,we used accuracy,precision,recall,and F1 score to assess prior *** show that the use of logistic regression,support vector machine,random forest model,and Naïve Bayes has 95%,94.97%,94.66%,and 93.1%accuracy,respectively.

关键词： Cyberbullying social media naïve bayes support vector machine natural language processing LDA

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An Improved Deep Learning Framework Based on Multi-Scale Convolutional Architecture for Road Crack Detection 2nd

An Improved Deep Learning Framework Based on Multi-Scale Con...

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2nd International Conference on Data science and Network Engineering, ICDSNE 2024

作者： Idris, Idris Ya’u Ya’u, Badamasi Imam Ali, Usman Gulzar, Yonis Department of Computer Science School of Science and Technology Federal Polytechnic Bauchi Bauchi743001 Nigeria Department of Mathematical Sciences Faculty of Science Abubakar Tafawa Balewa University Bauchi740272 Nigeria Gombe760253 Nigeria Department of Management Information Systems College of Business Administration King Faisal University Al Ahsa31982 Saudi Arabia

ISBN: (纸本)9789819783359

In addition to serving as a means of transportation, a highway connects the local economy. But as the pavement ages, several flaws including cracks, potholes, and deformation gradually show up on the surfaces of the road. Crack evaluation is typically done by hand using field surveys performed by humans. Nevertheless, these labor-intensive, time-consuming, manual survey methods are not reproducible or repeatable and place surveyors in dangerous situations. This proposes a novel deep-learning framework for road crack detection based on multi-scale convolutional architecture. To address the challenges of object occlusion, soft non-maximal suppression (NMS) is applied across crack proposals at various feature scales. The experimental results show that the introduced model attains an accuracy of 0.9989 and better detection performance across four distinct object proposal building block (OPBB) architectures. Thus, our model outperforms the benchmark models in every instance of OPBBs. In this way, the problem of large object scale variation in road crack images with different object occlusion challenges has been tackled, and the positive images are divided into a set of semantically significant regions, such as the road surface and cracks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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