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Machine Learning Approaches in Cybersecurity to Enhance Security in Future Network Technologies

SN computer science 2025年第4期6卷 1-18页

作者： Ahmad, Sultan Haque, Md. Alimul Abdeljaber, Hikmat A. M. Eljialy, A.E.M. Nazeer, Jabeen Mishra, B.K. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Mohali Punjab Gharuan 140413 India Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia Department of Physics Veer Kunwar Singh University Ara 802301 India

As technology continues to evolve rapidly, cybersecurity has become a critical global concern. The increasing sophistication of cyber threats poses significant risks to individuals, businesses, and governments. To combat these threats, cybersecurity tools play a crucial role in monitoring, detecting, and mitigating security risks in digital environments. These tools ensure data protection, prevent unauthorized access, and safeguard sensitive information. However, traditional cybersecurity approaches are struggling to keep pace with emerging cyber-attacks. In response, machine learning (ML) has emerged as a powerful solution for enhancing cybersecurity strategies. ML algorithms enable organizations to analyze large datasets, identify anomalies, and predict potential threats with greater accuracy. Cybersecurity tools, integrated with ML, act as the final line of defense against attacks such as data breaches, identity theft, and system intrusions. This research explores the application of ML in selecting and optimizing cybersecurity models for enterprise ICT systems. It also emphasizes the growing demand for skilled professionals who can develop and implement ML-based security solutions. By examining current trends and future possibilities, this study provides valuable insights into the role of ML in strengthening cybersecurity measures and enhancing overall digital protection for organizations worldwide. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber security Cybercrime Data analysis ML algorithms Network security Threats

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Thalassemia Screening by Sentiment Analysis on Social Media Platform Twitter

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computers, Materials & Continua 2023年第7期76卷 665-686页

作者： Wadhah Mohammed M.Aqlan Ghassan Ahmed Ali Khairan Rajab Adel Rajab Asadullah Shaikh Fekry Olayah Shehab Abdulhabib Saeed Alzaeemi Kim Gaik Tay Mohd Adib Omar Ernest Mangantig School of Computer Sciences Universiti Sains MalaysiaUSM11800PenangMalaysia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia86400JohorMalaysia IPPT Universiti Sains MalaysiaUSM11800PenangMalaysia

Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production,resulting in a drop in the size of red blood *** severe forms,it can lead to *** genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for ***,controlling thalassemia is extremely important and is made by promoting screening to the general population,particularly among thalassemia *** Twitter is one of the most influential social media platforms for sharing opinions and discussing different topics like people’s health conditions and major public health *** individuals’sentiments in these tweets helps the research centers to formulate strategies to promote thalassemia screening to the *** effective Lexiconbased approach has been introduced in this study by highlighting a classifier called valence aware dictionary for sentiment reasoning(VADER).In this study applied twitter intelligence tool(TWINT),Natural Language Toolkit(NLTK),and VADER constitute the three main *** represents a gold-standard sentiment lexicon,which is basically tailored to attitudes that are communicated by using social *** contribution of this study is to introduce an effective Lexicon-based approach by highlighting a classifier calledVADERto analyze the sentiment of the general population,particularly among thalassemia carriers on the social media platform *** this study,the results showed that the proposed approach achieved 0.829,0.816,and 0.818 regarding precision,recall,together with F-score,*** tweets were crawled using the search keywords,“thalassemia screening,”thalassemia test,“and thalassemia diagnosis”.Finally,results showed that India and Pakistan ranked the highest in mentions in tweets by the public’s conversations on thalassemia screening with 181 and 164 tweets,respectively.

关键词： Social media platform Twitter screening thalassemia lexicon-based VADER

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A Convnet Model-Based Deep Learning Approach for Pneumonia Recognition from Chest X-ray Images 2

A Convnet Model-Based Deep Learning Approach for Pneumonia R...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Venkateswarulu Naik, B. Unhelkar, Bhuvan Chakrabarti, Prasun Ghantasala, G.S. Pradeep Vidyullatha, Pellakuri Pyla, Meghana Department of Information Systems and Decision Sciences Muma College of Business University of South Florida South Florida United States Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science and Engineering Alliance University Bengaluru India

ISBN: (纸本)9798350383867

Pneumonia diagnosis using chest X-ray (CXR) images shows a decisive part in early detection and handling of the disease. Within this research, proposes a convolutional neural network (ConvNet) model for automated pneumonia diagnosis based on CXR images. Our model leverages the deep learning capabilities of ConvNets to analyze features in CXR images and categorize them as pneumonia-positive or pneumonia-negative. We conducted experimentations on a comprehensive dataset of CXR images, achieving auspicious outcomes in terms of efficiency and accuracy. The proposed ConvNet model demonstrates prospective for an accurate and appropriate pneumonia diagnosis, contributing to improved healthcare outcomes and resource optimization in medical settings. ConvNet model is implemented using tensorflow keras with preprocessing using Image data generator. The models are deployed like sequential, Conv2D with tuning parameters of Relu, dropout, max pool 2d and adam optimizer. For training the images with 150, 150 pixels, train sample as are 1000 and input shape is 3, epochs are 50, batch size is 20. Applications of the categorical cross-entropy loss function for multi-class division with Adam optimizer employed for parameter optimization. The Model trained for a stable amount of epochs through early based on validation loss toward prevent overfitting and learning rate, batch size, and dropout rate optimized through hyperparameter tuning. © 2024 IEEE.

关键词： Convolutional neural networks

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Data Analysis of Sensors Value Using Various Moving Average Methods 7

Data Analysis of Sensors Value Using Various Moving Average ...

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7th IEEE International Conference on Electrical, Electronics, and System Engineering, ICEESE 2024

作者： Tey, Kok Soon Leong, Wen Chek Rahman, Tuan A.Z. Kunjunni, Baarath Choong, Khong Neng Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia IoT Systems Lab MIMOS Berhad Kuala Lumpur57000 Malaysia

ISBN: (纸本)9798350367447

Sensors such as force and distance sensors are crucial for accurate measurements in many engineering applications. However, sensor data often contains noise that can compromise the reliability of the measurements. This study addresses the problem of noisy sensor data by applying several moving average methods to smooth the data. The methods investigated include simple, exponential, modified, square-root, and triangular moving averages, implemented using MATLAB. The effectiveness of these methods is evaluated using the Root Mean Square Error (RMSE) as a performance metric. The results demonstrate that all moving average methods significantly reduce noise, with the exponential moving average showing the lowest RMSE values, thereby indicating superior performance in improving data quality. © 2024 IEEE.

关键词： MATLAB

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Introducing the Hyperdynamic Adaptive Learning Fusion (HALF) model for superior predictive analytics in E-learning

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Neural Computing and Applications 2025年 1-21页

作者： Islam, Umar Alali, Ibrahim Khalil Alotaibi, Shoayee Dlaim Alzaid, Zaid Shah, Babar Ali, Ijaz Moreira, Fernando Department of Computer Science IQRA National University Swat Campus Peshawar Pakistan Department of Instructional Technology Jouf University Sakaka Saudi Arabia Department of Artificial Intelligence and Data Science University of Hail Hail Saudi Arabia Faculty of Computer Science and Information Systems Islamic University of Madinah Madinah42351 Saudi Arabia College of Technological Innovation Zayed University Dubai United Arab Emirates REMIT Portucalense University & IEETA Aveiro University Aveiro Portugal

Today’s complex world is defined by digital changes in educational paradigms to which E-learning has contributed significantly, and as such, accurate prediction methods are needed for student performance modeling. In this paper a new and complex model is proposed, namely the Hyperdynamic Adaptive Learning Fusion (HALF) model that leverages adaptive computing paradigms and artificial intelligence to build a fusion of learning that adapts to the new learning patterns. Many conventional predictive models employ linear and simplistic relationships to predict an outcome from an input;hence, they fail to decompose complex and heterogenic data patterns of educational data and also suffer from scalability issues for dealing with large volumes of data. To overcome these issues, the HALF model employs the relevant ensemble learning algorithms that consist of bagging, boosting, and an innovative adaptive fusion strategy that integrates base and adaptive models to achieve higher accuracy and resilience in the latter. In doing so, and by adopting the scientific method of working on trials and errors and rigorous assessment employing a database derived from the Open University VLE, the investigation presented in this paper provides compelling evidence of the HALF model’s superior efficacy, which yields an accuracy of 87%. 2%, precision of 85. 4% It has been proved that 3% of all students have significant learning disabilities, while the recall value is 89. 1%, surpassing traditional methods. The model’s equation can be easily applied to any variety of courses and of students, which makes it highly beneficial to educators and administrators;at the same time, the model is highly interpretable. Therefore, HALF model proves to be a revolutionary addition to the current kind of statistical modeling in E-learning that depicts student engagement pattern into more precise and accurate form, reduce biases in all way possible and provides solution that might help to improve the course outc

关键词： Contrastive Learning

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Leveraging Deep Learning Models for Accurate and Reproducible Cardiac Measurements in Echocardiography

Leveraging Deep Learning Models for Accurate and Reproducibl...

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2024 International Conference on E-Mobility, Power Control and Smart systems, ICEMPS 2024

作者： Stojmenski, Aleksandar Mihajlov, Martin Ss. Cyril and Methodius University Faculty of Computer Science and Engineering Skopje Macedonia Jozef Stefan Institute Laboratory For Open Systems and Networks Ljubljana Slovenia

ISBN: (纸本)9798350394399

The primary metrics recorded during ultrasound sessions focused on cardiac assessment are focused on the size of the heart's features, its two chambers and pre-chambers. Presently, these measurements are predominantly conducted manually, relying on the subjective judgment of medical professionals. A sonographer takes a transthoracic echocardiogram by first applying a viscous ultrasound gel on the patient at five distinct physical locations and placing the transducer on these positions at various angles. Each angle captures slightly different perspectives of the heart called views. From each view we can make out cutouts of cardiac structures and see motion of valves and walls. This study introduces an innovative approach to cardiac assessment which delegates the evaluation task to a trained algorithm. This has the potential to expedite the measurement process and help clinicians in the interpretation of the results by proposing common cardiac issues based on pre-trained data. In the paper we provide a process for intelligent extraction of diameters along the left ventricular outflow tract (LVOT) and aortic apparatus using only the ultrasound image. We show that it is possible to successfully train a model which can extract the measurements of the diameter of the LVOT in mid systole, the diameter of the aorta annulus (AA) in mid-systole, the diameter at the aortic sinus of Valsalva (ASV) at end diastole, and the diameter of the Sino tubular junction (SJ) at end diastole. With these measures extracted, the model uses machine learning to automate the diagnostics process which can bring diagnostics closer to patients' homes. © 2024 IEEE.

关键词： Echocardiography

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Enhancing Password Security: Analyzing Password Management Practices Among IT Students 7

Enhancing Password Security: Analyzing Password Management P...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power Engineering, CANDO-EPE 2024

作者： Kollar, Anita Mihalovicsne Katona, Jozsef Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Hungary

ISBN: (纸本)9798331531119

Today, with the digital world in its full swing, strong password management is key to protecting sensitive information from both internal and external threats. The current research explores the passwords managing behavior of the IT students at the University of Dunaujvaros. A survey made in May 2024 questioned 68 students, 59 males and 9 females, about behaviors such as using passwords with complexity, not repeating them for multiple accounts or machines, changing them regularly, and which applications they use for password management. The findings have depicted that, as a whole, students appear to be moderately sensitive about the password security instructions. Nonetheless, most continue to indulge in unsafe practices, including easily remembered or weak passwords and infrequent password changes. Furthermore, approximately half of the responders use password management applications, indicating room for significant improvement in the future. The results suggest that intervention and special education measures should be taken to enhance the practice of password security among future IT experts. This research has good potential to contribute toward strategies designed for people behaving safely in password management, and hence promote a secure digital environment. © 2024 IEEE.

关键词： Students

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A Semi-supervised Protein Complex Identification Algorithm Based on Sparseness Constraint

A Semi-supervised Protein Complex Identification Algorithm B...

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2024 International Conference on Applied Mathematics, Modelling and Statistics Application, AMMSA 2024

作者： Liu, Guangming Dang, Fengqing Wang, Bin Peng, Yonghong Wang, Xiaofan Tan, Li School of Computer Science and Engineering Shaanxi Key Laboratory for Network Computing and Security Technology Xi'An University of Technology Shaanxi Xi'an China Faculty of Science and Engineering Computing and Information Science Cambridge United Kingdom School of Computer and Information Engineering Beijing Technology and Business University Beijing China

Recognising protein complexes in protein interaction networks is crucial, but poses a major challenge due to the frequency of noisy interactions. These networks typically involve numerous protein complexes, with each protein generally only participating in a few complexes. Current recognition models often ignore this aspect. To address this problem, we present a semi-supervised protein complex identification algorithm that extends non-negative matrix factorization (NMF) with sparsity constraints. In contrast to conventional approaches that apply a global sparsity constraint to the entire matrix, our method imposes individual sparsity constraints on protein membership indicator vectors. This targeted strategy controls the algorithm more effectively. Our experimental results with yeast and human protein interaction networks show that our algorithm achieves higher accuracy in identifying protein complexes than leading contemporary methods. © Published under licence by IOP Publishing Ltd.

关键词： Non-negative matrix factorization

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Enhancing the Evaluation of Teaching Questionaries in Educational systems Using Sentiment Analysis Techniques

Enhancing the Evaluation of Teaching Questionaries in Educat...

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2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Abd-Elmegid, Laila A. Hegazy Mohamed, Mohamed Abdelgaber, Sayed Arab Open University Associate Information Systems Faculty of Computer Studies Cairo Egypt Helwan University Business Information Systems Department Cairo Egypt Department Cairo Egypt

ISBN: (纸本)9798350393415

Students are excellent at making make up for the poor foundation. Therefore, it is essential to ensure that students' emotions and responsibilities are appropriately controlled in order to ensure ongoing growth in tutoring close to a chance for improvement. Experts in several fields, including layout, have recently praised opinion mining (OM) as the greatest application. Managing and treating understudy beliefs can be difficult, especially when it comes to bearing because of the wide range of phonetics that students use and the volume of data that is available. Attitude mining is becoming more and more motivated, but there are still difficulties. The unique capability of the proposed SASCM, which monitors the undergraduate sentiment analysis comment model, is its capacity to mine comments from students who don't include outline messages. In a similar vein, it could benefit the directors by enabling the enhancement of the Opinion Mining interaction as a whole and carrying out the ensuing assessment to reactivate more illuminating foundations. This way, they can concentrate on understanding them to avoid the disastrous consequences on process learning. This will allow the directors to avoid having to deal with the bad effects of the encounters. Two of the three different modules in the proposed paradigm are the Information Mining unit and the Data Preprocessing tool. Our essay's main goal is to change tutoring systems through an analysis of teacher, student, and instructor feedback. The proposed SASCM model uses a language-based approach to identify a method for removing a significant amount of information from each response in the data collection. It also uses a bundling efforts to create bundles for students using its remarks. The exploratory audit's findings suggested that a particular kind could examine student remarks. Numerous studies have been conducted into the organized model. By changing the layers of its units, the standard is far more adaptable than lectur

关键词： Sentiment analysis

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Comparative Study of Various Machine Learning Techniques for Diabetes Prediction

Comparative Study of Various Machine Learning Techniques for...

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2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025

作者： Pujari, Jambukeshwar Hake, Ashok Department of Computer Science and Business Systems Maharashtra Kolhapur416234 India

ISBN: (纸本)9798331527518

Diabetes is a pervasive chronic disease affecting millions worldwide, necessitating effective and early prediction to improve patient outcomes and reduce healthcare costs. This study presents a comparative analysis of various machine learning models in prediction of diabetes, with an emphasis on both individual model performance and ensemble methods. Logistic Regression, Random Forest, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors, and XGBoost model's performance are analyzed using a dataset of clinical health indicators. Accuracy, Precision, Recall, and F1-score are the key metrics considered to determine their predictive model performance. Furthermore, a Voting Classifier is implemented to explore whether an ensemble of models can outperform individual algorithms in terms of predictive accuracy and *** of the experimental results indicate that the XGBoost model and the Voting Classifier provide superior accuracy and reliability, making them effective tools for early-stage diabetes prediction. This research highlights the potential of machine learning in healthcare and suggests a promising approach to predictive modeling for diabetes diagnosis. © 2025 IEEE.

关键词： Patient treatment

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