Research on Intrusion Detection Method Based on Pearson Correlation Coefficient Feature Selection Algorithm

UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)

Moustafa, N., & Slay, J. (2015) · 2015 Military Communications and Information Systems Conference (MilCIS) · doi:10.1109/milcis.2015.7348942 · Cited by 4,033

The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set

Moustafa, N., & Slay, J. (2016) · Information Security Journal A Global Perspective · doi:10.1080/19393555.2015.1125974 · Cited by 1,191

An application of machine learning to network intrusion detection

Sinclair, C., Pierce, L., & Matzner, S. (2003) · Annual Computer Security Applications Conference · doi:10.1109/csac.1999.816048 · Cited by 278

Classification Prediction of Breast Cancer Based on Machine Learning

Chen, H., Wang, N., Du, X., Mei, K., Zhou, Y. M., & Cai, G. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/6530719 · Cited by 136

Evaluating the impact of filter-based feature selection in intrusion detection systems

Zouhri, H., Idri, A., & Ratnani, A. (2023) · International Journal of Information Security · doi:10.1007/s10207-023-00767-y · Cited by 65

Diagnosis of Parkinson’s disease based on voice signals using SHAP and hard voting ensemble method

Ghaheri, P., Nasiri, H., Shateri, A., & Homafar, A. (2023) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2023.2263125 · Cited by 45

Ensemble learning-based stability improvement method for feature selection towards performance prediction

Xiang, F., Zhao, Y.-L., Zhang, M., Zuo, Y. N., Zou, X., & Tao, F. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.03.001 · Cited by 43

csORF-finder: an effective ensemble learning framework for accurate identification of multi-species coding short open reading frames

Zhang, M., Zhao, J., Li, C., Ge, F., Wu, J., Jiang, B., Song, J., & Song, X. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac392 · Cited by 33

Stock price series forecasting using multi-scale modeling with boruta feature selection and adaptive denoising

Li, J., Liu, Y., Gong, H., & Huang, X. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111365 · Cited by 28

Predicting superconducting transition temperature through advanced machine learning and innovative feature engineering

Gashmard, H., Shakeripour, H., & Alaei, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-54440-y · Cited by 25

Predicting compressive strength of green concrete using hybrid artificial neural network with genetic algorithm

Pan, L., Wang, Y., Li, K., & Guo, X. (2022) · Structural Concrete · doi:10.1002/suco.202200034 · Cited by 23

Feature selection via maximizing inter-class independence and minimizing intra-class redundancy for hierarchical classification

Shi, J., Li, Z., & Zhao, H. (2023) · Information Sciences · doi:10.1016/j.ins.2023.01.048 · Cited by 23

Dynamic Monitoring of Desertification in Ningdong Based on Landsat Images and Machine Learning

Li, P., Chen, P., Shen, J., Deng, W., Kang, X., Wang, G., & Zhou, S. (2022) · Sustainability · doi:10.3390/su14127470 · Cited by 22

Construction of an ECL-DPV Dual Model Biosensor for Dopamine Detection Based on PSO-ANN Algorithm

Rao, Z., Guo, B., Zu, J., Zheng, W., Xu, Y., & Yang, Y. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3358413 · Cited by 20

Assessing the effectiveness of dimensionality reduction on the interpretability of opaque machine learning-based attack detection systems

Zouhri, H., Idri, A., & Hakkoum, H. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109627 · Cited by 20

A Hybrid Machine Learning Framework for Predicting Hydrogen Storage Capacities in Metal Hydrides: Unsupervised Feature Learning with Deep Neural Networks

Bhattacharjee, S., Das, P., Ram, S., & Lee, S. (2025) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.5c03612 · Cited by 18

A three-stage wavelength selection algorithm for near-infrared spectroscopy calibration

Feng, X.-Y., Chen, Z., Yi, S., & Wang, P.-H. (2024) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2024.125029 · Cited by 14

CorrNet: pearson correlation based pruning for efficient convolutional neural networks

Kumar, A., Yin, B., Shaikh, A. M., Ali, M., & Wei, W. (2022) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-022-01624-5 · Cited by 14

A Comparative Assessment of Wrappers and Filters for Detecting Cyber Intrusions

Zouhri, H., & Idri, A. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-60221-4_12 · Cited by 13

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

Umer, M. J., Sharif, M. I., Alhaisoni, M. M., Tariq, U., Kim, Y. J., & Chang, B. (2022) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.030463 · Cited by 13

A comparative study of the selection of cutting fluids used in machining processes by multi criteria decision making (MCDM) methods

Özakın, B. (2023) · Sadhana · doi:10.1007/s12046-023-02265-2 · Cited by 13

Granger Causality-Based Forecasting Model for Rainfall at Ratnapura Area, Sri Lanka: A Deep Learning Approach

Saubhagya, S., Tilakaratne, C. D., Lakraj, P., & Mammadov, M. A. (2024) · Forecasting · doi:10.3390/forecast6040056 · Cited by 12

A variable selection method based on multicollinearity reduction for food origin traceability identification

Tian, L., Li, Y., & Zhang, M. (2025) · Vibrational Spectroscopy · doi:10.1016/j.vibspec.2025.103804 · Cited by 12

Feature selection and global interpretability of black-box classification for intrusion detection

Zouhri, H., & Idri, A. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11549-z · Cited by 12

Scalable and Robust Intrusion Detection System to Secure the IoT Environments using Software Defined Networks (SDN) Enabled Architecture

Alshammari, T. M., & Alserhani, F. (2022) · International Journal of Computer Networks And Applications · doi:10.22247/ijcna/2022/217701 · Cited by 12

Bayesian Deep Learning for Fault Diagnosis of Induction Motors With Reduced Data Reliance and Improved Interpretability

Lai, Z., Peng, W., Feng, G., & Pan, M. (2025) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2025.3546347 · Cited by 10

Jugar en el parque para promover el cuidado ambiental en la infancia

Zapata, A. M. C., & Adame-Rivera, L. M. (2024) · Revista Latinoamericana de Ciencias Sociales Niñez y Juventud · doi:10.11600/rlcsnj.22.3.6232 · Cited by 10

An early warning model for starfish disaster based on multi-sensor fusion

Li, L., Liu, T., Hui, H., Song, H., He, S., Li, P., Gu, Y., & Chen, J. (2023) · Frontiers in Marine Science · doi:10.3389/fmars.2023.1167191 · Cited by 10

Predicting Abrasion Resistance in Thermoplastic Polyurethanes Using Machine Learning

Zahmatkesh, S., Esmaeili, H., Valerio, A. T., & Rizvi, R. R. (2025) · Journal of Applied Polymer Science · doi:10.1002/app.57619 · Cited by 9

A wildfire occurrence risk model based on a back-propagation neural network-optimized genetic algorithm

Zhang, H., Liu, H., Ma, G., Zhang, Y., Yao, J., & Gu, C. (2023) · Frontiers in Energy Research · doi:10.3389/fenrg.2022.1031762 · Cited by 8

Optimization of ultra-thin battery heat transfer structures using entropy minimization and multi-objective genetic algorithm

Yao, X., Li, Y., He, H., Shi, Q., Liu, Q., Zhu, X., Jü, X., & Xu, C. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116623 · Cited by 7

Evaluating hybrid and machine learning models for landslide susceptibility mapping in a high-risk region of the Western Ghats, India

S, K. M. K., & Kothandaraman, S. (2025) · Geomatics Natural Hazards and Risk · doi:10.1080/19475705.2025.2595694 · Cited by 6

Machine Learning-Driven Pyrolysis Optimization: Capturing Hidden Feedstock Effects on Biochar and Bio-Oil Yields

Abaei, S., Derogar, E., & Tavakoli, O. (2025) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.5c03390 · Cited by 6

Breast Cancer Classification Using Machine Learning and Feature Subset Selection Techniques

Ray, S., Pradhan, D., & Ray, N. K. (2024) · OITS International Conference on Information Technology (OCIT) · doi:10.1109/ocit65031.2024.00091 · Cited by 6

Optimal Control Strategy for Ship Cabin’s Active Chilled Beam System Using Improved Multi-Objective Salp Swarm Algorithm

Liu, C., Su, Y., & Zhang, D. (2023) · Journal of Marine Science and Engineering · doi:10.3390/jmse11071396 · Cited by 6

Enhancing Auto Insurance Fraud Detection Using Convolutional Neural Networks

Wongpanti, R., & Vittayakorn, S. (2024) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse61278.2024.10613702 · Cited by 6

A feature selection and scoring scheme for dimensionality reduction in a machine learning task

Emmoh, P. U., Eke, C. I., & Moses, T. (2024) · Journal of the Nigerian Society of Physical Sciences · doi:10.46481/jnsps.2025.2273 · Cited by 6

SmartHive-IDS: an advanced intrusion detection framework for SDN-HoneyNet enabled ICPS using hybrid explainable ensemble feature selection with optimized Bi-LSTM

Krishnaveni, S., Zeadally, S., Sivamohan, S., & Sridhar, S. S. (2025) · Cluster Computing · doi:10.1007/s10586-025-05648-8 · Cited by 5

Early Detection of Autism Spectrum Disorder in Children Using Different Machine Learning Algorithms

Kadhum, S. W., & Tawfeeq, M. A. (2026) · Emerging Trends in Engineering and Sustainability · doi:10.65505/etes-26-0009 · Cited by 5

Machine Learning Guided Prediction of Superhard Materials Based on Compositional Features

Nam, C. (2022) · 대한금속재료학회지 · doi:10.3365/kjmm.2022.60.8.619 · Cited by 5

A holistic air monitoring dataset with complaints and POIs for anomaly detection and interpretability tracing

Liu, X., Zhi, X., Zhou, T., Zhao, L., Tian, L., Gao, R., Luo, J., Cui, W., & Wang, Q. (2025) · Scientific Data · doi:10.1038/s41597-025-05591-8 · Cited by 5

A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data

Sun, H., Zhou, Q., Shi, L., Li, C., Qin, S., Yao, D., Xu, M., Huang, Y., Hu, Q., & Guan, Y. (2025) · Remote Sensing · doi:10.3390/rs17243976 · Cited by 4

Leveraging machine learning to predict water flowback in tight-gas wells: Insights from low-pressure reservoirs in the Ordos Basin

Cheng, Y., Fu, Y., Li, Z., & Hong, K. (2025) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2025.214190 · Cited by 3

Anomaly-based intrusion detection leveraging optimized firewall log analysis: a real-time machine learning solution

Hung, T. C., Linh, D. M., Chau, H. M., Thoai, N. X., Phuong, T. D., & Thu, H. D. (2025) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v15i5.pp4785-4802 · Cited by 3

Tree-Driven Intelligent Security System for Intrusion Detection in IoT environment

Choukhairi, M., M’Haouach, M., Fakhri, Y., & Amnai, M. (2023) · International Conference on Wireless Networks and Mobile Communications (WINCOM) · doi:10.1109/wincom59760.2023.10322954 · Cited by 3

Intelligent evaluation of mean cutting force of conical pick by boosting trees and Bayesian optimization

Liu, Z.-D., Liu, Y., Sun, J., Yang, J., Yang, B., & Li, D. (2024) · Journal of Central South University · doi:10.1007/s11771-024-5659-7 · Cited by 3

Front-to-Side Hard and Soft Biometrics for Augmented Zero-Shot Side Face Recognition

Alsubhi, A. H., & Jaha, E. S. (2025) · Sensors · doi:10.3390/s25061638 · Cited by 3

Enhancing Intrusion Detection System for Software-Defined Networks Based on Machine Learning

Wahib, H. A., Abdullah, M. Z., & Mohammad, A. S. (2025) · Ingénierie des systèmes d information · doi:10.18280/isi.300418 · Cited by 3

Prediction of Railway Freight Customer Churn Based on Deep Forest

Liu, D., Zhang, X., Shi, Y., & Li, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84529-2_40 · Cited by 3

Hyperparameter Tuning for Edge-IIoT Intrusion Detection Using SMOTE

Thiyam, B., & Dey, S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-2100-3_32 · Cited by 2

Machine learning-based migraine analysis using retinal vessel diameters from optical coherence tomography: an alternative approach

Orhanbulucu, F., Ünlü, M., Sevim, D. G., Gültekin, M., & Latifoğlu, F. (2025) · Neurological Sciences · doi:10.1007/s10072-025-08462-7 · Cited by 2

Feature Selection on Grouping Students Into Lab Specializations for the Final Project Using Fuzzy C-Means

Rahmatullah, I., Nugraha, G. S., & Aranta, A. (2023) · Matrik Jurnal Manajemen Teknik Informatika dan Rekayasa Komputer · doi:10.30812/matrik.v23i1.3341 · Cited by 2

A Novel Feature Selection Algorithm for the Detection of Obstructive Sleep Apnea by Using Heart Rate Variability and ECG Derived Respiratory Analysis

Padhy, A. P., Pratyasha, P., Gupta, S., Pal, K., & Mishra, S. K. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-54547-4_18 · Cited by 1

Predictive Modeling of Genetic Diseases: A Comparison of Various Machine Learning Approaches

Sinha, R., & Kulkarni, P. V. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-86296-0_20 · Cited by 1

An evaluation of improved gradient boosting models for predicting dynamic normal stress on silo wall

Xu, Z., Guo, H., Huang, H., Zhao, L., & Ding, Y. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.106961 · Cited by 1

Matrix Pearson Correlation Feature Selection and ESPRT for DDoS Anomaly Detection

Ali, B. H., Al-Rawe, K. M., Kwad, A. M., Abdulkareem, O. A., Sulaiman, N., & Tariq, S. M. (2025) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.13223 · Cited by 1

Machine Learning Models for Estimation of Solubility for A Wide Range of Solutes in Multiple Solvents Using Molecular Descriptors

Deshpande, S. R., & Rani, K. Y. (2025) · Journal of Solution Chemistry · doi:10.1007/s10953-025-01495-8 · Cited by 1

Hybrid Approach of Pearson Correlation and PCA in Feature Selection for Opinion Mining

Romadloni, N. T., Kurniawan, W. I., Ariyadi, M. Y., & Efendi, B. (2025) · IJID (International Journal on Informatics for Development) · doi:10.14421/ijid.2025.5195 · Cited by 1

Appearance of Quantitative Characters and Character Correlation in Corn Lines Resulting from Selfing under Dryland Conditions

Suliartini, N. W. S., Sutresna, I. W., Sudika, I. W., Nufus, N. H., & Nuraida, W. O. (2026) · Jurnal Penelitian Pendidikan IPA · doi:10.29303/jppipa.v12i2.12862 · Cited by 1

Application of multimodal data fusion model GAT-Net in prediction of soluble solids content in mango

Huang, S., Zheng, J., Chen, S., Fu, J., Xie, J., Zhou, X., He, Y., Xia, K., Dai, D., & Wang, W. (2026) · Journal of Food Composition and Analysis · doi:10.1016/j.jfca.2026.109177 · Cited by 1

Regional Classification of Indonesian Folk Songs Based on K-NN and PCC-LDA Model

Mahardhika, F., Warnars, H. L. H. S., Nugroho, A. S., & Budiharto, W. (2023) · International Conference on Computer Science, Information Technology and Engineering (ICCoSITE) · doi:10.1109/iccosite57641.2023.10127794 · Cited by 1

Feature Selection: Traditional and Wrapping Techniques with Tabu Search

Benito-Epigmenio, L., Martínez, S. I., Ponce-Flores, M. P., & Rocha, J. A. C. (2023) · Studies in big data · doi:10.1007/978-3-031-40688-1_2 · Cited by 1

Shannon entropy based DDoS attacks detection using combination of machine learning based feature importance techniques

Ali, B. H., Sulaiman, N., Al-Haddad, S. A. R., Atan, R. B., Hassan, S. L. M., Al-Khafaji, A. J. A., Sjarif, N. N. A., & Al-Hashimi, A. S. (2025) · AIP conference proceedings · doi:10.1063/5.0257765 · Cited by 1

Quantitative Structure–Property Relationship for High Flash Point of Ester Oil Molecules and Its Experimental Verification

zhang, J., Hao, J., Åstrand, P., Li, H., Zhou, H., & Liao, R. (2026) · High Voltage · doi:10.1049/hve2.70208

A new strategy for constructing alternative consumer confidence indexes to explain household consumption: A fuzzy DEMATEL approach

Var, Ö., Durmuşoğlu, A., & Dereli, T. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e41447

Permissions-based detection of Malware for android environment using CNN model

Al-Gharrawi, A. H., & Alazawi, S. A. (2025) · AIP conference proceedings · doi:10.1063/5.0259008

Classification of University Students Using Feature Selection and Wrapping Methods in a Pattern Recognition System

Padilla, C. C. M. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-78440-8_14

Using a Combination of Effective Feature Selection Methods and an Entropy-based Approach to Identify DDoS Anomalies

Ali, B. H., Al-Rawe, K. M., Ahmed, M. A., Al-Khafaji, A. J. A., & Sulaiman, N. (2025) · Al-Iraqia Journal of Scientific Engineering Research · doi:10.58564/ijser.4.3.2025.319

Feature Selection Method for Intrusion Detection System Based on Enhanced Hybrid Genetic Algorithm

Wei, W., Chen, L., & Li, Q. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228070

Clima organizacional y manejo de conflictos en la Municipalidad Distrital de Huaura - 2023

Azaña Chiroque, R. A. (2023) · renati

APPLYING ARTIFICIAL INTELLIGENCE METHODS TO DETECT NETWORK TRAFFIC ANOMALIES

Dzhsupbekova, G., Ordabayeva, G., Beketova, A., & Baispay, G. (2025) · Вестник Академии гражданской авиации · doi:10.53364/24138614_2025_38_3_9

Introduction to Machine Learning Algorithms

Khanna, V. K. (2026) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003423089

Algoritmo para reducir características radiómicas en la clasificación de nódulos pulmonares

Llanes, R. A., Díaz, M. P., Ruiz-González, Y., Cañizares, D. G., & Orozco‐Morales, R. (2026) · Revista científica de ingeniería electrónica, automática y comunicaciones/Ingeniería electrónica, automática y comunicaciones

MACHINE LEARNING STRATEGIES FOR EARLY PREDICTION OF STRUCTURALEMBODIED EMISSIONS

Fenton, S. K. (2024) · theses.fr (ABES) · doi:10.70675/bb594476zf383z4009za18bz3692e2420028

Enhancing Software Defect Prediction through Hybrid Multi-Filter Feature Selection and Imbalance Handling

Maulana, M. R. A., Saputro, S. W., Faisal, M. R., Nugroho, R. A., & Ramadhan, A. (2026) · Journal of Computing Theories and Applications · doi:10.62411/jcta.15943

Hybrid Intelligent Detection Approach for Android Malware Using Gradient-Boosting Tree Ensembles and Correlation–Differential Evolution Feature Selection

Ali, W. (2026) · Information · doi:10.3390/info17060534

Leveraging defense-in-depth through a deception-driven security model for smart university

Naagas, M. A., Gamilla, A. P., & Rabang, M. C. (2026) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v15i3.11299

Seeing Threats Without Looking: An Encrypted Anomaly Detection Framework for Privacy-Aware Intrusion Detection

Schäfer, J. M., & Wiese, L. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-28957-5_15

Detection of different types of distributed denial of service attacks using multiple features of entropy and sequential probability ratio test

Al-Mafrachi, B. H. A. (2024) · Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia)

Analysis of Manufacturing Industry Capacity Utilization Rate Forecasts Using Decision Tree-Based Algorithms

Filiz, E. (2026) · Turkish Journal of Forecasting · doi:10.34110/forecasting.1863623

Feature-Centric Framework for Network Traffic Management and Optimization

Alawadi, A. H. (2025) · Al-Furat Journal of Innovations in Electronics and Computer Engineering

Synthesis and Characterization of Composite Textured Surfaces with Enhanced Tribological Responses on Icy Surfaces

Zahmatkesh, S. (2026) · York University Digital Library (York University)

Correlation-Based Feature Selection for Intrusion Detection Design

Chou, T., Yen, K. K., Luo, J., Pissinou, N., & Makki, K. (2007) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom.2007.4454806 · Cited by 32

Feature Selection Using Pearson Correlation with Lasso Regression for Intrusion Detection System

Putro, I. H., & Ahmad, T. (2024) · International Symposium on Digital Forensics and Security (ISDFS) · doi:10.1109/isdfs60797.2024.10527338 · Cited by 12

Network intrusion detection using data dimensions reduction techniques

Shiravani, A., Sadreddini, M. H., & Nahook, H. N. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00697-5 · Cited by 41

A New Approach Feature Selection for Intrusion Detection System Using Correlation Analysis

Hostiadi, D. P., Atmojo, Y. P., Huizen, R. R., Susila, I. M. D., Pradipta, G. A., & Liandana, M. (2022) · International Conference on Cybernetics and Intelligent System (ICORIS) · doi:10.1109/icoris56080.2022.10031468 · Cited by 8

Pearson Correlation Attribute Evaluation-based Feature Selection for Intrusion Detection System

Sugianela, Y., & Ahmad, T. (2020) · International Conference on Smart Technology and Applications (ICoSTA) · doi:10.1109/icosta48221.2020.1570613717 · Cited by 36