SwiftIDS: Real-time intrusion detection system based on LightGBM and parallel intrusion detection mechanism

MapReduce: simplified data processing on large clusters

Dean, J., & Ghemawat, S. (2008) · Communications of the ACM · doi:10.1145/1327452.1327492 · Cited by 18,476

LightGBM: A Highly Efficient Gradient Boosting Decision Tree

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Extreme Learning Machine for Regression and Multiclass Classification

Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2168604 · Cited by 5,575

A detailed analysis of the KDD CUP 99 data set

Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009) · 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications · doi:10.1109/cisda.2009.5356528 · Cited by 5,396

Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization

Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A. (2018) · Proceedings of the 4th International Conference on Information Systems Security and Privacy · doi:10.5220/0006639801080116 · Cited by 5,318

Characterization of Tor Traffic using Time based Features

Lashkari, A. H., Gil, G. D., Mamun, M., & Ghorbani, A. A. (2017) · International Conference on Information Systems Security and Privacy (ICISSP) · doi:10.5220/0006105602530262 · Cited by 926

The 1999 DARPA off-line intrusion detection evaluation

Lippmann, R. P., Haines, J. W., Fried, D. J., Korba, J., & Das, K. (2000) · Computer Networks · doi:10.1016/s1389-1286(00)00139-0 · Cited by 877

On Grouping for Maximum Homogeneity

Fisher, W. D. (1958) · Journal of the American Statistical Association · doi:10.1080/01621459.1958.10501479 · Cited by 796

HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection

Wang, W., Sheng, Y., Wang, J., Zeng, X., Ye, X., Huang, Y., & Zhu, M. (2017) · IEEE Access · doi:10.1109/access.2017.2780250 · Cited by 685

Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

Ambusaidi, M. A., He, X., Nanda, P., & Tan, Z. (2016) · IEEE Transactions on Computers · doi:10.1109/tc.2016.2519914 · Cited by 648

Multimodal machine learning in precision health: A scoping review

Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., & Luo, Y. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00712-8 · Cited by 528

Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM)

Rufo, D. D., Debelee, T. G., Ibenthal, A., & Negera, W. G. (2021) · Diagnostics · doi:10.3390/diagnostics11091714 · Cited by 317

The Comparison of LightGBM and XGBoost Coupling Factor Analysis and Prediagnosis of Acute Liver Failure

Zhang, D., & Gong, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3042848 · Cited by 241

An improved anomaly detection model for IoT security using decision tree and gradient boosting

Douiba, M., Benkirane, S., Guezzaz, A., & Azrour, M. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-022-04783-y · Cited by 194

Zero-day attack detection: a systematic literature review

Ahmad, R., Alsmadi, I. M., Al-Hamdani, W. A., & Tawalbeh, L. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10437-z · Cited by 228

A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams

Yang, L., & Shami, A. A. (2021) · IEEE Internet of Things Magazine · doi:10.1109/iotm.0001.2100012 · Cited by 172

E-XAI: Evaluating Black-Box Explainable AI Frameworks for Network Intrusion Detection

Arreche, O., Guntur, T. R., Roberts, J. W., & Abdallah, M. (2024) · IEEE Access · doi:10.1109/access.2024.3365140 · Cited by 153

Practical early prediction of students’ performance using machine learning and eXplainable AI

Jang, Y., Choi, S., Jung, H., & Kim, H. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11120-6 · Cited by 114

lIDS-SIoEL: intrusion detection framework for IoT-based smart environments security using ensemble learning

Hazman, C., Guezzaz, A., Benkirane, S., & Azrour, M. (2022) · Cluster Computing · doi:10.1007/s10586-022-03810-0 · Cited by 105

5G Security Challenges and Solutions: A Review by OSI Layers

Sullivan, S., Brighente, A., Kumar, S., & Conti, M. (2021) · IEEE Access · doi:10.1109/access.2021.3105396 · Cited by 100

Deep residual convolutional neural Network: An efficient technique for intrusion detection system

Kumar, G. S. C., Kumar, R. K., Kumar, K. P. V., Sai, N. R., & Brahmaiah, M. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121912 · Cited by 89

Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning

Lai, Y., Lin, P., Lin, F., Chen, M., Lin, C., Lin, X., Wu, L., Zheng, M., & Chen, J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1046410 · Cited by 87

An intrusion detection approach using ensemble Support Vector Machine based Chaos Game Optimization algorithm in big data platform

Ponmalar, A., & Dhanakoti, V. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.108295 · Cited by 79

Building an efficient intrusion detection system using grasshopper optimization algorithm for anomaly detection

Dwivedi, S., Vardhan, M., & Tripathi, S. (2021) · Cluster Computing · doi:10.1007/s10586-020-03229-5 · Cited by 74

Boosting-Based Machine Learning Applications in Polymer Science: A Review

Malashin, I. P., Тынченко, В. С., Gantimurov, A., Nelyub, V., & Бородулин, А. С. (2025) · Polymers · doi:10.3390/polym17040499 · Cited by 71

A novel flood risk management approach based on future climate and land use change scenarios

Nguyen, H. D., Nguyen, Q., Dang, D. K., Van, C. P., Trương, Q. H., Pham, S. D., Bui, Q., & Petrişor, A.-I. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.171204 · Cited by 68

A novel optimized probabilistic neural network approach for intrusion detection and categorization

Omer, N., Samak, A. H., Taloba, A. I., & El-Aziz, R. M. A. (2023) · Alexandria Engineering Journal · doi:10.1016/j.aej.2023.03.093 · Cited by 64

A supplier selection & order allocation planning framework by integrating deep learning, principal component analysis, and optimization techniques

Islam, S., Amin, S. H., & Wardley, L. J. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121121 · Cited by 63

A Novel Ensemble Framework for an Intelligent Intrusion Detection System

Seth, S., Chahal, K. K., & Singh, G. (2021) · IEEE Access · doi:10.1109/access.2021.3116219 · Cited by 60

Machine Learning for Detecting Anomalies and Intrusions in Communication Networks

Li, Z., Rios, A. L. G., & Trajković, L. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3078497 · Cited by 61

A Forest Fire Susceptibility Modeling Approach Based on Light Gradient Boosting Machine Algorithm

Sun, Y., Zhang, F., Lin, H., & Xu, S. (2022) · Remote Sensing · doi:10.3390/rs14174362 · Cited by 57

A Light Gradient-Boosting Machine algorithm with Tree-Structured Parzen Estimator for breast cancer diagnosis

Omotehinwa, T. O., Oyewola, D. O., & Dada, E. G. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100218 · Cited by 56

Multi-class imbalanced enterprise credit evaluation based on asymmetric bagging combined with light gradient boosting machine

Sun, J., Li, J., & Fujita, H. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109637 · Cited by 53

Anomaly detection model based on gradient boosting and decision tree for IoT environments security

Douiba, M., Benkirane, S., Guezzaz, A., & Azrour, M. (2022) · Journal of Reliable Intelligent Environments · doi:10.1007/s40860-022-00184-3 · Cited by 52

Baseline energy modeling for improved measurement and verification through the use of ensemble artificial intelligence models

Sarmas, E., Forouli, A., Marinakis, V., & Doukas, H. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119879 · Cited by 52

A tfidfvectorizer and singular value decomposition based host intrusion detection system framework for detecting anomalous system processes

Subba, B., & Gupta, P. (2020) · Computers & Security · doi:10.1016/j.cose.2020.102084 · Cited by 50

Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-Supervised Learning Approach

Xiao, Y., Xia, R., Li, Y., Shi, G., Nguyen, D. N., Hoang, D. T., Niyato, D. T., & Krunz, M. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3240821 · Cited by 52

Estimating the Energy Savings of Energy Efficiency Actions with Ensemble Machine Learning Models

Sarmas, E., Spiliotis, E., Dimitropoulos, N., Marinakis, V., & Doukas, H. (2023) · Applied Sciences · doi:10.3390/app13042749 · Cited by 48

A novel time efficient learning-based approach for smart intrusion detection system

Seth, S., Singh, G., & Chahal, K. K. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00498-8 · Cited by 48

Design of an Intrusion Detection Model for IoT-Enabled Smart Home

Rani, D., Gill, N. S., Gulia, P., Arena, F., & Pau, G. (2023) · IEEE Access · doi:10.1109/access.2023.3276863 · Cited by 46

Tunnel deformation prediction during construction: An explainable hybrid model considering temporal and static factors

Li, Z., Ma, E., Lai, J., & Su, X. (2024) · Computers & Structures · doi:10.1016/j.compstruc.2024.107276 · Cited by 46

Enhancing Cloud Security with Deep Learning-Based Intrusion Detection in Cloud Computing Environments

Devi, T. A., & Jain, A. (2024) · International Conference on Advancement in Computation & Computer Technologies (InCACCT) · doi:10.1109/incacct61598.2024.10551040 · Cited by 42

Edge-featured multi-hop attention graph neural network for intrusion detection system

Deng, P., & Huang, Y. H. (2024) · Computers & Security · doi:10.1016/j.cose.2024.104132 · Cited by 41

Leveraging LightGBM for Categorical Big Data

Hancock, J., & Khoshgoftaar, T. M. (2021) · IEEE International Conference on Big Data Computing Service and Applications (BigDataService) · doi:10.1109/bigdataservice52369.2021.00024 · Cited by 41

A Deep Learning Ensemble Approach to Detecting Unknown Network Attacks

Ahmad, R., Alsmadi, I. M., Al-Hamdani, W. A., & Tawalbeh, L. (2022) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2022.103196 · Cited by 44

Malicious traffic detection combined deep neural network with hierarchical attention mechanism

Liu, X., & Liu, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-91805-z · Cited by 38

Prediction of biogas production in anaerobic digestion of a full‐scale wastewater treatment plant using ensembled machine learning models

Sun, J., Xu, Y., Nairat, S., Zhou, J., & He, Z. (2023) · Water Environment Research · doi:10.1002/wer.10893 · Cited by 40

Advancing Snort IPS to Achieve Line Rate Traffic Processing for Effective Network Security Monitoring

Sheeraz, M., Durad, M. H., Tahir, S., Tahir, H., Saeed, S., & Almuhaideb, A. M. (2024) · IEEE Access · doi:10.1109/access.2024.3395123 · Cited by 38

Hybrid Neural Network-Based Intrusion Detection System: Leveraging LightGBM and MobileNetV2 for IoT Security

Yang, Y.-M., Chang, K.-C., & Luo, J.-N. (2025) · Symmetry · doi:10.3390/sym17030314 · Cited by 38

Unknown Security Attack Detection Using Shallow and Deep ANN Classifiers

Al‐Zewairi, M., Almajali, S., & Ayyash, M. (2020) · Electronics · doi:10.3390/electronics9122006 · Cited by 39

Improved Transformer-Based Privacy-Preserving Architecture for Intrusion Detection in Secure V2X Communications

Lai, Q., Chen, X., Chen, J., Wang, W. W., Chen, J., Gadekallu, T. R., Cai, M., & Hu, X. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3324081 · Cited by 40

A Two-Level Ensemble Learning Framework for Enhancing Network Intrusion Detection Systems

Arreche, O., Bibers, I., & Abdallah, M. (2024) · IEEE Access · doi:10.1109/access.2024.3407029 · Cited by 35

Lightweight real-time WiFi-based intrusion detection system using LightGBM

Bhutta, A. A., Nisa, M. U., & Mian, A. N. (2023) · Wireless Networks · doi:10.1007/s11276-023-03516-0 · Cited by 35

A smart model integrating LSTM and XGBoost for improving IoT-enabled smart cities security

Hazman, C., Guezzaz, A., Benkirane, S., & Azrour, M. (2024) · Cluster Computing · doi:10.1007/s10586-024-04780-1 · Cited by 34

A Transfer Learning Method for the Protection of Geographical Indication in China Using an Electronic Nose for the Identification of Xihu Longjing Tea

Wang, X., Gu, Y., & Liu, H. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3048534 · Cited by 34

Hybrid intrusion detection system based on Dempster-Shafer evidence theory

Weicheng, Q., Yinghua, M., Chen, X., Yu, H., & Chen, L. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102709 · Cited by 34

An Ensemble-Based Multiclass Classifier for Intrusion Detection Using Internet of Things

Rani, D., Gill, N. S., Gulia, P., & Chatterjee, J. M. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/1668676 · Cited by 34

An improved sparrow search algorithm optimized LightGBM approach for credit risk prediction of SMEs in supply chain finance

Hou, L., Bi, G., & Guo, Q. (2024) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2024.116197 · Cited by 33

A Survey on Multi-Agent Based Collaborative Intrusion Detection Systems

Bougueroua, N., Mazouzi, S., Belaoued, M., Seddari, N., Derhab, A., & Bouras, A. (2021) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.2478/jaiscr-2021-0008 · Cited by 37

Prediction of compressive strength and tensile strain of engineered cementitious composite using machine learning

Uddin, M. N., Shanmugasundaram, N., Praveenkumar, S., & Li, L. (2024) · International Journal of Mechanics and Materials in Design · doi:10.1007/s10999-023-09695-0 · Cited by 32

A FKPCA-GWO WDBiLSTM classifier for intrusion detection system in cloud environments

Geetha, T. V., & Deepa, A. J. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109557 · Cited by 30

Research on fault diagnosis system for belt conveyor based on internet of things and the LightGBM model

Wang, M., Shen, K., Tai, C., Zhang, Q., Yang, Z., & Guo, C. (2023) · PLoS ONE · doi:10.1371/journal.pone.0277352 · Cited by 32

XI2S-IDS: An Explainable Intelligent 2-Stage Intrusion Detection System

Mahmoud, M. M., Omar, Y. M. K., & Abdel-Hamid, A. (2025) · Future Internet · doi:10.3390/fi17010025 · Cited by 28

EFS-DNN: An Ensemble Feature Selection-Based Deep Learning Approach to Network Intrusion Detection System

Wang, Z., Liu, J. H., & Sun, L. (2022) · Security and Communication Networks · doi:10.1155/2022/2693948 · Cited by 28

Unsupervised online anomaly detection in Software Defined Network environments

Scaranti, G. F., Carvalho, L. F., Barbon, S., Lloret, J., & Proença, M. L. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116225 · Cited by 27

An integrated intrusion detection framework based on subspace clustering and ensemble learning

Zhu, J., & Liu, X. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109113 · Cited by 24

Enhanced Network Intrusion Detection System

Kotecha, K. V., Verma, R., Rao, P. V., Prasad, P., Mishra, V. K., Badal, T., Jain, D., Garg, D., & Sharma, S. (2021) · Sensors · doi:10.3390/s21237835 · Cited by 23

A High-dimensional Outlier Detection Approach Based on Local Coulomb Force

Zhu, P., Zhang, C., Li, X., Zhang, J., & Qin, X. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3172167 · Cited by 23

Status Forecast and Fault Classification of Smart Meters Using LightGBM Algorithm Improved by Random Forest

Tao, P., Shen, H., Zhang, Y., Ren, P., Zhao, J., & Jia, Y. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/3846637 · Cited by 20

Intrusion Detection Using Hybrid Enhanced CSA-PSO and Multivariate WLS Random-Forest Technique

Mohi-ud-din, G., Zhiqiang, L., Zheng, J., Sifei, W., Zhijun, L., Asim, M., Zhong, Y., & Chen, Y. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3258901 · Cited by 20

Effects of Rebar Size and Volume Fraction of Glass Fibers on Tensile Strength Retention of GFRP Rebars in Alkaline Environment via RSM and SHAP Analyses

Iqbal, M., Zhang, D., Khan, M. I., Zahid, M., & Jalal, F. E. (2023) · Journal of Materials in Civil Engineering · doi:10.1061/jmcee7.mteng-15589 · Cited by 20

An Explainable Evaluation Model for Building Thermal Comfort in China

Liu, H., & Ma, E. (2023) · Buildings · doi:10.3390/buildings13123107 · Cited by 22

Probe Attack Detection Using an Improved Intrusion Detection System

Almazyad, A. S., Halman, L. M., & Alsaeed, A. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.033382 · Cited by 19

Integrated explainable machine learning and multi-omics analysis for survival prediction in cancer with immunotherapy response

Hounye, A. H., Xiong, L., & Hou, M. (2024) · APOPTOSIS · doi:10.1007/s10495-024-02050-4 · Cited by 19

Real-Time Intrusion Detection and Prevention with Neural Network in Kernel Using eBPF

Zhang, J., Chen, P., He, Z., Chen, H., & Li, X. (2024) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn58291.2024.00048 · Cited by 18

Few-Shot Learning-Based Network Intrusion Detection through an Enhanced Parallelized Triplet Network

Tian, J., Wang, Z., Fang, H., Chen, L., Qin, J., Chen, J., & Wang, Z. (2022) · Security and Communication Networks · doi:10.1155/2022/3317048 · Cited by 17

Harris hawk optimization trained artificial neural network for anomaly based intrusion detection system

Narengbam, L., & Dey, S. (2023) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7771 · Cited by 16

Optimized Artificial Neural Network Techniques to Improve Cybersecurity of Higher Education Institution

ALGhamdi, A. S. A.-M., Ragab, M., Sabir, M. F. S., Elhassanein, A., & Gouda, A. A. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.026477 · Cited by 16

Machine learning modeling for predicting adherence to physical activity guideline

Choe, J.-P., Lee, S., & Kang, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-90077-1 · Cited by 17

An Enhanced Hybrid Intrusion Detection Based on Crow Search Analysis Optimizations and Artificial Neural Network

Gupta, C., Kumar, A., & Jain, N. K. (2024) · Wireless Personal Communications · doi:10.1007/s11277-024-10880-3 · Cited by 15

AI-based malware detection in IoT networks within smart cities: A survey

Alhamdi, M. J. M., López-Guede, J. M., AlQaryouti, J., Rahebi, J., Zulueta, E., & Fernández‐Gámiz, U. (2025) · Computer Communications · doi:10.1016/j.comcom.2025.108055 · Cited by 14

A Review on Recent Intrusion Detection Systems and Intrusion Prevention Systems in IoT

Prajapati, P., Bhatt, B., Zalavadiya, G., Ajwalia, M., & Shah, P. D. (2021) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence51648.2021.9377202 · Cited by 14

Ensemble Learning Framework for Anomaly Detection in Autonomous Driving Systems

Nazat, S., Alayed, W. M., Li, L., & Abdallah, M. (2025) · Sensors · doi:10.3390/s25165105 · Cited by 15

Sensitivity analysis and comparative assessment of novel hybridized boosting method for forecasting the power consumption

Zhou, J., Wang, Q., Khajavi, H., & Rastgoo, A. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123631 · Cited by 13

A Robust Security Detection Strategy for Next Generation IoT Networks

Assmi, H., Guezzaz, A., Benkirane, S., Azrour, M., Jabbour, S., Innab, N., & Alabdulatif, A. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.059047 · Cited by 16

Hybrid Deep Learning framework-based intrusion detection system for the Internet of Things

Kirubavathi, G., & Nair, A. R. (2024) · International Conference on Intelligent Systems for Cybersecurity (ISCS) · doi:10.1109/iscs61804.2024.10581228 · Cited by 14

Stacking Enabled Ensemble Learning Based Intrusion Detection Scheme (SELIDS) for IoV

Singh, A. P., Chaurasia, B. K., & Tripathi, A. (2024) · SN Computer Science · doi:10.1007/s42979-024-03376-1 · Cited by 12

LightGBM-based Ransomware Detection using API Call Sequences

Nguyen, D. T., & Lee, S. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0121016 · Cited by 12

X-Band Radar Attenuation Correction Method Based on LightGBM Algorithm

Yang, Q., Feng, Y., Guan, L., Wu, W., Wang, S., & Li, Q. (2023) · Remote Sensing · doi:10.3390/rs15030864 · Cited by 11

Comparing fatal crash risk factors by age and crash type by using machine learning techniques

Alshehri, A. H., Alanazi, F., Yosri, A. M., & Yasir, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0302171 · Cited by 10

Ensemble-IDS: An Ensemble Learning Framework for Enhancing AI-Based Network Intrusion Detection Tasks

Bibers, I., Arreche, O., Alayed, W. M., & Abdallah, M. (2025) · Applied Sciences · doi:10.3390/app151910579 · Cited by 10

Meerkat Clan-Based Feature Selection in Random Forest Algorithm for IoT Intrusion Detection

Hussein, A. Y., & Sadiq, A. T. (2022) · Iraqi Journal of Computer Communication Control and System Engineering · doi:10.33103/uot.ijccce.22.3.2 · Cited by 10

A weighted intrusion detection model of dynamic selection

Feng, T., & Dou, M. (2021) · Applied Intelligence · doi:10.1007/s10489-020-02090-8 · Cited by 9

Unveiling encrypted traffic types through hierarchical network characteristics

Chen, Y., Yang, J., Cui, S., Dong, C., Jiang, B., Liu, Y., & Lü, Z. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103645 · Cited by 9

Walking motion real-time detection method based on walking stick, IoT, COPOD and improved LightGBM

Wang, J., Jiang, X., Meng, Q., Saada, M., & Cai, H. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03264-2 · Cited by 10

Machine Learning Based Intrusion Detection System

Udayakumar, R., Balakrishnan, D., Reddy, Y. V., Prabhakar, P. B. E., & Thilaka, A. (2023) · International Conference on Technological Advancements in Computational Sciences (ICTACS) · doi:10.1109/ictacs59847.2023.10389883 · Cited by 9

Intrusion detection method based on imbalanced learning classification

Li, X., Kong, K., Shen, H., Wei, Z., & Liao, X. (2022) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2022.2104384 · Cited by 10

An Intrusion Detection System for the Internet of Things Based on the Ensemble of Unsupervised Techniques

Wang, Y., Sun, G., Cao, X., & Yang, J. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/8614903 · Cited by 11

IoT Intrusion Detection Using Modified Random Forest Based on Double Feature Selection Methods

Hussein, A. Y., Falcarin, P., & Sadiq, A. T. (2022) · Communications in computer and information science · doi:10.1007/978-3-030-97255-4_5 · Cited by 8

Two-Dimensional Projection-Based Wireless Intrusion Classification Using Lightweight EfficientNet

Aminanto, M. E., Purbomukti, I. R., Chandra, H., & Kim, K. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.026749 · Cited by 8

Shale gas well productivity potential evaluation based on data-driven methods: case study in the WY block

Tan, C., Deng, H., Song, W., Niu, H., & Wang, C. (2022) · Journal of Petroleum Exploration and Production Technology · doi:10.1007/s13202-022-01455-8 · Cited by 7

Finding Harmony in the Noise: Blending Security Alerts for Attack Detection

Roelofs, T., Bárbaro, E., Pekarskikh, S., Orzechowska, K., Kwapień, M., Tyrlik, J., Smadu, D., van Eeten, M. J. G., & Zhauniarovich, Y. (2024) · ACM Symposium on Applied Computing · doi:10.1145/3605098.3635981 · Cited by 7

A Scalable Framework for Real-Time Network Security Traffic Analysis and Attack Detection Using Machine and Deep Learning

Maasaoui, Z., Merzouki, M., Battou, A., & Lbath, A. (2025) · Platforms · doi:10.3390/platforms3020007 · Cited by 12

A Hybrid Network Intrusion Detection Model Based on CNN-LSTM and Attention Mechanism

Mu, J., He, H., Li, L., Pang, S., & Liu, C. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-0523-0_14 · Cited by 7

Enhancing Cyber Defense: Using Machine Learning Algorithms for Detection of Network Anomalies

Li, Z., & Trajković, L. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10394572 · Cited by 7

Network Intrusion Detection to Mitigate Jamming and Spoofing Attacks Using Federated Leading

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