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

(2017) · HAL · Cited by 10,512

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

LightGBM robust optimization algorithm based on topological data analysis

Yang, H., Qin, G., Liu, Z. Y., Hu, Y., & Dai, Q. (2024) · International Conference on Computer and Multimedia Technology (ICCMT) · doi:10.1145/3675249.3675350 · Cited by 6

Intrusion detection framework using auto‐metric graph neural network optimized with hybrid woodpecker mating and capuchin search optimization algorithm in IoT network

Govindaraju, S., Vinisha, W. V. R., Shajin, F. H., & Sivasakthi, D. A. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7197 · Cited by 6

Cloud Intrusion Detection Model Based on Deep Belief Network and Grasshopper Optimization

Parganiha, V., Shukla, S. P., & Sharma, L. K. (2022) · International Journal of Ambient Computing and Intelligence · doi:10.4018/ijaci.293123 · Cited by 6

Anomaly Detection - Recent Advances, AI and ML Perspectives and Applications

Krishna Parimala, V. (2023) · Artificial intelligence · doi:10.5772/intechopen.110988 · Cited by 5

Real Network DDoS Pattern Analysis and Detection

Hsu, Y.-F., Araki, R., & Matsuoka, M. (2022) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac54236.2022.00236 · Cited by 5

Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48

Otoom, M. M., Sattar, K. N. A., & Sadig, M. A. (2023) · Advances in Science and Technology – Research Journal · doi:10.12913/22998624/161820 · Cited by 5

FSEGM: feature selection and ensemble generative model for adaptive cloud security

Rout, C., Sethi, S., Badajena, J. C., & Sahoo, R. K. (2025) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-025-00818-w · Cited by 4

SDC Error Detection by Exploring the Importance of Instruction Features

Fang, W., Gu, J., Yan, Z., & Wang, Q. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85928-2_28 · Cited by 4

Remote State Estimation in Networked Systems: Data Pre-Processing or Not?

Qu, F., Al-Dabbagh, A. W., Zhao, Z., Liu, H., & Li, Y. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3275971 · Cited by 4

AIGCN: Attack Intention Detection for Power System Using Graph Convolutional Networks

Tang, Q., Chen, H., Ge, B., & Wang, H. (2022) · Journal of Signal Processing Systems · doi:10.1007/s11265-021-01724-5 · Cited by 4

Deep Security Analysis Model for Smart Grid

Di, T., Wu, Y., & Li, W. B. (2022) · IEEE International Conference on Information, Communication and Networks (ICICN) · doi:10.1109/icicn56848.2022.10006496 · Cited by 4

Default prediction of online credit loans based on mobile application usage behaviors

Xu, M., Tian, B., & Fu, Y. (2022) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-211825 · Cited by 4

An Integrated IDS Using ICA-Based Feature Selection and SVM Classification Method

Ogundokun, R. O., Misra, S., Bajeh, A. O., Okoro, U. O., & Ahuja, R. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-93453-8_11 · Cited by 4

Early Detection of Campus Network DDoS Attacks using Predictive Models

Araki, R., Hsu, Y.-F., & Matsuoka, M. (2022) · Globecom · doi:10.1109/globecom48099.2022.10000974 · Cited by 3

ESVI-GaMM: A fast network intrusion detection approach based on the Bayesian gamma mixture model

He, W., Cai, X., Lai, Y., & Yuan, X. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121001 · Cited by 3

IDU-Detector: A Synergistic Framework for Robust Masquerader Attack Detection

Huang, Z., Li, X., Cao, X., Chen, K., Wang, L., & Liu, L. B.-Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3503057 · Cited by 4

Intrusion Detection Method Based on Genetic Algorithm of Optimizing LightGBM

Li, Z., & Li, X. (2021) · International Conference on Electronic Information Technology and Computer Engineering (EITCE) · doi:10.1145/3501409.3501651 · Cited by 3

Anomaly Based Network Intrusion Detection System for IoT

Prajapati, G., Singh, P., & Rahul (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-6634-7_49 · Cited by 3

Secure intrusion detection system for IOT sensor data using LR-ECC and FY-SFL-DLNN algorithm

Chandrababu, M., & Senthilkumar, K. (2024) · Journal of Control and Decision · doi:10.1080/23307706.2023.2299857 · Cited by 3

Ensemble Machine Learning Models for Estimating Energy Savings from Efficiency Measures in Buildings

Sarmas, E., Marinakis, V., & Doukas, H. (2025) · Learning and analytics in intelligent systems · doi:10.1007/978-3-031-85209-1_7 · Cited by 3

Smart Intrusion Detection System Using Deep Neural Network Gated Recurrent Unit Technique

Seth, S., Singh, G. P., & Kaur, K. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-7985-8_30 · Cited by 3

An Attention-Based CNN with Batch Normalization Model for Network Intrusion Detection

Li, L., Mu, J., He, H., & Liu, C. (2021) · China Automation Congress (CAC) · doi:10.1109/cac53003.2021.9727384 · Cited by 3

Dirichlet Feature Embedding with Adaptive Long Short-Term Memory Model for Intrusion Detection System

(2022) · Journal of System and Management Sciences · doi:10.33168/jsms.2022.0424 · Cited by 3

AI Based Decision Tree Classifier for Detecting Anomalies in IoT Wireless Sensor Networks

Akhther, A. P., Sreelekha, G., & S, P. V. (2025) · International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) · doi:10.1109/icmcsi64620.2025.10883363 · Cited by 2

An intrusion detection method combining Bayesian optimization and LightGBM

Xu, D., & Hua, B. (2023) · International Conference on Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI) · doi:10.1117/12.3011542 · Cited by 2

A Feature Selection Algorithm for Intrusion Detection System Based on Binary PSO

Vengaimarbhan, D., Srinivas, B., & Praveena, M. G. (2023) · International Conference on Intelligent Computing and Control for Engineering and Business Systems (ICCEBS) · doi:10.1109/iccebs58601.2023.10448587 · Cited by 2

Real-Time Intrusion Detection with Sequence-Aware Neural Networks for Internet of Medical Things

Werneck, H., Batista, D. M., Hirata, R., & Queiroz, M. (2024) · IEEE Virtual Conference on Communications (VCC) · doi:10.1109/vcc63113.2024.10914399 · Cited by 2

Stacking-Based Ensemble Learning Method for House Price Prediction

Liu, Y., Wu, Y., Su, L., Li, W., & Lei, J. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-90318-3_22 · Cited by 2

Literature Review of Machine Learning Models on Intrusion Detection for Internet of Things Attacks

Aminanto, A. E., & Survadi, M. T. (2021) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet52533.2021.9698760 · Cited by 2

LightGBM-RF: A Hybrid Model for Anomaly Detection in Smart Building

Ekpo, O., Takyi, K., & Gyening, R.-M. O. M. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-8445-7_3 · Cited by 2

Xai-Ids: Towards Proposing an Explainable Artificial Intelligence Framework for Enhancing Network Intrusion Detection SystemsPreprint

Arreche, O., Guntur, T. R., & Abdallah, M. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4567885 · Cited by 2

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 2

Influence of Data Scaling and Train/Test Split Ratios on LightGBM Efficacy for Obesity Rate Prediction

Fahrudin, N. F., Putra, K. R., Umaroh, S., & LAUTAN, G. B. (2024) · MIND Journal · doi:10.26760/mindjournal.v9i2.220-234 · Cited by 1

Machine Learning-Driven Energy Consumption Forecasting for Building Profiling

Sarmas, E., Marinakis, V., & Doukas, H. (2025) · Learning and analytics in intelligent systems · doi:10.1007/978-3-031-85209-1_5 · Cited by 1

Optimization Accuracy of Intrusion Detection System Based on Multilayered Neural Network

Gupta, C., Kumar, A., & Jain, N. K. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-87511-3_14 · Cited by 1

A Generative AI Method for Minority Class Handling in Anomaly Detection with Drift and Explainability Analysis

Mwiga, K. J., Dida, M. A., Mohsin, A., & Sarker, I. H. (2026) · New Generation Computing · doi:10.1007/s00354-026-00318-8 · Cited by 4

Machine Learning Approach for Adaptive Data Protection in Document Database Systems

Belhaj, A., Ziti, S., Ladrham, K., Lagmiri, S. N., & Bouchti, K. E. (2026) · Journal of Trends in Computer Science and Smart Technology · doi:10.36548/jtcsst.2026.2.002 · Cited by 1

Perspicacious Apprehension of HDTbNB Algorithm Opposed to Security Contravention

Shyla, & Bhatnagar, V. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.029126 · Cited by 1

Intrus-ML: An Intrusion Detection Model Based on Machine Learning

Mohanty, A., Ghosh, S., Dash, A., & Darshana, S. (2023) · International Conference on Communication, Circuits, and Systems (IC3S) · doi:10.1109/ic3s57698.2023.10169341 · Cited by 1

Boosting Algorithms-Based Intrusion Detection System: A Performance Comparison Perspective

Prasad, A., & Chandra, S. (2023) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-99-3432-4_24 · Cited by 1

Anomaly Detection through Adaptive DASO Optimization Techniques

Bhosale, S., Deshmukh, A., Deore, B. S., & Bhosale, P. (2023) · Artificial intelligence · doi:10.5772/intechopen.112421 · Cited by 1

RCALN: A Hybrid Intrusion Detection System Incorporating Channel Attention in CNN and Residual Networks with LSTM

Chen, M., Luo, X., Zhang, Y., Duan, Z., & Li, F. (2023) · International Conference on Computer Science and Automation Technology (CSAT) · doi:10.1109/csat61646.2023.00125 · Cited by 1

Hypervisor Based Intrusion Detection Using Enhanced Radial Basis Neural Network on Cloud Environment

Anumukonda, N. S. K., Yadav, R. K., & Raghava, N. S. (2024) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi60426.2024.10503284 · Cited by 1

Intrusion detection system for Denial of Service (DoS) attacks in healthcare cybersecurity on System on Chip (SoC) – PYTHON productivity for ZYNQ (PYNQ Z1)

Sridevi, A., Janakiraman, S., & Amirtharajan, R. (2025) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-025-02019-3 · Cited by 1

Two-Dimensional Projection Based Wireless Intrusion Classification Using Lightweight EfficientNet

Tekleselassie, H. (2022) · Journal of Cyber Security and Mobility · doi:10.13052/jcsm2245-1439.1145 · Cited by 1

Let us Unveil Network Intrusion Features: Enhancing Network Intrusion Detection Systems via XAI-based Feature SelectionPreprint

Arreche, O., Guntur, T. R., & Abdallah, M. (2024) · Research Square · doi:10.21203/rs.3.rs-4701709/v1 · Cited by 1

LightGBM model applied in the teaching course of Civic Education integrating red culture

Huang, T. (2023) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns.2023.2.00173

The Efficiency of Ensemble Machine Learning Models on Network Intrusion Detection using KDDCup 99 Dataset

Varghese, N., & Vivek, R. (2023) · IEEE International Conference on Contemporary Computing and Communications (InC4) · doi:10.1109/inc457730.2023.10263037

Enhanced Network Intrusion Detection System Using Machine Learning

Singh, V. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.56541

Network Intrusion Detection Systems Using Deep Learning To Mitigate Attacks

Alla, K. R., & Thangarasu, G. (2023) · International Conference On Smart Technologies For Smart Nation (SmartTechCon) · doi:10.1109/smarttechcon57526.2023.10391449 · Cited by 1

Intrusion Detection Method Based on L1-REF and LightGBM

Xu, D., & Hua, B. (2023) · International Conference on Electronic Information Engineering and Computer Science (EIECS) · doi:10.1109/eiecs59936.2023.10435571

A Secure Approach for Next-Generation IoT Networks

Assmi, H., Guezzaz, A., Benkirane, S., & Azrour, M. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-70102-3_14

Secure Federated Distillation Framework for Encrypted Traffic Classification

Teng, L., Feng, Q. B., Zhao, W., Luo, M., & He, D. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9053-1_1

Dynamic Network Traffic Analysis for Cyberattack Detection Using Multiple Deep Learning Techniques

Rao, A. V. M., & Akthar, S. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-4892-1_14

Mixed data outlier detection based on information entropy integration strategy

Wang, R., Song, Y., Wu, P., & Li, X. (2025) · International Conference on Computer Information Science and Application Technology (CISAT) · doi:10.1109/cisat66811.2025.11181717

JITI: Dynamic Model Serving for Just-in-Time Traffic Inference

Jiang, X., Liu, S., Naama, S., Bronzino, F., Schmitt, P., & Feamster, N. (2025) · Proceedings of the ACM on Networking · doi:10.1145/3768992

An Optimized Deep Features Based Intrusion Detection Framework for the Wireless NetworksPreprint

Majjaru, C., & Senthilkumar, K. (2022) · Research Square · doi:10.21203/rs.3.rs-1433967/v1

Machine Learning for Mirai Attack Detection in 5G-Enabled IoT Networks

Zancanella, M., Silva, E. F., Peixoto, R. M., Stenner, M., & Vieira, A. B. (2025) · IFIP Wireless Days · doi:10.1109/wd67713.2025.11302620

A Technical approach for Protecting IoT Environments with IDS

Lokesh.D, Teja, K. G., Kumari, S. K., & Rasool.Md (2025) · IEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) · doi:10.1109/isssc66652.2025.11388541

Digital twins in healthcare development of an accurate machine learning model for health monitoring through digital twins

Mariprasath, T., Saraswathi, P., & Kumar, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40678-2.00007-6

An Attention-Enhanced CNN-BiGRU Framework for Intrusion Detection

Wang, P., Sang, P., & Gao, P. (2026) · International Journal of Computational Intelligence and Applications · doi:10.1142/s1469026826500069

Advanced similarity metrics for IP flow data analytics

Petr, I., Hrabáková, J., Friedjungová, M., Vašata, D., & Hynek, K. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112188

CAAMF: Conflict-Aware Adaptive Multimodal Fusion for QR Code Phishing Attack Detection

Nigam, N., & Bhandari, R. (2026) · Journal of Trends in Computer Science and Smart Technology · doi:10.36548/jtcsst.2026.3.010

Explainable AI and Machine Learning Framework for Cyber Threat Detection and Adaptive Defense Systems

Prakash, P. A., Salath Joseph, A., Akash Madhavan, M., Aswath, S., Sudharsan, K., & Vignesh Manikandan, C. (2026) · International Conference on Smart Systems and Inventive Technology (ICSSIT) · doi:10.1109/icssit69151.2026.11656434

Implementation and Performance Evaluation of Intrusion Detection Systems under high-speed networks

Saber, M., Belkasmi, M. G., Chadli, S., Emharraf, M., & Farissi, I. E. (2017) · International Conference on Big Data, Cloud and Applications (BDCA) · doi:10.1145/3090354.3090393 · Cited by 3

An Intelligent Intrusion Detection Scheme Powered by Boosting Algorithm

Divakar, S., Priyadarshini, R., Barik, R. K., & Roy, D. S. (2021) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence51648.2021.9377076 · Cited by 3

Network Traffic Intrusion Detection

(2026) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets86098

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

A practical intrusion detection system based on denoising autoencoder and LightGBM classifier with improved detection performance

Ayubkhan, S. A. H., Yap, W., Morris, E., & Rawthar, M. B. K. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-022-04449-w · Cited by 27