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

Real-time big data processing for anomaly detection: A survey

Ariyaluran Habeeb, R. A., Nasaruddin, F., Gani, A., Targio Hashem, I. A., Ahmed, E., & Imran, M. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.08.006 · Cited by 487

An intrusion detection and prevention system in cloud computing: A systematic review

Patel, A., Taghavi, M., Bakhtiyari, K., & Celestino Júnior, J. (2013) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2012.08.007 · Cited by 425

Algorithms for advanced packet classification with ternary CAMs

Lakshminarayanan, K., Rangarajan, A., & Venkatachary, S. (2005) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/1080091.1080115 · Cited by 274

Toward scalable internet traffic measurement and analysis with Hadoop

Lee, Y., & Lee, Y. (2012) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2427036.2427038 · Cited by 195

Distributed Abnormal Behavior Detection Approach Based on Deep Belief Network and Ensemble SVM Using Spark

Marir, N., Wang, H., Feng, G., Li, B., & Jia, M. (2018) · IEEE Access · doi:10.1109/access.2018.2875045 · Cited by 184

Time and area efficient pattern matching on FPGAs

Baker, Z. K., & Prasanna, V. K. (2004) · ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA) · doi:10.1145/968280.968312 · Cited by 156

Kargus

Jamshed, M. A., Lee, J., Moon, S., Yun, I., Deok-jin, K., Lee, S., Yi, Y., & Park, K. (2012) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2382196.2382232 · Cited by 151

LightGBM

Wang, D., Zhang, Y., & Zhao, Y. (2017) · International Conference on Computational Biology and Bioinformatics (ICCBB) · doi:10.1145/3155077.3155079 · Cited by 150

A Predictive Data Feature Exploration-Based Air Quality Prediction Approach

Zhang, Y., Wang, Y., Gao, M., Ma, Q., Zhao, J., Zhang, R., Wang, Q., & Huang, L. (2019) · IEEE Access · doi:10.1109/access.2019.2897754 · Cited by 150

Data Mining Techniques in Intrusion Detection Systems: A Systematic Literature Review

Salo, F., Injadat, M., Nassif, A. B., Shami, A. A., & Essex, A. (2018) · IEEE Access · doi:10.1109/access.2018.2872784 · Cited by 132

Packet and Flow Based Network Intrusion Dataset

Gogoi, P., Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2012) · Communications in computer and information science · doi:10.1007/978-3-642-32129-0_34 · Cited by 128

Real time intrusion detection system for ultra-high-speed big data environments

Rathore, M. M., Ahmad, A., & Paul, A. (2016) · The Journal of Supercomputing · doi:10.1007/s11227-015-1615-5 · Cited by 119

Feature selection and classification using flexible neural tree

Chen, Y., Abraham, A., & Yang, B. (2006) · Neurocomputing · doi:10.1016/j.neucom.2006.01.022 · Cited by 115

Fast regular expression matching using small TCAMs for network intrusion detection and prevention systems

Meiners, C. R., Patel, J., Norige, E., Torng, E. K., & Liu, A. X. (2010) · Cited by 109

A feature selection approach to find optimal feature subsets for the network intrusion detection system

Kang, S., & Kim, K. J. (2016) · Cluster Computing · doi:10.1007/s10586-015-0527-8 · Cited by 92

Improving network security using genetic algorithm approach

Banković, Z., Stepanović, D., Bojanić, S., & Nieto-Taladriz, O. (2007) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2007.05.010 · Cited by 89

MARK-ELM: Application of a novel Multiple Kernel Learning framework for improving the robustness of Network Intrusion Detection

Fossaceca, J. M., Mazzuchi, T. A., & Sarkani, S. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.12.040 · Cited by 86

Intrusion detection system using hybrid binary PSO and K-nearest neighborhood algorithm

Syarif, A. R., & Gata, W. (2017) · International Conference on Information & Communication Technology and System (ICTS) · doi:10.1109/icts.2017.8265667 · Cited by 78

Feature Selection Using Relative Fuzzy Entropy and Ant Colony Optimization Applied to Real-time Intrusion Detection System

Varma, P. R. K., Kumari, V. V., & Kumar, S. S. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.05.203 · Cited by 62

Hashdoop: A MapReduce framework for network anomaly detection

Fontugne, R., Mazel, J., & Fukuda, K. (2014) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infcomw.2014.6849281 · Cited by 52

A Practical Network-Based Intrusion Detection and Prevention System

Wattanapongsakorn, N., Srakaew, S., Wonghirunsombat, E., Sribavonmongkol, C., Junhom, T., Jongsubsook, P., & Charnsripinyo, C. (2012) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2012.46 · Cited by 39

A multi-gigabit rate deep packet inspection algorithm using TCAM

Sung, J.-S., Kang, S.-M., Lee, Y., Kwon, T.-G., & Kim, B.-T. (2005) · Globecom · doi:10.1109/glocom.2005.1577667 · Cited by 38

An Effective Technique for Intrusion Detection Using Neuro-Fuzzy and Radial SVM Classifier

Chandrasekhar, A. M., & Raghuveer, K. (2013) · Lecture notes in electrical engineering · doi:10.1007/978-1-4614-6154-8_49 · Cited by 20

An Efficient Approach for Intrusion Detection in Reduced Features of KDD99 Using ID3 and Classification with KNNGA

Singh, P. B., & Tiwari, A. (2015) · International Conference on Advances in Computing and Communication Engineering · doi:10.1109/icacce.2015.49 · Cited by 17

An Efficient Algorithm for Network Intrusion Detection System

Jaiganesh, V., Mangayarkarasi, S., & Sumathi, P. (2014) · International Journal of Computer Applications · doi:10.5120/15771-4100 · Cited by 15

Network Intrusion Prediction Model based on RBF Features Classification

Xing-zhu, W. (2016) · International Journal of Security and Its Applications · doi:10.14257/ijsia.2016.10.4.23 · Cited by 7

Trusted intrusion detection architecture for high‐speed networks based on traffic classification, load balancing and high availability mechanism

Sourour, M., Bouhoula, A., & Abbes, T. (2010) · Security and Communication Networks · doi:10.1002/sec.161 · Cited by 5

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

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