An intrusion detection model using smote and ensemble learning

Ling-feng Qiu; Ya-fei Song

doi:10.1117/12.2667349

22 February 2023 An intrusion detection model using smote and ensemble learning

Ling-feng Qiu, Ya-fei Song

Proceedings Volume 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022); 125870J (2023) https://doi.org/10.1117/12.2667349
Event: Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 2022, Shanghai, China

Abstract

As a security defense technology to protect the network from attack, network intrusion detection system plays a very important role in the field of computer system and network security. Aiming at the multi classification problem of unbalanced data in network intrusion detection, machine learning has been widely used in intrusion detection, which is more intelligent and accurate than traditional methods. The existing multi classification methods of network intrusion detection are improved, and an intrusion detection model using smote and ensemble learning is proposed. The model is mainly divided into two parts: smote oversampling and stacking classifier. The NSL-KDD dataset is used to test the Stacked Ensemble model in this paper. Compared with the other five basic learner models, the Stacked Ensemble has a higher detection rate. Stacked Ensemble has significant advantages in solving the multi classification problem of unbalanced network intrusion detection data. It is a practical and feasible intrusion detection method.

Citation Download Citation

Ling-feng Qiu and Ya-fei Song "An intrusion detection model using smote and ensemble learning", Proc. SPIE 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 125870J (22 February 2023); https://doi.org/10.1117/12.2667349

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