Intrusion detection system in the Smart Distribution Network: A feature engineering based AE-LightGBM approach

Abstract

Bi-directional communication network is the foundation of Smart Distribution Network(SDN), but it also exposes SDN to more serious communication risks. Most of the current researches​ solve this problem by Intrusion Detection Systems(IDSs), yet they focus more on the detection performance, while ignoring the real-time requirements, redundant network traffic features, and unbalanced data distribution in SDN communication network. To address these problems, this paper proposes a feature engineering based AutoEncoder(AE)-LightGBM intrusion detection system for SDN. The proposed system uses Borderline-SMOTE to optimize the data distribution firstly, after that, AE is used for feature engineering to extract the main features. Finally LightGBM is trained to recognize the intrusion using the extracted features. Experimental results on the KDDCup99 and NSL-KDD datasets show that the accuracy, precision, and F1-score performance of the proposed model are better than those of traditional models and related works, and have significant advantages in real-time performance.