Combining Oversampling with Recurrent Neural Networks for Intrusion Detection

Jenq-Haur Wang, Tri Wanda Septian

Abstract: Previous studies on intrusion detection focus on analyzing features from existing datasets. With various types of fast-changing attacks, we need to adapt to new features for effective protection. Since the real network traffic is very imbalanced, it’s essential to train appropriate classifiers that can deal with rare cases. In this paper, we propose to combine oversampling techniques with deep learning methods for intrusion detection in imbalanced network traffic. First, after preprocessing with data cleaning and normalization, we use feature importance weights generated from ensemble decision trees to select important features. Then, the Synthetic Minority Oversampling Technique (SMOTE) is used for creating synthetic samples from minority class. Finally, we use Recurrent Neural Networks (RNNs) including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for classification. In our experimental results, oversampling improves the performance of intrusion detection for both machine learning and deep learning methods. The best performance can be obtained for CIC-IDS2017 dataset using LSTM classifier with an F1-score of 98.9%, and for CSE-CIC-IDS2018 dataset using GRU with an F1-score of 98.8%. This shows the potential of our proposed approach in detecting new types of intrusion from imbalanced real network traffic.