  With massive data being generated daily and the ever-increasing
interconnectivity of the world's Internet infrastructures, a machine learning
based intrusion detection system (IDS) has become a vital component to protect
our economic and national security. In this paper, we perform a comprehensive
study on NSL-KDD, a network traffic dataset, by visualizing patterns and
employing different learning-based models to detect cyber attacks. Unlike
previous shallow learning and deep learning models that use the single learning
model approach for intrusion detection, we adopt a hierarchy strategy, in which
the intrusion and normal behavior are classified firstly, and then the specific
types of attacks are classified. We demonstrate the advantage of the
unsupervised representation learning model in binary intrusion detection tasks.
Besides, we alleviate the data imbalance problem with SVM-SMOTE oversampling
technique in 4-class classification and further demonstrate the effectiveness
and the drawback of the oversampling mechanism with a deep neural network as a
base model.
