Hierarchical Autoencoder for Network Intrusion Detection

Hyoseon Kye, Miru Kim, Minhae Kwon

Abstract: With the development of the Internet and networks, various types of network data have been shared. Intelligent and various cyber attacks are continuously increasing as the amount of network data increases rapidly. Although anomaly detection based on autoencoders worked actively, autoencoder has a limitation that it cannot utilize hidden space and detects abnormal data with various anomalies only at one point. We propose a step-by-step anomaly detection using the hidden space of the autoencoder. The proposed system improves the performance of anomaly detection by utilizing hidden spaces. The pre-detection rate of the abnormal data is 20%, enabling proactive response. The total detection rate of the abnormal data is 99%, which outperforms other existing solutions.