Intrusion Detection System using Semi-Supervised Learning with Adversarial Auto-encoder

Abstract

As computer networks become vulnerable to attacks from intruders, Intrusion Detection Systems (IDS) is a critical component that monitors activities of computer networks and classifies them as either normal or anomalous. Remarkable advancement of machine learning makes us consider to use supervised machine learning to build IDS. Supervised machine learning requires a large amount of training data, leading to costly human-labor operation; it requires the human operator to examine data, classify them, and annotate them with a label. To address this issue, we propose an IDS that employs semi-supervised learning. Semi-supervised learning uses a small number of labeled data in training dataset to reduce costly human-labor tasks and improves the performance with support of unlabeled data in training dataset. The proposed method employs Adversarial Auto-encoder (AAE), a semi-supervised learning algorithm that incorporates the Generative Adversarial Nets (GAN) into the Auto-encoder (AE). We evaluate the effectiveness of the proposed method using NSL-KDD dataset. We confirm that the proposed method that uses only 0.1 percent of labeled data achieves comparable performance with existing IDSs that use machine learning methods.