TDL-IDS: Towards A Transfer Deep Learning based Intrusion Detection System

With the development of Internet of Things (IoT), network security has become very important as cyber-attackers can easily compromise such distributed networks and systems. An intrusion detection system (IDS) is a basic and essential security mechanism to detect malicious traffic. In the literature, in addition to traditional machine learning algorithms, many deep learning schemes have been examined to enhance the detection performance. However, insufficient amounts of labeled samples are still a challenge for real-world implementation, especially in some scenarios such as smart home and Internet of Vehicles. To address this issue, we explore transfer learning as a promising solution. In this work, we develop TDL-IDS, a transfer deep learning based IDS that can work with limited labeled data items. Our approach first uses Long Short Term Memory (LSTM) to train a model on the source domain and then leverages transfer learning to continue the training process on the target domain. In the evaluation, we use NSL-KDD as the source domain, and AWID as the target domain. Our results indicate that TDL-IDS can outperform many similar approaches.