Abstract
The development of computer and network technology has provided convenience to our daily life, however, attack and intrusion in network emerge endlessly. Intrusion Detection System (IDS) has been developed to confront network attacks. As a result, the research of IDS is one of the most popular fields in recent years. This paper proposes a Gradient Boosting Decision Tree (GBDT)-paralleled quadratic ensemble learning method for intrusion detection system. We use GBDT to deal with the spatial part of traffic data and use Gated Recurrent Unit (GRU) model with special modification for network traffic to deal with temporal data. Then, in order to combine the spatial feature and temporal feature, we fuse GBDT model and GRU model to make a quadratic ensemble model as our final intrusion detection system. The experimental results based on CICIDS2017 dataset show that the advanced spatial-temporal intrusion detection system based on ensemble learning achieves better accuracy, recall, precision and F1 score than the state-of-the-art methods. The accuracies of detecting benign, port scan, Distributed Denial of Service (DDoS), infiltration and web attack traffic are up to 99.9%, 99.9%, 99.9%, 99.9%, and 99.9%, respectively. We also use our method in Information-Centric Networking (ICN) dataset and the results show our method achieves much better performance compared with existing methods.
Highlights
In recent years, Intrusion Detection System (IDS) is widely used in all respects
In 2018, a new IDS dataset called CICIDS2017 (Intrusion Detection Evaluation Dataset provided by Canadian Institute for Cybersecurity in 2017) [44] was proposed by Iman et al from Canadian Institute for Cybersecurity
This dataset includes traditional attacks such as Denial of Service (DoS), Distributed Denial of Service (DDoS), and port scanning, and some new types of attacks and intrusions, such as the Heartbleed Bug based attack, which is just be found in recent years
Summary
Intrusion Detection System (IDS) is widely used in all respects. Pan et al [1] developed a hybrid intrusion detection system for power system. Et al [2] built a special intrusion detection system for connected vehicles in smart cities. Ambusaidi et al [3] created an IDS system to study traffic problem. Hodo et al [4] tried to use IDS in Internet of Things networks. Due to the explosive growth of Internet, more and more researchers pay attention on IDS in field of internet security [5]–[8]
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