Publication: Network Intrusion Detection Systems Using Adversarial Reinforcement Learning with Deep Q-network
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Issued Date
2020
Resource Type
File Type
application/pdf
ISSN
21570981
Other identifier(s)
2-s2.0-85098882716
Rights
Srinakharinwirot University
Rights Holder(s)
มหาวิทยาลัยศรีนครินทรวิโรฒ
Bibliographic Citation
International Conference on ICT and Knowledge Engineering. Vol 2020-November, (2020)
Suggested Citation
Suwannalai E., Polprasert C. Network Intrusion Detection Systems Using Adversarial Reinforcement Learning with Deep Q-network. International Conference on ICT and Knowledge Engineering. Vol 2020-November, (2020). doi:10.1109/ICTKE50349.2020.9289884 Retrieved from: https://hdl.handle.net/20.500.14740/5715
Author(s)
Abstract
In this paper, we investigate the performance of deep reinforcement learning (DRL) in network intrusion detection systems (NIDS) problems. We propose the Adversarial/Multi Agent Reinforcement Learning using Deep Q-Learning (AE-DQN) algorithm for anomaly-based NIDS. The performance of our proposed is investigated over NSL-KDD dataset using KDDTest+ dataset. We focus on 5-label classification problem. Our proposed algorithm yields 80% accuracy and 79% macro F1 score. In addition, our proposed algorithm exhibits superior performance in detecting certain types of attacks in NSL-KDD dataset compared to those obtained using the Recurrent Neural Network (RNN) IDS (2) and Adversarial Reinforcement Learning with SMOTE (AESMOTE) IDS (3). Future work will focus on improving detection performance over other types of attacks. © 2020 IEEE.