期刊
COMPUTERS & ELECTRICAL ENGINEERING
卷 79, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compeleceng.2019.106460
关键词
Network anomalies; Online learning systems; Network intrusion detection (NID); Deep Q-Network (DQN); Reinforcement learning (RL)
类别
资金
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2017R1D1A1B03035833]
Because of the increasing application of reinforcement learning (RL), particularly deep Q-learning algorithm, research organizations utilize it with increasing frequency. The prediction of cyber vulnerability and development of efficient real-time online network intrusion detection (NID) systems are progressions toward becoming RL-powered. An open issues in NID is the model design and prediction of real-time online data composed of a series of time-related feature patterns. There have been concerns regarding the operation of the developed systems because cyber-attack scenarios vary continuously to circumvent NID. These issues have been related to the human interaction significance and the decrease in accuracy verification. Therefore, we employ an RL that permits a deep auto-encoder in the Q-network (DAEQ-N). The proposed DAEQ-N attempts to achieve the maximum prediction accuracy in online learning systems into which continuous behavior patterns are fed and which are trained with more significant weights by classifying it as either normal or anomalous. (C) 2019 Elsevier Ltd. All rights reserved.
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