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In order to gain access to networks, different types of intrusion attackshave been designed, and the attackers are working on improving them. Computernetworks have become increasingly important in daily life due to the increasingreliance on them. In light of this, it is quite evident that algorithms withhigh detection accuracy and reliability are needed for various types ofattacks. The purpose of this paper is to develop an intrusion detection systemthat is based on deep reinforcement learning. Based on the Markov decisionprocess, the proposed system can generate informative representations suitablefor classification tasks based on vast data. Reinforcement learning isconsidered from two different perspectives, deep Q learning, and double deep Qlearning. Different experiments have demonstrated that the proposed systemshave an accuracy of $99.17\%$ over the UNSW-NB15 dataset in both approaches, animprovement over previous methods based on contrastive learning andLSTM-Autoencoders. The performance of the model trained on UNSW-NB15 has alsobeen evaluated on BoT-IoT datasets, resulting in competitive performance.

Analysis of Anomalous Behavior in Network Systems Using Deep Reinforcement Learning with CNN Architecture