Honeypot Intrusion Detection System using an Adversarial Reinforcement Learning for Industrial Control Networks

Published: 14 March 2022| Version 1 | DOI: 10.17632/gxntkyyjvf.1


The simulated environment operates as a second agent against the original one in this technique. To evaluate the performance of the proposed method, we compare it with two categories of DDoS attacks, including NetBIOS and LDAP.


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README Anomaly Detection with a Honeypot Using RLAE Overview Using reinforcement learning to identify abnormalities and maybe trigger a reaction in the future, CICDDoS2019 was utilized as the dataset containing data on different abnormalities. Using deep Q-learning in conjunction with Keras/TensorFlow to produce the network code for the article "Honeypot Intrusion Detection System Using Adversarial Reinforcement Learning for Industrial Control Networks." Pashaei, A., Akbari, M. E., Lighvan, M. Z., and Charmin, A., 2022. CICDDoS2019 notebooks: EIDS-RLAE-CICDDoS2019.ipynb.


Islamic Azad University Ahar Branch