Analysis of anomalous behaviour in network systems using deep reinforcement learning with convolutional neural network architecture  

作　　者：Mohammad Hossein Modirrousta Parisa Forghani Arani Reza Kazemi Mahdi Aliyari-Shoorehdeli 

机构地区：[1]Faculty of Electrical Engineering,K.N.Toosi University of Technology,Tehran,Iran [2]Faculty of Mechatronics Engineering,K.N.Toosi University of Technology,Tehran,Iran

出　　处：《CAAI Transactions on Intelligence Technology》2024年第6期1467-1484,共18页智能技术学报(英文)

摘　　要：To gain access to networks,various intrusion attack types have been developed and enhanced.The increasing importance of computer networks in daily life is a result of our growing dependence on them.Given this,it is glaringly obvious that algorithmic tools with strong detection performance and dependability are required for a variety of attack types.The objective is to develop a system for intrusion detection based on deep reinforcement learning.On the basis of the Markov decision procedure,the developed system can construct patterns appropriate for classification purposes based on extensive amounts of informative records.Deep Q-Learning(DQL),Soft DQL,Double DQL,and Soft double DQL are examined from two perspectives.An evaluation of the authors’methods using UNSW-NB15 data demonstrates their superiority regarding accuracy,precision,recall,and F1 score.The validity of the model trained on the UNSW-NB15 dataset was also checked using the BoT-IoT and ToN-IoT datasets,yielding competitive results.

关 键 词：deep reinforcement learning intrusion detection Q-LEARNING transferability 

分 类 号：TH2[机械工程—机械制造及自动化]