A New Intrusion Detection System Based on Gated Recurrent Unit (GRU) and Genetic Algorithm

  • Mahdi ManaviEmail author
  • Yunpeng Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11611)


Distributed systems are extensive nowadays. The challenge of preventing network penetration by malware and hackers in these systems has been extensively considered and many types of research have been carried out in this field. Due to the high volume of input and output data in distributed systems, definitive and static algorithms that are used for small environments are not appropriate. For this problem, one of the new techniques is the deep learning method, which allows one to find optimal answers. In this paper, deep learning is used to investigate the behavior patterns of requests that enter the distributed network and then attacks are detected based on these patterns, which send an alarm to administrators (Anomaly Detection). In the next step, the genetic algorithm is used with the rule-based database to examine misuse detection. In this paper, considering the results obtained, it can be seen that the proposed algorithm provides high accuracy in detecting attacks with a low false alarm rate.


Intrusion detection Recurrent neural network Gated recurrent unit Genetic algorithm KDD Hybrid detection method 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Mirdamad Institute of Higher Education of GorganGorganIran
  2. 2.Department of Information and Logistics TechnologyUniversity of HoustonHoustonUSA

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