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Research on Network Abnormal Behavior Recognition Model Based on Parallel Decision Tree Algorithm

Published: 19 December 2023 Publication History

Abstract

As the network is more and more inseparable from people's life, network security has become very important. Nowadays, in the face of a large number of network attacks, effective network attack identification means is particularly important. Therefore, this paper proposes a new parallel structure model based on decision tree, which improves the traditional binary decision tree network. At the same time, it combines the ensemble learning model to avoid the local optimal solution problem and improve the accuracy of network attack recognition. When encountering a large amount of network traffic data, the identification time of network traffic monitoring is greatly shortened. Combined with the simulation experiment to complete the relevant verification, the simulation results show that the model proposed in this paper can greatly shorten the attack recognition time on the basis of ensuring the accuracy of network attack recognition, which provides an important guarantee for network attack and defense.

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      ICCDA '23: Proceedings of the 2023 7th International Conference on Computing and Data Analysis
      September 2023
      137 pages
      ISBN:9798400700576
      DOI:10.1145/3629264
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 19 December 2023

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