Research on intrusion detection method of party building network in colleges and universities based on machine learning

Chaozhi Fan

doi:10.1117/12.3038748

22 July 2024 Research on intrusion detection method of party building network in colleges and universities based on machine learning

Chaozhi Fan

Author Affiliations +

Proceedings Volume 13222, International Conference on Signal Processing and Communication Security (ICSPCS 2024); 132221A (2024) https://doi.org/10.1117/12.3038748
Event: Third International Conference on Signal Processing and Communication Security (ICSPCS 2024), 2024, Chengdu, China

Abstract

The rapid development of the Internet and the intelligent information age have provided more possibilities for data leakage, network attacks and other behaviors of malicious persons. The network security of party building in colleges and universities faces severe challenges. Intrusion detection is a new information security technology used to detect intrusions in computer network systems. Intrusion detection methods based on machine learning can better identify unknown anomalies than rule-based methods. The essence of the intrusion detection problem is a classification problem. There are many classification methods in machine learning, including convolutional neural networks, naive Bayes, SVM, etc. These algorithms have good classification performance on balanced data sets. How to extract more representative attribute features from complex data sets to more accurately describe normal behavior and intrusion behavior has become an important breakthrough point in current intrusion detection solutions based on machine learning. In order to solve the problem of network intrusion for party building in universities, this paper proposes to apply the convolutional neural network algorithm and random forest algorithm in machine learning to the intrusion detection model of party building network in universities, which effectively improves the overall classification accuracy of the model and the recognition rate of minority classes.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Chaozhi Fan "Research on intrusion detection method of party building network in colleges and universities based on machine learning", Proc. SPIE 13222, International Conference on Signal Processing and Communication Security (ICSPCS 2024), 132221A (22 July 2024); https://doi.org/10.1117/12.3038748

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KEYWORDS

Computer intrusion detection

Data modeling

Convolutional neural networks

Random forests

Machine learning

Network security

Universities

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