Research on communication network intrusion detection method based on machine learning algorithm

Fengping Hao; Yi Li; Jingjing Wang

doi:10.1117/12.3013387

29 November 2023 Research on communication network intrusion detection method based on machine learning algorithm

Fengping Hao, Yi Li, Jingjing Wang

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129371I (2023) https://doi.org/10.1117/12.3013387
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

Communication network is an important part of social life, but it is extremely vulnerable to various network attacks, and the energy of data nodes in the network is limited, and abnormal behavior in the network cannot be detected in the highdimensional traffic data mode. Therefore, an intrusion behavior detection method based on machine learning algorithm was proposed to detect attack behavior in communication network in real time. Firstly, through the restricted Boltzmann machine algorithm model of machine learning, high-dimensional traffic data features are extracted and dimension reduction operation is carried out. Then, combined with the characteristics of communication network cluster structure, a multi-layer support vector machine classification model is established to detect network abnormal state. Finally, the public intrusion detection data set is used to verify the proposed intrusion behavior detection model. The experimental results show that the detection accuracy of the model for network traffic can reach 99.63%, in order to provide reference for further research on intrusion behavior detection methods in the future.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fengping Hao, Yi Li, and Jingjing Wang "Research on communication network intrusion detection method based on machine learning algorithm", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129371I (29 November 2023); https://doi.org/10.1117/12.3013387

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