<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Network intrusion detection has been widely discussed and studied as an important part of protecting network security. Therefore, this paper presents an in-depth study of the application of an improved V-detector algorithm in network intrusion detection. In this paper, we construct a V-detector intrusion detection model, adopt the “self-oriented” identification principle, and randomly generate detectors with large differences from the health library. A smaller number of detectors are used to compare the data information generated by the computer, and if they are similar, they are judged as intrusions. Intrusion detection experiments are performed on multiple types of networks by using classifiers to determine whether the access to be detected is an attack access. The experimental results show that the model has the lowest false alarm rate for mixed feature networks, with a false alarm rate of only 13% and a detection rate of 89%, with a sample size of 25,987. After the improvement of the V-detector intrusion detection model, the error correction output problem leads to a network intrusion with a miss rate of only 11% and a protection rate of 85%. The experimental data proved that the model has the advantages of large data size and comprehensive intrusion attack types.</p>
</abstract>

Network intrusion detection has been widely discussed and studied as an important part of protecting network security. Therefore, this paper presents an in-depth study of the application of an improved V-detector algorithm in network intrusion detection. In this paper, we construct a V-detector intrusion detection model, adopt the “self-oriented” identification principle, and randomly generate detectors with large differences from the health library. A smaller number of detectors are used to compare the data information generated by the computer, and if they are similar, they are judged as intrusions. Intrusion detection experiments are performed on multiple types of networks by using classifiers to determine whether the access to be detected is an attack access. The experimental results show that the model has the lowest false alarm rate for mixed feature networks, with a false alarm rate of only 13% and a detection rate of 89%, with a sample size of 25,987. After the improvement of the V-detector intrusion detection model, the error correction output problem leads to a network intrusion with a miss rate of only 11% and a protection rate of 85%. The experimental data proved that the model has the advantages of large data size and comprehensive intrusion attack types.

Research on the application of improved V-detector algorithm in network intrusion detection

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Network intrusion detection has been widely discussed and studied as an important part of protecting network security. Therefore, this paper presents an...

Research on the application of improved V-detector algorithm in network intrusion detection

Online veröffentlicht: 07. Okt. 2023

Seitenbereich: -

Eingereicht: 22. Dez. 2022

Akzeptiert: 17. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00526

Schlüsselwörter
Improved V-detector algorithm, Network intrusion, False alarm rate, Multilayer network

© 2023 Yuming Zhong et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the application of improved V-detector algorithm in network intrusion detection

Online veröffentlicht: 07. Okt. 2023

Seitenbereich: -

Eingereicht: 22. Dez. 2022

Akzeptiert: 17. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00526

SchlüsselwörterImproved V-detector algorithm, Network intrusion, False alarm rate, Multilayer network

© 2023 Yuming Zhong et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Improved V-detector algorithm, Network intrusion, False alarm rate, Multilayer network