Research on intrusion classification detection based on IDA-CNN

Daoquan Li; Ruolin Nie; Shengkai Zhu

doi:10.1117/12.3009577

16 October 2023 Research on intrusion classification detection based on IDA-CNN

Daoquan Li, Ruolin Nie, Shengkai Zhu

Author Affiliations +

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128031D (2023) https://doi.org/10.1117/12.3009577
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

In the Internet era, network security faces challenges from various attacks like data modification, compromised applications, and DoS. To tackle abnormal traffic caused by malicious programs, we propose an improved Convolutional Neural Network (CNN) learning model for classifying traffic as normal or attack. Our model enhances feature extraction in both width and depth, resulting in improved accuracy and robustness of the network. Compared to commonly used detection models, our approach achieves an average accuracy improvement of 4.55%. Ablation experiments also show that employing our IDA-CNN-based intrusion classification detection model reduces the average error rate compared to using all feature subsets.

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

Citation Download Citation

Daoquan Li, Ruolin Nie, and Shengkai Zhu "Research on intrusion classification detection based on IDA-CNN", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128031D (16 October 2023); https://doi.org/10.1117/12.3009577

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