ResInceptNet-SA: A Network Traffic Intrusion Detection Model Fusing Feature Selection and Balanced Datasets
Source abstract
Abstract
Network intrusion detection models are vital techniques for ensuring cybersecurity. However, existing models face several challenges, such as insufficient feature extraction capabilities, dataset imbalance, and suboptimal detection accuracy. In this paper, a new type of model (ResIncepNet-SA) based on InceptionNet, Resnet, and convolutional neural networks with a self-attention mechanism was proposed to detect network intrusions. The model used the PCA-ADASYN algorithm to compress network traffic features, extract high-correlation feature datasets, and oversample and balance the feature datasets to classify abnormal network traffic. The experimental results show that the accuracy, precision, recall, and F1-score of the proposed ResIncepNet-SA model using the NSL-KDD dataset reach 0.99366, 0.99343, 0.99339, and 0.99338, respectively. This model enhances the accuracy of abnormal network traffic detection and outperforms existing models when applied to imbalanced datasets, offering a new solution for network traffic intrusion detection.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection · Internet Traffic Analysis and Secure E-voting · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China
School of Electronic Information Engineerings, Hebei University of Technology, Tianjin 300130, China
Hangzhou Zhijiang Switchgear Stock Co., Ltd., Hangzhou 311200, China
Funding
Tianjin Science and Technology Commission
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