Deep Learning Based Traffic Classification with Feature Selection Mechanism and Explainable Artificial Intelligence (Xai)
Keywords:
Deep learning, feature selection, XAI, traffic classification, QoSAbstract
In computer networking and cybersecurity, traffic categorization, which identifies the kind and nature of network traffic flows, is a vital activity. For the purpose of controlling Quality of Service (QoS), optimising network resources, and increasing security measures, accurate traffic classification is essential. Recently, the field of traffic classification has undergone a revolution and explicable artificial intelligence. To improve the comprehension and interpretability of classification findings, this study investigates the application of explainable AI methodologies with deep learning models for efficient traffic categorization and dominant feature selection. Network functions like software-delivered networking structures use traffic cataloguing extensively. Numerous techniques for classifying traffic without looking at the packet payload have been developed, including deep learning models. They have a significant obstacle, though, given that deep learning's method is puzzling. Malfunction yields when training dataset with the improper data hence we have insufficient deep leaning model. This can be fixed with the help of XAI to get better deep learning model. In this work we presented genetic algorithm which works on support of XAI to explore traffic classification by using deep learning model. Model can be applied on traffic classifier after each feature can be evaluated properly. The role of genetic algorithm is to generate mask of feature. In comparative works our model proved with better accuracy and good dominance rate.
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