Machine learning-based enhanced deep packet inspection for IP packet priority classification with differentiated services code point for advance network management
In modern networking, the efficient prioritization and classification of network traffic is paramount to ensure optimal network performance and optimization. This study presents an approach to enhance intelligent packet forwarding priority classification on Differentiated Services Code Point (DSCP),...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universiti Teknikal Malaysia Melaka (UTeM).
2024
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/113077/7/113077_Machine%20learning-based%20enhanced%20deep%20packet%20inspection.pdf http://irep.iium.edu.my/113077/ https://jtec.utem.edu.my/jtec/article/view/6323/4113 https://doi.org/10.54554/jtec.2024.16.02.002 |
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| Summary: | In modern networking, the efficient prioritization and classification of network traffic is paramount to ensure optimal network performance and optimization. This study presents an approach to enhance intelligent packet forwarding priority classification on Differentiated Services Code Point (DSCP), leveraging classifiers from machine learning algorithms for Deep Packet Inspection (DPI). The DSCP resides inside the Differentiated Services (DS) field of the Internet Protocol (IP) packet header in an OSI or TCP/IP model, which prioritizes different types of packets for forwarding to the router based on the attached payload. Similarly, DPI plays a crucial role in network management, enabling the identification of applications, services, and potential threats within the network traffic. In this study, various machine learning models, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Logistic Regression and ensemble models such as, XGBoost, AdaBoost were used to harness the capabilities of network packet classification based on DSCP. Detailed experimentation was conducted to evaluate their performance. The results show that AdaBoost demonstrated superior performance with an accuracy of around 89.91%, showcasing its ability to adapt the evolving network configurations and conditions while maintaining high classification accuracy on the IP packets. The random forest model also performed well, achieving an accuracy of 89.41%, making it a strong candidate for the DSCP classification in network transmission. This study has the potential to significantly improve how networks manage traffic, prioritize packets, and secure complex and dynamic network environments. |
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