Machine Learning in Network Traffic Analysis: Classification, Optimization, and Security

Abstract

With the rapid expansion of digital networks, ensuring efficient and secure network traffic management has become a significant challenge. Traditional rule-based approaches struggle to handle evolving traffic patterns, particularly with the increasing use of encryption. Machine learning (ML) has emerged as a powerful alternative, providing enhanced capabilities for traffic classification, anomaly detection, and optimization. This paper presents a comprehensive review of ML-based techniques, including supervised learning, unsupervised learning, deep learning, and graph-based learning. Key challenges such as data imbalance, real-time processing, and computational overhead are explored. The study consolidates findings from multiple research papers, emphasizing the role of AI-driven models in improving cybersecurity, traffic prediction, and quality of service (QoS). Future research directions include hybrid models, federated learning, and the integration of ML with emerging networking paradigms such as Software-Defined Networking (SDN) and 5G.

Introduction

The rapid growth of internet use, driven by technologies like cloud computing, IoT, and 5G, has increased the need for efficient and intelligent network traffic classification, security, and optimization. Traditional methods such as deep packet inspection are less effective due to encryption and evolving traffic patterns, making machine learning (ML) techniques essential for accurate traffic analysis and anomaly detection.

ML approaches in network traffic analysis include:

• Supervised learning (e.g., decision trees, SVMs) for accurate classification using labeled data.

• Unsupervised learning (e.g., clustering) to detect unknown threats without labeled data.

• Deep learning (e.g., CNNs, RNNs, graph neural networks) for extracting complex features and improving real-time monitoring.

• AI-based optimization (e.g., reinforcement learning, genetic algorithms) to enhance traffic flow and quality of service.

Challenges remain, including limited labeled datasets, computational overhead, privacy concerns, handling encrypted traffic, data imbalance, and the need for real-time adaptability.

Key findings highlight high accuracy of neural networks, superior performance of graph neural networks in complex networks, and advantages of edge AI and federated learning in reducing latency and preserving data privacy.

Future directions focus on hybrid ML models that combine deep learning with traditional methods for improved accuracy and efficiency, federated learning for privacy-preserving classification, and integrating ML with Software-Defined Networking (SDN) and 5G to enable smarter, adaptive network management.

Conclusion

The integration of AI and ML techniques in network traffic optimization has shown promising results, particularly in enhancing security, improving resource allocation, and reducing latency [29]. Machine Learning (ML) has significantly advanced network traffic analysis by enhancing classification, anomaly detection, and optimization capabilities [4].By leveraging various ML techniques, including deep learning and traditional algorithms, models have achieved high accuracy in identifying complex traffic patterns and security threats. These advancements facilitate real-time decision-making, proactive threat mitigation, and adaptive network management strategies. Despite these improvements, several challenges persist. Analysing encrypted traffic remains a significant obstacle, as traditional inspection methods are ineffective in privacy-preserving environments [30]. ML has revolutionized network traffic analysis by offering enhanced classification, anomaly detection, and optimization capabilities. Despite these advancements, challenges such as encrypted traffic analysis, real-time processing, and computational efficiency persist. Future research should focus on hybrid models, federated learning, and integration with next-generation networking technologies to further improve traffic classification and security [4][31].The findings of this study contribute to the evolution of intelligent, self-adaptive network traffic management frameworks capable of handling the growing demands of modern digital infrastructure [23].

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Copyright

Copyright © 2025 Yash Gujarathi, Yash Potekar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.