Cor-ENTC:correlation with ensembled approach for network traffic classification using SDN technology for future networks

Paramasivam, Suguna; Velusamy, R. Leela

doi:10.1007/s11227-022-04969-4

Cited by 5 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. [33] integrated random forest, Adaboost, gradient boost, and XGBoost to build an ensemble classification framework Cor-ENTC to classify VPN traffic entering the SDN environment. The scheme is highly efficient in communication, significantly more accurate than a single classifier, and can also handle unknown applications.…”

Section: Unknown Internet Traffic Identificationmentioning

confidence: 99%

Unknown Traffic Recognition Based on Multi-Feature Fusion and Incremental Learning

et al. 2023

View full text Add to dashboard Cite

Accurate classification and identification of Internet traffic are crucial for maintaining network security. However, unknown network traffic in the real world can affect the accuracy of current machine learning models, reducing the efficiency of traffic classification. Existing unknown traffic classification algorithms are unable to optimize traffic features and require the entire system to be retrained each time new traffic data are collected. This results in low recognition efficiency, making the algoritms unsuitable for real-time application detection. To solve the above issues, we suggest a multi-feature fusion-based incremental technique for detecting unknown traffic in this paper. The approach employs a multiple-channel parallel architecture to extract temporal and spatial traffic features. It then uses the mRMR algorithm to rank and fuse the features extracted from each channel to overcome the issue of redundant encrypted traffic features. In addition, we combine the density-ratio-based clustering algorithm to identify the unknown traffic features and update the model via incremental learning. The cassifier enables real-time classification of known and unknown traffic by learning newly acquired class knowledge. Our model can identify encrypted unknown Internet traffic with at least 86% accuracy in various scenarios, using the public ISCX-VPN-Tor datasets. Furthermore, it achieves 90% accuracy on the intrusion detection dataset NSL-KDD. In our self-collected dataset from a real-world environment, the accuracy of our model exceeds 96%. This work offers a novel method for identifying unknown network traffic, contributing to the security preservation of network environments.

show abstract

Section: Unknown Internet Traffic Identificationmentioning

confidence: 99%