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A New Loss Function for Traffic Classification Task on Dramatic Imbalanced Datasets | IEEE Conference Publication | IEEE Xplore

A New Loss Function for Traffic Classification Task on Dramatic Imbalanced Datasets

Publisher: IEEE

Abstract:

Traffic classification has always been a hot research topic, which can be used in network performance optimization, security management and some other scenarios. There ha...View more

Abstract:

Traffic classification has always been a hot research topic, which can be used in network performance optimization, security management and some other scenarios. There have been a lot of high-performance classifiers in network classification domain, but nearly all these classifiers only focus on the overall accuracy. There exists tremendous traffic volume gaps among various network applications, which causes extreme imbalanced datasets when applying some artificial intelligence (AI) approaches to classify the traffic into categories or specific applications. The most intractable problem caused by training on imbalanced dataset is that even though the classifier misclassifies categories in rather small sample scale, the overall classification accuracy can be still quite high. This issue is intolerant if the minority category is vital but in small scale. To solve this problem mentioned above, we propose a self-defined loss function UniLoss, which greatly improves the classification accuracy of minority categories and maintains the performance of majorities meanwhile. VoIP traffic is representative for its imbalanced distribution, and thus VoIP traffic is chosen as the test instance. In addition, we design four deep neural networks and construct four test cases with different dramatic imbalanced category sample distributions, on which the results have verified the effectiveness of UniLoss.
Date of Conference: 07-11 June 2020
Date Added to IEEE Xplore: 27 July 2020
ISBN Information:

ISSN Information:

Publisher: IEEE
Conference Location: Dublin, Ireland

References

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