Electrical Engineering Systems ZOOM Seminar

Speaker: Daniel Shalev

M.Sc. student under the supervision of Prof. Yuval Shavitt

Wednesday, 25^th September 2024, at 15:00

FlowPicClip: Language Supervision Improves Network Traffic Classification

Abstract

Traffic classification has gained much attention in the past decade, and using deep learning proved to exhibit good performance.  However, the lack of large labeled datasets pushed research to explore few-shots learning approaches where only a few labeled samples per class are available, and usage of augmentation techniques tailored to the domain of network traffic was proved as a viable solution.

In this paper, we demonstrate that it is possible to improve the performance of augmentations using a different approach. Our solution simplifies preprocessing and reduces training time, while effectively utilizing small amounts of training data. Furthermore, it proves to be highly effective in few-shot scenarios, demonstrating robust results when tested on disjoint datasets, specifically the UCDAVIS19 and ISCX datasets. Inspired by the recent breakthroughs in integrating image and text data, particularly the CLIP model by OpenAI, we introduce FlowPicClip. This model harnesses the power of contrastive learning with FlowPics and their labels as text sentences. By leveraging Large Language Model (LLM) encoders FlowPicClip aligns network traffic representations with their textual descriptions.  We demonstrate 2.75% and 1.4% improvements over the best published results on the UCDavis19-Human and ISCX datasets for classification tasks, along with 7.7% and 6.2% improvements in few-shot classification on these datasets.

השתתפות בסמינר תיתן קרדיט שמיעה = עפ"י רישום בצ'ט של שם מלא + מספר ת.ז.