Estimating the Number of HTTP/3 Responses in QUIC Using Deep Learning
- URL: http://arxiv.org/abs/2410.06140v1
- Date: Tue, 8 Oct 2024 15:40:22 GMT
- Title: Estimating the Number of HTTP/3 Responses in QUIC Using Deep Learning
- Authors: Barak Gahtan, Robert J. Shahla, Reuven Cohen, Alex M. Bronstein,
- Abstract summary: This paper proposes a novel solution for estimating the number of HTTP/3 responses in a given QUIC connection by an observer.
The proposed scheme transforms QUIC connection traces into a sequence of images and trains machine learning (ML) models to predict the number of responses.
The scheme achieves up to 97% cumulative accuracy in both known and unknown web server settings and 92% accuracy in estimating the total number of responses in unseen QUIC traces.
- Score: 7.795761092358769
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: QUIC, a new and increasingly used transport protocol, enhances TCP by providing better security, performance, and features like stream multiplexing. These features, however, also impose challenges for network middle-boxes that need to monitor and analyze web traffic. This paper proposes a novel solution for estimating the number of HTTP/3 responses in a given QUIC connection by an observer. This estimation reveals server behavior, client-server interactions, and data transmission efficiency, which is crucial for various applications such as designing a load balancing solution and detecting HTTP/3 flood attacks. The proposed scheme transforms QUIC connection traces into a sequence of images and trains machine learning (ML) models to predict the number of responses. Then, by aggregating images of a QUIC connection, an observer can estimate the total number of responses. As the problem is formulated as a discrete regression problem, we introduce a dedicated loss function. The proposed scheme is evaluated on a dataset of over seven million images, generated from $100,000$ traces collected from over $44,000$ websites over a four-month period, from various vantage points. The scheme achieves up to 97\% cumulative accuracy in both known and unknown web server settings and 92\% accuracy in estimating the total number of responses in unseen QUIC traces.
Related papers
- Exploring QUIC Dynamics: A Large-Scale Dataset for Encrypted Traffic Analysis [7.795761092358769]
We introduce VisQUIC, a labeled dataset comprising over 100,000 QUIC traces from more than 44,000 websites (URLs)
These traces provide the foundation for generating more than seven million images, with parameters of window length, pixel resolution, normalization, and labels.
To illustrate the dataset's potential, we offer a use-case example of an observer estimating the number of HTTP/3 responses/requests pairs in a given QUIC.
arXiv Detail & Related papers (2024-09-30T10:50:12Z) - Match and Locate: low-frequency monocular odometry based on deep feature
matching [0.65268245109828]
We introduce a novel approach for the robotic odometry which only requires a single camera.
The approach is based on matching image features between the consecutive frames of the video stream using deep feature matching models.
We evaluate the performance of the approach in the AISG-SLA Visual Localisation Challenge and find that while being computationally efficient and easy to implement our method shows competitive results.
arXiv Detail & Related papers (2023-11-16T17:32:58Z) - Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection [54.041049052843604]
We present STEMD, a novel end-to-end framework that enhances the DETR-like paradigm for multi-frame 3D object detection.
First, to model the inter-object spatial interaction and complex temporal dependencies, we introduce the spatial-temporal graph attention network.
Finally, it poses a challenge for the network to distinguish between the positive query and other highly similar queries that are not the best match.
arXiv Detail & Related papers (2023-07-01T13:53:14Z) - Robust Semi-supervised Federated Learning for Images Automatic
Recognition in Internet of Drones [57.468730437381076]
We present a Semi-supervised Federated Learning (SSFL) framework for privacy-preserving UAV image recognition.
There are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules.
We propose an aggregation rule based on the frequency of the client's participation in training, namely the FedFreq aggregation rule.
arXiv Detail & Related papers (2022-01-03T16:49:33Z) - Predicting Bandwidth Utilization on Network Links Using Machine Learning [0.966840768820136]
We present a solution to predict the bandwidth utilization between different network links with a very high accuracy.
A simulated network is created to collect data related to the performance of the network links on every interface.
We show that the proposed solution can be used in real time with a reaction managed by a Software-Defined Networking (SDN) platform.
arXiv Detail & Related papers (2021-12-04T19:47:41Z) - Learning from Images: Proactive Caching with Parallel Convolutional
Neural Networks [94.85780721466816]
A novel framework for proactive caching is proposed in this paper.
It combines model-based optimization with data-driven techniques by transforming an optimization problem into a grayscale image.
Numerical results show that the proposed scheme can reduce 71.6% computation time with only 0.8% additional performance cost.
arXiv Detail & Related papers (2021-08-15T21:32:47Z) - On Improving Deep Learning Trace Analysis with System Call Arguments [1.3299507495084417]
Kernel traces are sequences of low-level events comprising a name and multiple arguments.
We introduce a general approach to learning a representation of the event names along with their arguments using both embedding and encoding.
arXiv Detail & Related papers (2021-03-11T19:26:34Z) - Object Tracking through Residual and Dense LSTMs [67.98948222599849]
Deep learning-based trackers based on LSTMs (Long Short-Term Memory) recurrent neural networks have emerged as a powerful alternative.
DenseLSTMs outperform Residual and regular LSTM, and offer a higher resilience to nuisances.
Our case study supports the adoption of residual-based RNNs for enhancing the robustness of other trackers.
arXiv Detail & Related papers (2020-06-22T08:20:17Z) - Corella: A Private Multi Server Learning Approach based on Correlated
Queries [30.3330177204504]
We propose $textitCorella$ as an alternative approach to protect the privacy of data.
The proposed scheme relies on a cluster of servers, where at most $T in mathbbN$ of them may collude, each running a learning model.
The variance of the noise is set to be large enough to make the information leakage to any subset of up to $T$ servers information-theoretically negligible.
arXiv Detail & Related papers (2020-03-26T17:44:00Z) - Taurus: A Data Plane Architecture for Per-Packet ML [59.1343317736213]
We present the design and implementation of Taurus, a data plane for line-rate inference.
Our evaluation of a Taurus switch ASIC shows that Taurus operates orders of magnitude faster than a server-based control plane.
arXiv Detail & Related papers (2020-02-12T09:18:36Z) - Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [71.86955275376604]
We propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem.
We design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection.
We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
arXiv Detail & Related papers (2020-01-10T05:29:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.