Application-layer Characterization and Traffic Analysis for Encrypted QUIC Transport Protocol
- URL: http://arxiv.org/abs/2310.10676v1
- Date: Tue, 10 Oct 2023 20:09:46 GMT
- Title: Application-layer Characterization and Traffic Analysis for Encrypted QUIC Transport Protocol
- Authors: Qianqian Zhang, Chi-Jiun Su,
- Abstract summary: We propose a novel rule-based approach to estimate the application-level traffic attributes without decrypting QUIC packets.
Based on the size, timing, and direction information, our proposed algorithm analyzes the associated network traffic.
The inferred HTTP attributes can be used to evaluate the QoE of application-layer services and identify the service categories for traffic classification in the encrypted QUIC connections.
- Score: 14.40132345175898
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quick UDP Internet Connection (QUIC) is an emerging end-to-end encrypted, transport-layer protocol, which has been increasingly adopted by popular web services to improve communication security and quality of experience (QoE) towards end-users. However, this tendency makes the traffic analysis more challenging, given the limited information in the QUIC packet header and full encryption on the payload. To address this challenge, a novel rule-based approach is proposed to estimate the application-level traffic attributes without decrypting QUIC packets. Based on the size, timing, and direction information, our proposed algorithm analyzes the associated network traffic to infer the identity of each HTTP request and response pair, as well as the multiplexing feature in each QUIC connection. The inferred HTTP attributes can be used to evaluate the QoE of application-layer services and identify the service categories for traffic classification in the encrypted QUIC connections.
Related papers
- Lens: A Foundation Model for Network Traffic in Cybersecurity [18.372089452482133]
Lens is a foundation model for network traffic that leverages the T5 architecture to learn the pre-trained representations from large-scale unlabeled data.
We design a novel loss that combines three distinct tasks: Masked Span Prediction (MSP), Packet Order Prediction (POP), and Homologous Traffic Prediction (HTP)
arXiv Detail & Related papers (2024-02-06T02:45:13Z) - Prevention of cyberattacks in WSN and packet drop by CI framework and
information processing protocol using AI and Big Data [0.0]
This study integrates a cognitive intelligence (CI) framework, an information processing protocol, and sophisticated artificial intelligence (AI) and big data analytics approaches.
The framework is capable of detecting and preventing several forms of assaults, including as denial-of-service (DoS) attacks, node compromise, and data tampering.
It is highly resilient to packet drop occurrences, which improves the WSN's overall reliability and performance.
arXiv Detail & Related papers (2023-06-15T19:00:39Z) - Matching Game for Optimized Association in Quantum Communication
Networks [65.16483325184237]
This paper proposes a swap-stable request-QS association algorithm for quantum switches.
It achieves a near-optimal (within 5%) performance in terms of the percentage of served requests.
It is shown to be scalable and maintain its near-optimal performance even when the size of the QCN increases.
arXiv Detail & Related papers (2023-05-22T03:39:18Z) - Optimization of Image Transmission in a Cooperative Semantic
Communication Networks [68.2233384648671]
A semantic communication framework for image transmission is developed.
Servers cooperatively transmit images to a set of users utilizing semantic communication techniques.
A multimodal metric is proposed to measure the correlation between the extracted semantic information and the original image.
arXiv Detail & Related papers (2023-01-01T15:59:13Z) - Utilizing Background Knowledge for Robust Reasoning over Traffic
Situations [63.45021731775964]
We focus on a complementary research aspect of Intelligent Transportation: traffic understanding.
We scope our study to text-based methods and datasets given the abundant commonsense knowledge.
We adopt three knowledge-driven approaches for zero-shot QA over traffic situations.
arXiv Detail & Related papers (2022-12-04T09:17:24Z) - Multi-view Multi-label Anomaly Network Traffic Classification based on
MLP-Mixer Neural Network [55.21501819988941]
Existing network traffic classification based on convolutional neural networks (CNNs) often emphasizes local patterns of traffic data while ignoring global information associations.
We propose an end-to-end network traffic classification method.
arXiv Detail & Related papers (2022-10-30T01:52:05Z) - Federated Semi-Supervised Classification of Multimedia Flows for 3D
Networks [0.16799377888527683]
Traffic classification is crucial for traffic shaping, network slicing, and Quality of Service (QoS) management.
3D networks offer multiple routes that can guarantee different levels of anomaly detection.
In this paper, a cooperative feature selection and feature reduction learning scheme is proposed to classify network traffic in a semi-supervised manner.
arXiv Detail & Related papers (2022-05-01T20:18:07Z) - AI-aided Traffic Control Scheme for M2M Communications in the Internet
of Vehicles [61.21359293642559]
The dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies.
We consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it.
arXiv Detail & Related papers (2022-03-05T10:54:05Z) - Fast and Secure Routing Algorithms for Quantum Key Distribution Networks [5.659290426197763]
We consider the problem of secure packet routing at the maximum achievable rate in Quantum Key Distribution (QKD) networks.
We propose a new secure throughput-optimal policy called Tandem Queue Decomposition (TQD)
We show that the TQD policy solves the problem of secure and efficient packet routing for a broad class of traffic, including unicast, broadcast, multicast, and anycast.
arXiv Detail & Related papers (2021-09-16T12:29:41Z) - FENXI: Deep-learning Traffic Analytics at the Edge [69.34903175081284]
We present FENXI, a system to run complex analytics by leveraging TPU.
FENXI decouples operations and traffic analytics which operates at different granularities.
Our analysis shows that FENXI can sustain forwarding line rate traffic processing requiring only limited resources.
arXiv Detail & Related papers (2021-05-25T08:02:44Z) - Website fingerprinting on early QUIC traffic [12.18618920843956]
We study the vulnerabilities of GQUIC, IQUIC, and HTTPS to WFP attacks from the perspective of traffic analysis.
GQUIC is the most vulnerable to WFP attacks among GQUIC, IQUIC, and HTTPS, while IQUIC is more vulnerable than HTTPS, but the vulnerability of the three protocols is similar in the normal full traffic scenario.
arXiv Detail & Related papers (2021-01-28T08:53:51Z)
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.