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19 results for “QUIC”
BRAIN Journal-About the Design of QUIC Firefox Transport Protocol-Figure 2. QUIC vs TLS handshake protocol
<p>Figure 2 describes a sequence diagram using QUIC VS TLS handshake protocol. The right<br> side of Figure 2 shows the seven steps of calls and returns until a HTTP Get() method is<br> successfully implemented using a TLS handshake. In contrast, on the left side of Figure 2, we see<br> the implementation of HTTP Get() method using a single call of QUIC handshake.<br> Firefox is a completely open source browser with a tremendous community support. The<br> latest version of Firefox supports TLS 1.3 protocol in an experimental stage. The primary purpose<br> of this study was integrating the QUIC protocol in the Firefox web browser. The source code of this<br> software product is as large as 650MB. </p> <p> </p>
BRAIN Journal-About the Design of QUIC Firefox Transport Protocol-Figure 1. TLS vs QUIC protocol stack
<p>QUIC addresses many network problems such as the Head Of Line (HOL) blocking as well as the TCP reconnection over a subnet/network change. In addition, QUIC has many features such as connection IDs, which can overcome the challenge of changing networks. In this way, if someone switches from a WIFI network to a cellular network, the connection to the server will not be broken or lost. Paper (Langley and Chang, 2016) described the QUIC crypto protocol, representing the part of QUIC that provides transport security to a connection. The QUIC crypto protocol is now replaced by TLS 1.3. Currently, QUIC provides security of TLS 1.3 (in an experimental stage), considered the highest security standards for the communication protocols (Valsorda; 2016). </p> <p>Figure 1 describes the TLS vs. QUIC protocol user level stack in the context of application layer and transport layer.</p>
CESNET-QUIC22: A large one-month QUIC network traffic dataset from backbone lines
<p><strong>Please refer to the original data article for further data description: </strong>Jan Luxemburk et al. CESNET-QUIC22: A large one-month QUIC network traffic dataset from backbone lines, Data in Brief, 2023, 108888, ISSN 2352-3409, <a href="https://doi.org/10.1016/j.dib.2023.108888">https://doi.org/10.1016/j.dib.2023.108888</a>. </p><p><strong>We recommend using the</strong> <strong>CESNET DataZoo python library, which facilitates the work with large network traffic datasets. </strong>More information about the DataZoo project can be found in the GitHub repository <a href="https://github.com/CESNET/cesnet-datazoo">https://github.com/CESNET/cesnet-datazoo</a>.</p><p>The QUIC (Quick UDP Internet Connection) protocol has the potential to replace TLS over TCP, which is the standard choice for reliable and secure Internet communication. Due to its design that makes the inspection of QUIC handshakes challenging and its usage in HTTP/3, there is an increasing demand for research in QUIC traffic analysis. This dataset contains one month of QUIC traffic collected in an ISP backbone network, which connects 500 large institutions and serves around half a million people. The data are delivered as enriched flows that can be useful for various network monitoring tasks. The provided server names and packet-level information allow research in the encrypted traffic classification area. Moreover, included QUIC versions and user agents (smartphone, web browser, and operating system identifiers) provide information for large-scale QUIC deployment studies.</p><p><strong>Data capture</strong> The data was captured in the flow monitoring infrastructure of the <a href="https://www.cesnet.cz">CESNET2</a> network. The capturing was done for four weeks between 31.10.2022 and 27.11.2022. The following list provides per-week flow count, capture period, and uncompressed size:</p><ul><li><strong>W-2022-44</strong><ul><li>Uncompressed Size: 19 GB</li><li>Capture Period: 31.10.2022 - 6.11.2022</li><li>Number of flows: 32.6M</li></ul></li><li><strong>W-2022-45</strong><ul><li>Uncompressed Size: 25 GB</li><li>Capture Period: 7.11.2022 - 13.11.2022</li><li>Number of flows: 42.6M</li></ul></li><li><strong>W-2022-46</strong><ul><li>Uncompressed Size: 20 GB</li><li>Capture Period: 14.11.2022 - 20.11.2022</li><li>Number of flows: 33.7M</li></ul></li><li><strong>W-2022-47</strong><ul><li>Uncompressed Size: 25 GB</li><li>Capture Period: 21.11.2022 - 27.11.2022</li><li>Number of flows: 44.1M</li></ul></li><li><strong>CESNET-QUIC22 </strong><ul><li>Uncompressed Size: 89 GB</li><li>Capture Period: 31.10.2022 - 27.11.2022</li><li>Number of flows: 153M</li></ul></li></ul><p> </p><p><strong>Data description</strong> The dataset consists of network flows describing encrypted QUIC communications. Flows were created using <a href="https://github.com/CESNET/ipfixprobe">ipfixprobe</a> flow exporter and are extended with packet metadata sequences, packet histograms, and with fields extracted from the QUIC Initial Packet, which is the first packet of the QUIC connection handshake. The extracted handshake fields are the Server Name Indication (SNI) domain, the used version of the QUIC protocol, and the user agent string that is available in a subset of QUIC communications.</p><p><strong>Packet Sequences</strong> Flows in the dataset are extended with sequences of packet sizes, directions, and inter-packet times. For the packet sizes, we consider payload size after transport headers (UDP headers for the QUIC case). Packet directions are encoded as ±1, <i>+1</i> meaning a packet sent from client to server, and <i>-1</i> a packet from server to client. Inter-packet times depend on the location of communicating hosts, their distance, and on the network conditions on the path. However, it is still possible to extract relevant information that correlates with user interactions and, for example, with the time required for an API/server/database to process the received data and generate the response to be sent in the next packet. Packet metadata sequences have a length of 30, which is the default setting of the used flow exporter. We also derive three fields from each packet sequence: its length, time duration, and the number of roundtrips. The roundtrips are counted as the number of changes in the communication direction (from packet directions data); in other words, each client request and server response pair counts as one roundtrip.</p><p><strong>Flow statistics</strong> Flows also include standard flow statistics, which represent aggregated information about the entire bidirectional flow. The fields are: the number of transmitted bytes and packets in both directions, the duration of flow, and packet histograms. Packet histograms include binned counts of packet sizes and inter-packet times of the entire flow in both directions (more information in the <a href="https://github.com/CESNET/ipfixprobe/tree/master#phists">PHISTS plugin documentation</a> There are eight bins with a logarithmic scale; the intervals are 0-15, 16-31, 32-63, 64-127, 128-255, 256-511, 512-1024, >1024 [ms or B]. The units are milliseconds for inter-packet times and bytes for packet sizes. Moreover, each flow has its end reason - either it was idle, reached the active timeout, or ended due to other reasons. This corresponds with the official <a href="https://www.iana.org/assignments/ipfix/ipfix.xhtml#ipfix-flow-end-reason">IANA IPFIX-specified values</a>. The <i>FLOW_ENDREASON_OTHER</i> field represents the <i>forced end</i> and <i>lack of resources</i> reasons. The <i>end of flow detected</i> reason is not considered because it is not relevant for UDP connections.</p><p><strong>Dataset structure</strong> The dataset flows are delivered in compressed CSV files. CSV files contain one flow per row; data columns are summarized in the provided list below. For each flow data file, there is a JSON file with the number of saved and seen (before sampling) flows per service and total counts of all received (observed on the CESNET2 network), service (belonging to one of the dataset's services), and saved (provided in the dataset) flows. There is also the <i>stats-week.json</i> file aggregating flow counts of a whole week and the <i>stats-dataset.json</i> file aggregating flow counts for the entire dataset. Flow counts before sampling can be used to compute sampling ratios of individual services and to resample the dataset back to the original service distribution. Moreover, various dataset statistics, such as feature distributions and value counts of QUIC versions and user agents, are provided in the <i>dataset-statistics</i> folder. The mapping between services and service providers is provided in the <i>servicemap.csv</i> file, which also includes SNI domains used for ground truth labeling. The following list describes flow data fields in CSV files:</p><ul><li><strong>ID:</strong> Unique identifier</li><li><strong>SRC_IP:</strong> Source IP address</li><li><strong>DST_IP:</strong> Destination IP address</li><li><strong>DST_ASN:</strong> Destination Autonomous System number</li><li><strong>SRC_PORT:</strong> Source port</li><li><strong>DST_PORT:</strong> Destination port</li><li><strong>PROTOCOL:</strong> Transport protocol</li><li><strong>QUIC_VERSION QUIC:</strong> protocol version</li><li><strong>QUIC_SNI:</strong> Server Name Indication domain</li><li><strong>QUIC_USER_AGENT:</strong> User agent string, if available in the QUIC Initial Packet</li><li><strong>TIME_FIRST:</strong> Timestamp of the first packet in format YYYY-MM-DDTHH-MM-SS.ffffff</li><li><strong>TIME_LAST:</strong> Timestamp of the last packet in format YYYY-MM-DDTHH-MM-SS.ffffff</li><li><strong>DURATION:</strong> Duration of the flow in seconds</li><li><strong>BYTES:</strong> Number of transmitted bytes from client to server</li><li><strong>BYTES_REV:</strong> Number of transmitted bytes from server to client</li><li><strong>PACKETS:</strong> Number of packets transmitted from client to server</li><li><strong>PACKETS_REV:</strong> Number of packets transmitted from server to client</li><li><strong>PPI:</strong> Packet metadata sequence in the format: [[inter-packet times], [packet directions], [packet sizes]]</li><li><strong>PPI_LEN:</strong> Number of packets in the PPI sequence</li><li><strong>PPI_DURATION:</strong> Duration of the PPI sequence in seconds</li><li><strong>PPI_ROUNDTRIPS:</strong> Number of roundtrips in the PPI sequence</li><li><strong>PHIST_SRC_SIZES:</strong> Histogram of packet sizes from client to server</li><li><strong>PHIST_DST_SIZES: </strong>Histogram of packet sizes from server to client</li><li><strong>PHIST_SRC_IPT: </strong>Histogram of inter-packet times from client to server</li><li><strong>PHIST_DST_IPT:</strong> Histogram of inter-packet times from server to client</li><li><strong>APP:</strong> Web service label</li><li><strong>CATEGORY:</strong> Service category</li><li><strong>FLOW_ENDREASON_IDLE:</strong> Flow was terminated because it was idle</li><li><strong>FLOW_ENDREASON_ACTIVE:</strong> Flow was terminated because it reached the active timeout</li><li><strong>FLOW_ENDREASON_OTHER:</strong> Flow was terminated for other reasons</li></ul><p> </p><p><strong>Link to other CESNET datasets</strong></p><ul><li><a href="https://www.liberouter.org/technology-v2/tools-services-datasets/datasets/">https://www.liberouter.org/technology-v2/tools-services-datasets/datasets/</a></li><li><a href="https://github.com/CESNET/cesnet-datazoo">https://github.com/CESNET/cesnet-datazoo</a></li></ul><p><strong>Please cite the original data article:</strong></p><blockquote><p>@article{CESNETQUIC22, author = {Jan Luxemburk and Karel Hynek and Tomáš Čejka and Andrej Lukačovič and Pavel Šiška}, title = {CESNET-QUIC22: a large one-month QUIC network traffic dataset from backbone lines}, journal = {Data in Brief}, pages = {108888}, year = {2023}, issn = {2352-3409}, doi = {https://doi.org/10.1016/j.dib.2023.108888}, url = {https://www.sciencedirect.com/science/article/pii/S2352340923000069} }</p></blockquote>
BRAIN Journal-About the Design of QUIC Firefox Transport Protocol-Figure 3. An example of a network switch
<p> Seamless network transition: While switching networks, QUIC can adapt to a network or a subnet switch (illustrated in Figure 3). This means that if the IP address of the device is changed, then the QUIC connection is not broken or lost. Unlike the TCP protocol where the connection is defined by the IP address and a port number, QUIC connections are defined by a connection ID. Whenever a network switch occurs, QUIC detects it and sends a piggybacked notification to the other party by indicating the new IP address and connection ID. In this way, the communication can resume normally. In contrast, TCP a new connection has to be established. </p>
BRAIN Journal-About the Design of QUIC Firefox Transport Protocol-Figure 5. The head-of-line blocking in TCP/TLS
<p>Multiplexed streams: After establishing a connection, each QUIC connection forms a stream for every needed resource. Streams can be represented by a two way communication channel abstraction. Each and every stream has a stream ID. All the data and ACK packets are sent through as QUIC streams (illustrated in Figure 5). </p>
BRAIN Journal-About the Design of QUIC Firefox Transport Protocol-Figure 4. QUIC Streams and FEC packets
<p>The FEC mechanism is flexible, which means that the protocol adapts the number of packets that need to be incorporated in one FEC packet according to the packets that are lost. The number of packets in one FEC packet is inversely related to the number of packets lost (illustrated in Figure 4). </p>
Diagnosis of prion diseases by RT-QuIC results in improved surveillance
<p><span><u>Objective:</u> We present the National Prion Disease Pathology Surveillance Center's (NPDPSC) experience using cerebrospinal fluid (CSF) real time quaking induced conversion (RT-QuIC) as a diagnostic test, examine factors associated with false negative RT-QuIC results, and investigate RT-QuIC's impact on prion disease surveillance. </span></p> <p> </p> <p><span><u>Methods:</u> Between May 2015-April 2018, the NPDPSC received 10,498 CSF specimens that were included in the study. Sensitivity and specificity analyses were performed using 567 autopsy verified cases. Prion disease type, demographic characteristics, specimen color, and time variables were examined for association with RT-QuIC results. The effect of including positive RT-QuIC cases in prion disease surveillance was examined.</span></p> <p> </p> <p><span><u>Results:</u> The diagnostic sensitivity and specificity of RT-QuIC across all prion diseases was 90.3% and 98.5%, respectively. Diagnostic sensitivity was lower for fatal familial insomnia, Gerstmann-Sträussler-Scheinker disease, sporadic fatal insomnia, variably protease sensitive prionopathy, and the VV1 and MM2 subtypes of sCJD. Individuals with prion disease and negative RT-QuIC results were younger, had elevated tau levels, and non-elevated 14-3-3 levels compared to RT-QuIC positive cases. Sensitivity was high throughout the disease course. Some cases that initially tested RT-QuIC negative had a subsequent specimen test positive. Including positive RT-QuIC cases in surveillance statistics increased laboratory-based case ascertainment of prion disease by 90% over autopsy alone. </span></p> <p> </p> <p><span><u>Conclusions:</u> RT-QuIC has high sensitivity and specificity for diagnosing prion diseases. Sensitivity limitations are associated with prion disease type, age, and related CSF diagnostic results. RT-QuIC greatly improves laboratory-based prion disease ascertainment for surveillance purposes. </span></p> <p> </p> <p><span><u>Classification of Evidence:</u> This study provides Class III evidence that 2<sup>nd</sup> generation real time quaking-induced conversion (RT-QuIC) identifies prion disease with sensitivity of 90.3% and specificity of 98.5%, among patients being screened for these diseases due to concerning symptoms. </span></p>
Dataset to paper "ECN with QUIC: Challenges in the Wild"
<p>In the following you can find the according measurements of our IMC'23 paper "ECN with QUIC: Challenges in the Wild" for the publicly available toplists. Please note that we cannot publicly share our CZDS-based measurements due to their proprietary license.</p> <p>All data is available as line-delimited json files which have been zstandard compressed.</p> <p>If you use any portion of our work, please consider citing our publication.</p> <pre><code>@Inproceedings { 2023-sander-quic-ecn, title = {ECN with QUIC: Challenges in the Wild}, year = {2023}, month = {10}, publisher = {ACM}, booktitle = {Proceedings of the Internet Measurement Conference (IMC '23)}, DOI = {10.1145/3618257.3624821}, author = {Sander, Constantin and Kunze, Ike and Bl{\"o}cher, Leo and Kosek, Mike and Wehrle, Klaus} }</code></pre>
Diagnosis of prion diseases by RT-QuIC results in improved surveillance
Open the record for dataset details and reuse information.
A Quic(k) Security Overview: A Literature Research on Implemented Security Recommendations
<p>Contains additional material and the slideset for the ARES 2023 paper <a href="https://dl.acm.org/doi/10.1145/3600160.3605164" rel="nofollow">A Quic(k) Security Overview: A Literature Research on Implemented Security Recommendations</a>.</p>
Establishing Alpha-synuclein RT-QuIC Assay as a Diagnostic Technique in REM Sleep Behaviour Disorder
ClinicalTrials.gov study NCT04266457. IPD Sharing: UNDECIDED. Countries: 1. Publications: 24.
The Packet Number Space Debate in Multipath QUIC: Artifacts
<p>The measurement data and a Jupyter notebook to reproduce the graphs in the paper.</p>
QCSD: QUIC Client-Side Website-Fingerprinting Defence Dataset
<p>Contains live-defended, defended under simulation, and undefended website-fingerprinting traces associated with the paper "QCSD: QUIC Client-Side Website-Fingerprinting Defence Framework" (USENIX Security '22).</p>
Does It Spin? On the Adoption and Use of QUIC's Spin Bit
<pre>This repository contains the extracted raw spin bit values as used in our paper as well as golang scripts for analyzing them. ## Publication * Ike Kunze, Constantin Sander, and Klaus Wehrle: *Does It Spin? On the Adoption and Use of QUIC’s Spin Bit. In Proceedings of the ACM Internet Measurement Conference 2023 (IMC '23), 2023. If you use any portion of our work, please consider citing our publication. ``` @Inproceedings { 2023-kunze-spin-bit-in-the-wild, author = {Kunze, Ike and Sander, Constantin and Wehrle, Klaus}, title = {Does It Spin? On the Adoption and Use of QUIC’s Spin Bit}, booktitle = {Proceedings of the Internet Measurement Conference (IMC '23)}, year = {2023}, month = {10}, publisher = {ACM}, doi = {TBD} } ```</pre> <p>## Use</p> <p>1. Decompress the dataset:</p> <p>For using the data set, first untar the data.</p> <p>```<br> tar -xzf <a href="https://zenodo.org/api/files/8677e735-1668-46d6-afcf-2f57b9e4e0b2/extracted-spin-bit-values.tar.gz?versionId=0b2e3f22-4a1b-437d-8e64-47121c27780c">extracted-spin-bit-values.tar.gz</a></p> <p>```</p> <p>1.1 Data format:</p> <p>The files contained in the data set each contain line-wise dictionaries with the following content:</p> <p>- QlogRTTs: RTTs as provided by the QUIC stack via qlog</p> <p>- SentSpinBits: all packets sent out by our client, each containing a timestamp, the spinbit value (Val) and the packet number (PN)</p> <p>- RecvSpinBits: all packets received by our client, each containing a timestamp, the spinbit value (Val) and the packet number (PN)</p> <p> 2. Compile golang module:</p> <p>We use a golang module for analyzing the data.<br> Compile the module using</p> <p>```<br> go build -o analyze-spinbit analyze-spinbit.go<br> ```</p> <p>3. Analyze individual files: </p> <p>You can analyze the files by running the compiled `analyze-spinbit` program on individual files.<br> <br> ```<br> cat FILE_TO_ANALYZE | ./analyze-spinbit -buffer 1073741824 -logfile LOGFILENAME -outputfilebasename OUTPUTFILENAME --batchsize 1000000 --outputsuffix json<br> ```</p> <p>4. Output format:</p> <p>Line-wise dictionaries containing:</p> <p>- which spin mode was detected</p> <p>- number of considered packets</p> <p>- real RTTs based on QUIC stack estimates</p> <p>- minimum RTT based on QUIC stack estimates</p> <p>- RTTs as calculated via the spin bit</p> <p>- RTTs as calculated via the spin bit when reordering the packets using the packet numbers</p>
Does It Spin? On the Adoption and Use of QUIC's Spin Bit -- Raw zgrab2 toplist results
<p>In the following you can find the raw measurements of our IMC'23 paper "Does It Spin? On the Adoption and Use of QUIC’s Spin Bit" for the publicly available toplists.</p>
Alpha-synuclein Rt-quic and Neurologic Symptoms in Persons With idiOpathic anosMiA
ClinicalTrials.gov study NCT05740683. IPD Sharing: NO. Countries: 1. Publications: 0.
Study of Gemcitabine, Cisplatin, AB680 and AB122 During First Line Treatment of Advanced Biliary Tract Cancers (QUIC)
ClinicalTrials.gov study NCT06048133. IPD Sharing: Not stated. Countries: 1. Publications: 0.
DATASET RELATED TO ARTICLE "The Alpha-Synuclein RT-QuIC Products Generated by the Olfactory Mucosa of Patients with Parkinson's Disease and Multiple System Atrophy Induce Inflammatory Responses in SH-SY5Y Cells"
<p>RAW DATA RELATED TO ARTICLE AT TITLE</p>
Dataset relate to article "Discrimination of MSA-P and MSA-C by RT-QuIC analysis of olfactory mucosa: the first assessment of assay reproducibility between two specialized laboratories"
<p>raw data related to article at title</p>
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