Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

ITC-net-audio-5: an audio streaming dataset for application identification in network traffic classification.

ABSTRACT:

Objectives

An essential aspect of network traffic classification is application identification. This involves capturing and analyzing the traffic patterns of applications. There are a few publicly available datasets that specifically capture streaming data from network-based applications. Therefore, our objective is to generate an up-to-date dataset with a focus on audio streaming data. This dataset can be a valuable resource for identifying audio streaming applications in the field of network traffic classification.

Data description

The dataset contains network traffic captured during audio streaming communications on five trending applications: Google Meet, Skype, Telegram, WhatsApp, and SoundCloud. It includes 500 files in PCAP format captured by Wireshark and PCAPdroid tools during voice calls and online music playback. The concurrent utilization of these tools facilitates the avoidance of capturing background traffic.

SUBMITTER: Nikbakht M

PROVIDER: S-EPMC10900609 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

ITC-net-audio-5: an audio streaming dataset for application identification in network traffic classification.

Nikbakht Mohammad M Teimouri Mehdi M

BMC research notes 20240227 1

<h4>Objectives</h4>An essential aspect of network traffic classification is application identification. This involves capturing and analyzing the traffic patterns of applications. There are a few publicly available datasets that specifically capture streaming data from network-based applications. Therefore, our objective is to generate an up-to-date dataset with a focus on audio streaming data. This dataset can be a valuable resource for identifying audio streaming applications in the field of n ...[more]

PMID: 38414004

Similar Datasets

YouTube Dataset on Mobile Streaming for Internet Traffic Modeling and Streaming Analysis

Project description:Around 4.9 billion Internet users worldwide watch billions of hours of online video every day. As a result, streaming is by far the predominant type of traffic in communication networks. According to Google statistics, three out of five video views come from mobile devices. Thus, in view of the continuous technological advances in end devices and increasing mobile use, datasets for mobile streaming are indispensable in research but only sparsely dealt with in literature so far. With this public dataset, we provide 1,081 hours of time-synchronous video measurements at network, transport, and application layer with the native YouTube streaming client on mobile devices. The dataset includes 80 network scenarios with 171 different individual bandwidth settings measured in 5,181 runs with limited bandwidth, 1,939 runs with emulated 3 G/4 G traces, and 4,022 runs with pre-defined bandwidth changes. This corresponds to 332 GB video payload. We present the most relevant quality indicators for scientific use, i.e., initial playback delay, streaming video quality, adaptive video quality changes, video rebuffering events, and streaming phases. Measurement(s)Mobile video streaming with YouTubeTechnology Type(s)Tcpdump and publicly available wrapper app for YouTube dataSample Characteristic - Environmentmobile phone • YouTube appSample Characteristic - LocationGermany • Europe

| S-EPMC9192599 | biostudies-literature

Dataset for file fragment classification of audio file formats.

Project description:OBJECTIVES:File fragment classification of audio file formats is a topic of interest in network forensics. There are a few publicly available datasets of files with audio formats. Therewith, there is no public dataset for file fragments of audio file formats. So, a big research challenge in file fragment classification of audio file formats is to compare the performance of the developed methods over the same datasets. DATA DESCRIPTION:In this study, we present a dataset that contains file fragments of 20 audio file formats: AMR, AMR-WB, AAC, AIFF, CVSD, FLAC, GSM-FR, iLBC, Microsoft ADPCM, MP3, PCM, WMA, A-Law, µ-Law, G.726, G.729, Microsoft GSM, OGG Vorbis, OPUS, and SPEEX. Corresponding to each format, the dataset contains the file fragments of audio files with different compression settings. For each pair of file format and compression setting, 210 file fragments are provided. Totally, the dataset contains 20,160 file fragments.

| S-EPMC6925457 | biostudies-literature

NanoChest-Net: A Simple Convolutional Network for Radiological Studies Classification.

Project description:The new coronavirus disease (COVID-19), pneumonia, tuberculosis, and breast cancer have one thing in common: these diseases can be diagnosed using radiological studies such as X-rays images. With radiological studies and technology, computer-aided diagnosis (CAD) results in a very useful technique to analyze and detect abnormalities using the images generated by X-ray machines. Some deep-learning techniques such as a convolutional neural network (CNN) can help physicians to obtain an effective pre-diagnosis. However, popular CNNs are enormous models and need a huge amount of data to obtain good results. In this paper, we introduce NanoChest-net, which is a small but effective CNN model that can be used to classify among different diseases using images from radiological studies. NanoChest-net proves to be effective in classifying among different diseases such as tuberculosis, pneumonia, and COVID-19. In two of the five datasets used in the experiments, NanoChest-net obtained the best results, while on the remaining datasets our model proved to be as good as baseline models from the state of the art such as the ResNet50, Xception, and DenseNet121. In addition, NanoChest-net is useful to classify radiological studies on the same level as state-of-the-art algorithms with the advantage that it does not require a large number of operations.

| S-EPMC8145173 | biostudies-literature

Ar-DAD: Arabic diversified audio dataset.

Project description:The automatic identification and verification of speakers through representative audio continue to gain the attention of many researchers with diverse domains of applications. Despite this diversity, the availability of classified and categorized multi-purpose Arabic audio libraries is scarce. Therefore, we introduce a large Arabic-based audio clips dataset (15810 clips) of 30 popular reciters cantillating 37 chapters from the Holy Quran. These chapters have a variable number of verses saved to different subsequent folders, where each verse is allocated one folder containing 30 audio clips for the declared reciters covering the same textual content. An additional 397 audio clips for 12 competent imitators of the top reciters are collected based on popularity and number of views/downloads to allow for cross-comparison of text, reciters, and authenticity. Based on the volume, quality, and rich diversity of this dataset we anticipate a wide range of deployments for speaker identification, in addition to setting a new direction for the structure and organization of similar large audio clips dataset.

| S-EPMC7689366 | biostudies-literature

NIPS4Bplus: a richly annotated birdsong audio dataset.

Project description:Recent advances in birdsong detection and classification have approached a limit due to the lack of fully annotated recordings. In this paper, we present NIPS4Bplus, the first richly annotated birdsong audio dataset, that is comprised of recordings containing bird vocalisations along with their active species tags plus the temporal annotations acquired for them. Statistical information about the recordings, their species specific tags and their temporal annotations are presented along with example uses. NIPS4Bplus could be used in various ecoacoustic tasks, such as training models for bird population monitoring, species classification, birdsong vocalisation detection and classification.

| S-EPMC7924541 | biostudies-literature

CESNET-QUIC22: A large one-month QUIC network traffic dataset from backbone lines.

Project description: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.

| S-EPMC9851865 | biostudies-literature

DroneRF dataset: A dataset of drones for RF-based detection, classification and identification.

Project description:Modern technology has pushed us into the information age, making it easier to generate and record vast quantities of new data. Datasets can help in analyzing the situation to give a better understanding, and more importantly, decision making. Consequently, datasets, and uses to which they can be put, have become increasingly valuable commodities. This article describes the DroneRF dataset: a radio frequency (RF) based dataset of drones functioning in different modes, including off, on and connected, hovering, flying, and video recording. The dataset contains recordings of RF activities, composed of 227 recorded segments collected from 3 different drones, as well as recordings of background RF activities with no drones. The data has been collected by RF receivers that intercepts the drone's communications with the flight control module. The receivers are connected to two laptops, via PCIe cables, that runs a program responsible for fetching, processing and storing the sensed RF data in a database. An example of how this dataset can be interpreted and used can be found in the related research article "RF-based drone detection and identification using deep learning approaches: an initiative towards a large open source drone database" (Al-Sa'd et al., 2019).

| S-EPMC6727013 | biostudies-literature

PSG-Audio, a scored polysomnography dataset with simultaneous audio recordings for sleep apnea studies.

Project description:The sleep apnea syndrome is a chronic condition that affects the quality of life and increases the risk of severe health conditions such as cardiovascular diseases. However, the prevalence of the syndrome in the general population is considered to be heavily underestimated due to the restricted number of people seeking diagnosis, with the leading cause for this being the inconvenience of the current reference standard for apnea diagnosis: Polysomnography. To enhance patients' awareness of the syndrome, a great endeavour is conducted in the literature. Various home-based apnea detection systems are being developed, profiting from information in a restricted set of polysomnography signals. In particular, breathing sound has been proven highly effective in detecting apneic events during sleep. The development of accurate systems requires multitudinous datasets of audio recordings and polysomnograms. In this work, we provide the first open access dataset, comprising 212 polysomnograms along with synchronized high-quality tracheal and ambient microphone recordings. We envision this dataset to be widely used for the development of home-based apnea detection techniques and frameworks.

| S-EPMC8333307 | biostudies-literature

Encrypted audio dataset based on the Collatz conjecture.

Project description:In information security, one way to keep a secret content is through encryption. The objective is to alter the content so that it is not intelligible, and therefore only the intended user can reveal the secret content. With the aim to provide examples of encrypted audio data, we applied a novel method of encryption based on the Collatz conjecture in five hundred speech recordings (50 speakers, 10 different messages), and then five hundred encrypted audio files were obtained. The main characteristics of our encrypted recordings are as follows: the spectrogram is quasi-uniform, histograms have a repetitive pattern, average of samples is around -0.4, standard deviation is around 0.55; Shannon entropy is around 7.5 (for 8-bits per sample). The novelty of the results consists in obtaining a completely different behavior than natural speech recordings, i.e.: spectrogram with higher energy in low frequencies, histogram with Gaussian behavior, average of samples around 0, standard deviation around 0.11, entropy around 5.5. A more comprehensive analysis of our encrypted signals may be obtained from the article "High-uncertainty audio signal encryption based on the Collatz conjecture" in the Journal of Information Security and Applications.

| S-EPMC6811906 | biostudies-literature

Swedish civil air traffic control dataset.

Project description:The Swedish Civil Air Traffic Control (SCAT) dataset consists of 13 weeks of data collected from the area control in Sweden flight information region. The dataset consists of detailed data from almost 170,000 flights as well as airspace data and weather forecasts. The flight data includes system updated flight plans, clearances from air traffic control, surveillance data and trajectory prediction data. Each week of data is continuous but the 13 weeks are spread over one year to provide variations in weather and seasonal traffic patterns. The dataset does only include scheduled flights not involved in any incident reports. Sensitive data such as military and private flight has been removed. The SCAT dataset can be useful for any research related to air traffic control, e.g. analysis of transportation patterns, environmental impact, optimization and automation/AI.

| S-EPMC10293992 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data