Traffic analytics, rankings, and competitive metrics for linux.org as of September 2025

VLC Data: A Multi-Class Network Traffic Dataset Covering Diverse Applications and Platforms

Valencia Data (VLC Data) is a network traffic dataset collected from various applications and platforms. It includes both encrypted and, when applicable, unencrypted protocols, capturing realistic usage scenarios and application-specific behavior.

The dataset covers 18.5 hours, 58 pcapng files, and 24.26 GB, with traffic from:

Video streaming: Netflix and Prime Video (10–50 min) via Firefox.

Gaming: Roblox sessions on Windows (20–35 min), recorded outside of virtual machines, despite VM support.

Video conferencing: Microsoft Teams (20 min) via Firefox.

Web browsing: Wikipedia, BBC, Google, LinkedIn, Amazon, and OWIN6G (2–5 min) via Firefox or Chrome.

Audio streaming: Spotify (30–33 min) on multiple OS.

Web streaming: YouTube in 4K and Full HD (20–30 min).

This dataset is publicly available for traffic analysis across different apps, protocols, and systems.

Table Description:

Type Applications Platform Time [min] Comments Filename Size (MB)

Video Streaming Netflix Linux 10 Running Netflix on Firefox Browser netflix_linux_10m_01 95.1

Video Streaming Netflix Linux 20 Running Netflix on Firefox Browser netflix_linux_20m_01 167.7

Video Streaming Netflix Linux 20 Running Netflix on Firefox Browser netflix_linux_20m_02 237.9

Video Streaming Netflix Linux 20 Running Netflix on Firefox Browser netflix_linux_20m_03 212.6

Video Streaming Netflix Linux 25 Running Netflix on Firefox, but 2 min in Menu netflix_linux_25m_01 610.7

Video Streaming Netflix Linux 35 Running Netflix on Firefox, but 1 min in Menu netflix_linux_35m_01 534.8

Video Streaming Netflix Linux 50 Running Netflix on Firefox Browser netflix_linux_50m_01 660.9

Video Streaming Netflix Windows 10 Running Netflix on Firefox Browser netflix_windows_10m_01 132.1

Video Streaming Netflix Windows 20 Running Netflix on Firefox Browser netflix_windows_20m_01 506.4

Video Streaming Prime Video Linux 20 Running Prime Video on Firefox Browser prime_linux_20m_01 767.3

Video Streaming Prime Video Linux 20 Running Prime Video on Firefox Browser prime_linux_20m_02 569.3

Video Streaming Prime Video Windows 20 Running Prime Video on Firefox Browser prime_windows_20m_01 512.3

Video Streaming Prime Video Windows 20 Running Prime Video on Firefox Browser prime_windows_20m_02 364.2

Gaming Roblox Windows 20 Doesn't run in VM roblox_windows_20m_01 127.5

Gaming Roblox Windows 20 Doesn't run in VM roblox_windows_20m_02 378.5

Gaming Roblox Windows 20 Doesn't run in VM roblox_windows_20m_03 458.9

Gaming Roblox Windows 30 Doesn't run in VM roblox_windows_30m_01 519.8

Gaming Roblox Windows 30 Doesn't run in VM roblox_windows_30m_02 357.3

Gaming Roblox Windows 35 Doesn't run in VM roblox_windows_35m_01 880.4

Audio Streaming Spotify Linux 30 Running Spotify app on Ubuntu-Linux spotify_linux_30m_01 98.2

Audio Streaming Spotify Linux 30 Running Spotify app on Ubuntu-Linux spotify_linux_30m_02 112.2

Audio Streaming Spotify Linux 30 Running Spotify app on Ubuntu-Linux spotify_linux_30m_03 175.5

Audio Streaming Spotify Windows 30 Running Spotify app on Windows spotify_windows_30m_01 50.7

Audio Streaming Spotify Windows 30 Doesn't run in VM spotify_windows_30m_02 63.2

Audio Streaming Spotify Windows 33 Running Spotify app on Windows spotify_windows_33m_01 70.9

Video Conferencing Teams Linux 20 Running Teams on Firefox Browser teams_linux_20m_01 134.6

Video Conferencing Teams Linux 20 Running Teams on Firefox Browser teams_linux_20m_02 343.3

Video Conferencing Teams Linux 20 Running Teams on Firefox Browser teams_linux_20m_03 376.6

Video Conferencing Teams Windows 20 Running Teams on Firefox Browser teams_windows_20m_01 634.1

Video Conferencing Teams Windows 20 Running Teams on Firefox Browser teams_windows_20m_02 517.8

Video Conferencing Teams Windows 20 Running Teams on Firefox Browser teams_windows_20m_03 629.9

Web Browsing Web Linux 2 OWIN6G website on Firefox Browser web_linux_2m_owin6g 1.2

Web Browsing Web Linux 2 Wikipedia website on Firefox Browser web_linux_2m_wikipedia 19.7

Web Browsing Web Linux 3 OWIN6G website on Firefox Browser web_linux_3m_owin6g 4.5

Web Browsing Web Linux 3 Wikipedia website on Firefox Browser web_linux_3m_wikipedia 23.5

Web Browsing Web Linux 5 Amazon website on Chrome Browser web_linux_5m_amazon 262.9

Web Browsing Web Linux 5 BBC website on Firefox Browser web_linux_5m_bbc 55.7

Web Browsing Web Linux 5 Google website on Firefox Browser web_linux_5m_google 22.6

Web Browsing Web Linux 5 Linkedin website on Firefox Browser web_linux_5m_linkedin 39.8

Web Browsing Web Windows 3 OWIN6G website on Firefox Browser web_windows_3m_owin6g 32.6

Web Browsing Web Windows 3 Wikipedia website on Firefox Browser web_windows_3m_wikipedia 94.9

Web Browsing Web Windows 5 Amazon website on Chrome Browser web_windows_5m_amazon 104.0

Web Browsing Web Windows 5 BBC website on Firefox Browser web_windows_5m_bbc 23.1

Web Browsing Web Windows 5 Google website on Firefox Browser web_windows_5m_google 31.5

Web Browsing Web Windows 5 Linkedin website on Firefox Browser web_windows_5m_linkedin 104.1

Web Streaming Youtube Linux 20 One Video Streaming, 4K youtube_linux_20m_01 1,145.6

Web Streaming Youtube Linux 20 One Video Streaming, FullHD youtube_linux_20m_02 389.4

Web Streaming Youtube Linux 20 One Video Streaming, FullHD youtube_linux_20m_03 2,007.1

Web Streaming Youtube Linux 20 One Video Streaming, 4K youtube_linux_20m_04 390.4

Web Streaming Youtube Linux 20 One Video Streaming, FullHD youtube_linux_20m_05 410.1

Web Streaming Youtube Linux 20 One Video Streaming, FullHD youtube_linux_20m_06 571.9

Web Streaming Youtube Linux 25 One Video Streaming, FullHD youtube_linux_25m_04 617.0

Web Streaming Youtube Linux 30 One Video Streaming, FullHD youtube_linux_30m_01 422.9

Web Streaming Youtube Linux 30 One Video Streaming, FullHD youtube_linux_30m_02 494.1

Web Streaming Youtube Linux 30 One Video Streaming, 4K youtube_linux_30m_03 871.0

Web Streaming Youtube Windows 20 One Video Streaming, 4K youtube_windows_20m_01 4,243.4

Web Streaming Youtube Windows 25 One Video Streaming, FullHD youtube_windows_25m_01 284.7

Web Streaming Youtube Windows 25 One Video Streaming, FullHD youtube_windows_25m_02 291.9

Total

18.5

24,260.3

Introduction

Fedora Statistics: Fedora is a very popular operating system based on Linux, which is cutting-edge and has very good community support. However, at the end of December 2024, Fedora did not have much market penetration as far as web server deployment was concerned.

According to W3Techs, out of all the websites whose operating system is known, less than 0.1% use Fedora, while 55.2% of these websites are powered by Linux as a whole. This article will show the trends in Fedora statistics.

In a world dominated by technology giants and proprietary ecosystems, a quiet powerhouse has steadily risen in influence: Linux. What started in 1991 as a hobby project by Linus Torvalds now fuels mission-critical infrastructure, powers millions of devices, and defines the backbone of modern computing. Linux is not just an...

Mobile App Statistics (Apple iOS app store)

The ever-changing mobile landscape is a challenging space to navigate. . The percentage of mobile over desktop is only increasing. Android holds about 53.2% of the smartphone market, while iOS is 43%. To get more people to download your app, you need to make sure they can easily find your app. Mobile app analytics is a great way to understand the existing strategy to drive growth and retention of future user.

With million of apps around nowadays, the following data set has become very key to getting top trending apps in iOS app store. This data set contains more than 7000 Apple iOS mobile application details. The data was extracted from the iTunes Search API at the Apple Inc website. R and linux web scraping tools were used for this study.

Interactive full Shiny app can be seen here( https://multiscal.shinyapps.io/appStore/)

Data collection date (from API); July 2017

Dimension of the data set; 7197 rows and 16 columns

Content:

appleStore.csv

1. "id" : App ID

2. "track_name": App Name

3. "size_bytes": Size (in Bytes)

4. "currency": Currency Type

5. "price": Price amount

6. "rating_count_tot": User Rating counts (for all version)

7. "rating_count_ver": User Rating counts (for current version)

8. "user_rating" : Average User Rating value (for all version)

9. "user_rating_ver": Average User Rating value (for current version)

10. "ver" : Latest version code

11. "cont_rating": Content Rating

12. "prime_genre": Primary Genre

13. "sup_devices.num": Number of supporting devices

14. "ipadSc_urls.num": Number of screenshots showed for display

15. "lang.num": Number of supported languages

16. "vpp_lic": Vpp Device Based Licensing Enabled

appleStore_description.csv

1. id : App ID
2. track_name: Application name
3. size_bytes: Memory size (in Bytes)
4. app_desc: Application description

Acknowledgements

The data was extracted from the iTunes Search API at the Apple Inc website. R and linux web scraping tools were used for this study.

Inspiration

1. How does the App details contribute the user ratings?
2. Try to compare app statistics for different groups?

Reference: R package From github, with devtools::install_github("ramamet/applestoreR")

Licence

Copyright (c) 2018 Ramanathan Perumal

Traffic analytics, rankings, and competitive metrics for linux-kvm.org as of September 2025

The Linux-based Network Operating System (NOS) market has increasingly established itself as a crucial element of modern IT infrastructure, providing robust solutions that cater to the ever-evolving needs of businesses across various sectors. Renowned for its open-source nature, which promotes flexibility and custom

AIT Log Data Sets

This repository contains synthetic log data suitable for evaluation of intrusion detection systems. The logs were collected from four independent testbeds that were built at the Austrian Institute of Technology (AIT) following the approach by Landauer et al. (2020) [1]. Please refer to the paper for more detailed information on automatic testbed generation and cite it if the data is used for academic publications. In brief, each testbed simulates user accesses to a webserver that runs Horde Webmail and OkayCMS. The duration of the simulation is six days. On the fifth day (2020-03-04) two attacks are launched against each web server.

The archive AIT-LDS-v1_0.zip contains the directories "data" and "labels".

The data directory is structured as follows. Each directory mail.

Setup details of the web servers:

• OS: Debian Stretch 9.11.6
• Services:
  • Apache2
  • PHP7
  • Exim 4.89
  • Horde 5.2.22
  • OkayCMS 2.3.4
  • Suricata
  • ClamAV
  • MariaDB

Setup details of user machines:

• OS: Ubuntu Bionic
• Services:
  • Chromium
  • Firefox

User host machines are assigned to web servers in the following way:

• mail.cup.com is accessed by users from host machines user-{0, 1, 2, 6}
• mail.spiral.com is accessed by users from host machines user-{3, 5, 8}
• mail.insect.com is accessed by users from host machines user-{4, 9}
• mail.onion.com is accessed by users from host machines user-{7, 10}

The following attacks are launched against the web servers (different starting times for each web server, please check the labels for exact attack times):

• Attack 1: multi-step attack with sequential execution of the following attacks:
  • nmap scan
  • nikto scan
  • smtp-user-enum tool for account enumeration
  • hydra brute force login
  • webshell upload through Horde exploit (CVE-2019-9858)
  • privilege escalation through Exim exploit (CVE-2019-10149)
• Attack 2: webshell injection through malicious cookie (CVE-2019-16885)

Attacks are launched from the following user host machines. In each of the corresponding directories user-

• user-6 attacks mail.cup.com
• user-5 attacks mail.spiral.com
• user-4 attacks mail.insect.com
• user-7 attacks mail.onion.com

The log data collected from the web servers includes

• Apache access and error logs
• syscall logs collected with the Linux audit daemon
• suricata logs
• exim logs
• auth logs
• daemon logs
• mail logs
• syslogs
• user logs

Note that due to their large size, the audit/audit.log files of each server were compressed in a .zip-archive. In case that these logs are needed for analysis, they must first be unzipped.

Labels are organized in the same directory structure as logs. Each file contains two labels for each log line separated by a comma, the first one based on the occurrence time, the second one based on similarity and ordering. Note that this does not guarantee correct labeling for all lines and that no manual corrections were conducted.

Version history and related data sets:

• AIT-LDS-v1.0: Four datasets, logs from single host, fine-granular audit logs, mail/CMS.
  • AIT-LDS-v1.1: Removed carriage return of line endings in audit.log files.
• AIT-LDS-v2.0: Eight datasets, logs from all hosts, system logs and network traffic, mail/CMS/cloud/web.

Acknowledgements: Partially funded by the FFG projects INDICAETING (868306) and DECEPT (873980), and the EU project GUARD (833456).

If you use the dataset, please cite the following publication:

[1] M. Landauer, F. Skopik, M. Wurzenberger, W. Hotwagner and A. Rauber, "Have it Your Way: Generating Customized Log Datasets With a Model-Driven Simulation Testbed," in IEEE Transactions on Reliability, vol. 70, no. 1, pp. 402-415, March 2021, doi: 10.1109/TR.2020.3031317. [PDF]

The global Linux Operating System market is poised for robust expansion, projected to reach a market size of approximately $35,000 million by 2025, with a Compound Annual Growth Rate (CAGR) of around 15% anticipated to drive its value to over $65,000 million by 2033. This significant growth is underpinned by several key drivers, most notably the escalating adoption of cloud computing services, where Linux commands a dominant market share due to its stability, security, and cost-effectiveness. The increasing demand for open-source solutions across enterprises, coupled with the proliferation of Internet of Things (IoT) devices and the burgeoning Big Data analytics sector, further fuels market expansion. The versatility of Linux distributions, catering to diverse needs from individual users to large-scale commercial enterprises, ensures its continued relevance and adoption. Specific distributions like Ubuntu, Fedora, and CentOS are experiencing heightened demand due to their active communities, regular updates, and comprehensive support. Despite its impressive growth trajectory, the Linux Operating System market faces certain restraints. The perceived complexity of certain distributions for less technically inclined users can pose a barrier to entry, although this is being mitigated by more user-friendly interfaces and simplified installation processes. Furthermore, the availability of free, open-source alternatives can sometimes impact the revenue streams for commercial Linux vendors, necessitating a focus on value-added services and enterprise-grade support. Nonetheless, the inherent flexibility, customization capabilities, and strong security features of Linux continue to make it a preferred choice for a wide array of applications, from web servers and embedded systems to supercomputers and desktop environments. Key players like Amazon Web Services, Canonical Ltd., and Red Hat Inc. are instrumental in shaping the market through continuous innovation and strategic partnerships. This report provides an in-depth analysis of the global Linux Operating System market, spanning the historical period of 2019-2024, the base and estimated year of 2025, and a comprehensive forecast period extending to 2033. The market is projected to witness significant growth, driven by a confluence of technological advancements, evolving industry demands, and strategic initiatives from key players. The report utilizes values in the millions to quantify market size and projections.

The Linux-based Network Operating System (NOS) market is booming, driven by SDN, NFV, and the need for cost-effective, scalable networking solutions. Learn about market trends, key players (Cumulus, IP Infusion, Big Switch), and future projections in this in-depth analysis. Explore the growth drivers, restraints, and regional market share.

Replication package of incivility in code review discussions of rejected patches in the Linux community.

Cuckoo Sandbox is the leading open sourceautomated malware analysis system. You can throw any suspicious file atit and in a matter of seconds Cuckoo will provide you back some detailedresults outlining what such file did when executed inside an isolatedenvironment.

Cuckoo Sandbox is free software that automated the task of analyzing any malicious file under Windows, OS X, Linux, and Android.

What can it do?

Cuckoo Sandbox is an advanced, extremely modular, and 100% open source automated malware analysis system with infinite application opportunities. By default it is able to:


Analyze many different malicious files (executables, office documents, pdf files, emails, etc) as well as malicious websites under Windows, Linux, Mac OS X, and Android virtualized environments.
Trace API calls and general behavior of the file and distill this into high level information and signatures comprehensible by anyone.
Dump and analyze network traffic, even when encrypted with SSL/TLS. With native network routing support to drop all traffic or route it through InetSIM, a network interface, or a VPN.
Perform advanced memory analysis of the infected virtualized system through Volatility as well as on a process memory granularity using YARA.


Due to Cuckoo s open source nature and extensive modular design one may customize any aspect of the analysis environment, analysis results processing, and reporting stage. Cuckoo provides you all the requirements to easily integrate the sandbox into your existing framework and backend in the way you want, with the format you want, and all of that without licensing requirements.

.

ABSTRACT In this paper, a new software for Statistical Process Control (SPC) is proposed. The system, the so-called CEP Online, was developed based on statistical computing resources of well-known free softwares, such as HTML, PHP, R and MySQL under an online server with operating system Linux Ubuntu. The main uni and multivariate SPC tools are available for monitoring and evaluation of manufacturing and non-manufacturing production processes over time. Some advantages of the new software are: (i) low operational cost, since it is cloud-based, only needing a computer connected to the Internet; (ii) easy to use with great interaction with the user; (iii) it does not require investment in any specific hardware or software; (iv) real time reports generation on process condition monitoring and process capability. Thus, the CEP Online offers for SPC practitioners fast, efficient and accurate SPC procedures. Therefore, CEP Online becomes an important resource for those who have no access to non-free softwares, such as SAS, SPSS, Minitab and STATISTICA. To the best of our knowledge, the CEP Online is unique with respect to its characteristics.

Web application as free reference manager and academic social network to organize your research, collaborate with others online, and discover the latest research. Automatically generate bibliographies, Collaborate easily with other researchers online, Easily import papers from other research software, Find relevant papers based on what you're reading, Access your papers from anywhere online, Read papers on the go with the iPhone app. The software, Mendeley Desktop, offers: * Automatic extraction of document details * Efficient management of your papers * Sharing and synchronization of your library (or parts of it) * Additional features: A plug-in for citing your articles in Microsoft Word, OCR (image-to-text conversion, so you can full-text search all your scanned PDFs), etc The website, Mendeley Web, complements Mendeley Desktop by offering these features: * An online back up of your library * Statistics of all things interesting * A research network that allows you to keep track of your colleagues' publications, conference participations, awards etc * A recommendation engine for papers that might interest you.

This dataset represents the baseline benign and attack traffic for IoT (Internet of Things) consumer devices that may be representative of a smart-home network. The purpose of this dataset, in comparison to other IoT datasets, is to simplify the input data in terms of size and its ability to be interpreted under different scenarios.

A wireshark column template is provided to add extra columns of interest beyond the default view (view bottom right profile area in wireshark, right-click and "import" zip file below) - wiresharkprofile_template.zip

The dataset is provided in PCAP format (readable by Wireshark or other platforms) and is categorized as follows:

IoT SETUP (real network traffic patterns to represent setup exchanges for common IoT devices) - iot_setup_plug1_an.pcapng - iot_setup_bulb1_an.pcapng

IoT BENIGN IDLE (Network traffic associated with IoT devices on the network that are on, but only in a standby state) - all_idle_1Hrs_an.pcapng - all_idle_5Hrs_an.pcapng - all_idle_10Hrs_part1_an.pcapng - all_idle_10Hrs_part2_an.pcapng

IoT BENIGN ACTIVE (Network traffic associated with IoT devices on the network that are active and in use) - all_active_1Hrs_an.pcapng - all_active_5Hrs_an.pcapng - all_active_10Hrs_part1_an.pcapng - all_active_10Hrs_part2_an.pcapng

IoT ATTACK TRAFFIC (Kali Linux HPING3 from 192.168.100.240 attack machine using ICMP Floods and SYN Floods as Attacks for four (4) IoT device targets in use)
- ICMP flood of IoT Camera 1 (192.168.100.11) - two separate segments of flood attack within five minute session - ICMP flood of IoT EchoShow (192.168.100.21) - two separate segments of flood attack within five minute session - ICMP flood of IoT plug1 (192.168.100.31) - two separate segments of flood attack within five minute session - ICMP flood of IoT lightbulb1 (192.168.100.41) - two separate segments of flood attack within five minute session - SYN flood of IoT Camera 1 (192.168.100.11) - SYN flood of IoT EchoShow (192.168.100.21)
- SYN flood of IoT plug1 (192.168.100.31)
- SYN flood of IoT lightbulb1 (192.168.100.41)

This academic work is part of ongoing dissertation research at Colorado State University. All credit should reference the authors David Weissman (PhD Candidate) and Dr. Anura Jayasumana (Professor) - copyright (c) 2023-2024.

Datasets are subject to revisions or enhancements over time.

The High Availability Server market is experiencing robust growth, projected to reach a significant market size with a Compound Annual Growth Rate (CAGR) of 16% from 2025 to 2033. This expansion is fueled by several key drivers. Increasing reliance on mission-critical applications across diverse sectors, such as IT & Telecommunications, BFSI (Banking, Financial Services, and Insurance), and Healthcare, demands uninterrupted uptime. The growing adoption of cloud-based solutions and the rise of big data analytics further contribute to market growth. Businesses are increasingly investing in high-availability servers to ensure data integrity, minimize downtime, and enhance operational efficiency. While on-premise deployments remain prevalent, the shift towards cloud-based solutions is gaining momentum, driven by scalability, cost-effectiveness, and enhanced disaster recovery capabilities. The market is segmented by operating system (Windows, Linux, and others), deployment (cloud-based and on-premise), and end-user industry, reflecting the diverse applications of high-availability servers. Competitive landscape analysis indicates major players such as Dell, Oracle, Cisco, IBM, and Amazon Web Services are actively shaping market dynamics through innovation and strategic partnerships. The market's growth trajectory is not without challenges. Potential restraints include the high initial investment costs associated with implementing high-availability solutions, the complexity of managing such systems, and the need for specialized expertise. However, ongoing technological advancements, increasing awareness of data security risks, and the rising adoption of virtualization and containerization technologies are expected to mitigate these challenges and further propel market expansion. The Asia Pacific region is anticipated to show significant growth, driven by rapid technological advancements and increasing digitalization across various sectors. North America and Europe are expected to maintain strong market positions owing to the mature IT infrastructure and high adoption rates of advanced technologies within these regions. The forecast period, 2025-2033, presents significant opportunities for market players to capitalize on the growing demand for robust and reliable server solutions. Key drivers for this market are: , High Adoption Rate of High Availability Server Across BFSI Sector; Growing Demand for Modular & Micro Data Center with the Increasing Application of IoT Devices. Potential restraints include: , High Adoption Rate of High Availability Server Across BFSI Sector; Growing Demand for Modular & Micro Data Center with the Increasing Application of IoT Devices. Notable trends are: BFSI Sector is Expected to Have a Significant Growth Rate.

Explore the booming Linux-based Network Operating System market, projected to hit USD 20,408 million by 2033 with an 18% CAGR. Discover key drivers like SDN, NFV, and open networking, market trends, and leading companies shaping the future of network infrastructure for SMEs and large enterprises.

Microsoft’s Windows is the most widely used computer operating system in the world, accounting for ***** percent share of the desktop, tablet, and console OS market in October 2025. Apple’s OS X ranks as the next most widely used operating system, while its iOS mobile operating system, the standard installation on all iPad devices, ranks fifth. Linux OS versions serve as the primary option for users who prefer open-source software and intend to avoid the influence of major OS developers. Operating Systems Operating systems serve as the underlying platforms which connect computer hardware and software. They provide users with the graphical interface through which they issue commands and perform tasks on electronic devices. Billions of people make use of these devices and their operating systems on a regular basis, meaning that the companies that develop these widely used technologies have a great deal of influence on the daily lives of internet users around the world. Although Microsoft Windows is the clear leader in terms of desktop operating systems, the company’s mobile device operating system failed to make a successful transition into the smartphone market, where Android and iOS are essentially the only two options.

The dataset contains source code and data relevant for the paper "Combining Model Learning and Model Checking to Analyze TCP Implementations".Paper url: https://link.springer.com/chapter/10.1007/978-3-319-41540-6_25PDF url: http://www.sws.cs.ru.nl/publications/papers/fvaan/FJV16/main.pdfRamon Janssen's Master's thesis url: http://www.ru.nl/publish/pages/769526/z_thesis_ramon_janssen.pdfIn this work, we use automata learning with abstraction to infer models of 3 TCP client and server implementations (Windows, Linux, FreeBSD). We then verify properties on these models using the NuSMV model checker. The dataset comprises the software components of our experimental setup apart from the actual implementations, some useful scripts and the learned models and associated experimental logs.In more concrete terms, the dataset contains:- the learner (setup) implementation - Java code for the setup built around LearnLib to perform learning with abstraction over sockets. Abstraction is provided by a mapper, described in a mapper language described in the Master's thesis.- the mapper library - Java code for loading mappers written in the mapper language and executing them in both directions (from abstract to concrete and from concrete to abstract)- the network adapter - crafts packets from strings or messages ("SYN(0,10)") and sends them to a TCP entity, receives packets and turns them back to strings- mappers - the mappers defined for the three operating systems learned- experimental data - Mealy Machine models for 6 TCP client/server implementations (BitVise, OpenSSH, DropBear) accompanied by other experimental data (statistics, input configuration ...)- model checking setup - project comprising bash scripts and Java libraries used to perform model checking on the learned models, model checking is mostly automatedWhat can be re-used:- the learner setup (connect to the learner through the mapper to a different system over sockets), note that there are some limitations of the mapper language- the mapper adapter, again note the limitations which fit the TCP case study but may not fit other case studies- the network adapter, can be adapted to learn other lower layer protocols or can be tweaked to include more information from packets in strings- model checking setup, this cannot really be reused outside of the TCP case study but it may serve as inspiration for one who wants to perform model checking of concretized abstract models (the mappers used to learn the models were also used to concretize them during model checking) or of interactions between two parties- beautifying Java library (if one wants to make complex .dot files more readable)Also, the dataset is suitable for one who wants to try out/expand learning for TCP. Learning and model checking can be done with minimal adaptation (perhaps slight changes in the mapper and adapting the model checking scripts).Paper Abstract: We combine model learning and model checking in a challenging case study involving Linux, Windows and FreeBSD implementations of TCP. We use model learning to infer models of different software components and then apply model checking to fully explore what may happen when these components (e.g. a Linux client and a Windows server) interact. Our analysis reveals several instances in which TCP implementations do not conform to their RFC specifications.