Web traffic statistics for the several City-Parish websites, brla.gov, city.brla.gov, Red Stick Ready, GIS, Open Data etc. Information provided by Google Analytics.
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License information was derived automatically
Code:
Packet_Features_Generator.py & Features.py
To run this code:
pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j
-h, --help show this help message and exit -i TXTFILE input text file -x X Add first X number of total packets as features. -y Y Add first Y number of negative packets as features. -z Z Add first Z number of positive packets as features. -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,... -s S Generate samples using size s. -j
Purpose:
Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.
Uses Features.py to calcualte the features.
startMachineLearning.sh & machineLearning.py
To run this code:
bash startMachineLearning.sh
This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags
Options (to be edited within this file):
--evaluate-only to test 5 fold cross validation accuracy
--test-scaling-normalization to test 6 different combinations of scalers and normalizers
Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use
--grid-search to test the best grid search hyperparameters - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:' - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'
Purpose:
Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.
Data
Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.
Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:
First number is a classification number to denote what website, query, or vr action is taking place.
The remaining numbers in each line denote:
The size of a packet,
and the direction it is traveling.
negative numbers denote incoming packets
positive numbers denote outgoing packets
Figure 4 Data
This data uses specific lines from the Virtual Reality.txt file.
The action 'LongText Search' refers to a user searching for "Saint Basils Cathedral" with text in the Wander app.
The action 'ShortText Search' refers to a user searching for "Mexico" with text in the Wander app.
The .xlsx and .csv file are identical
Each file includes (from right to left):
The origional packet data,
each line of data organized from smallest to largest packet size in order to calculate the mean and standard deviation of each packet capture,
and the final Cumulative Distrubution Function (CDF) caluclation that generated the Figure 4 Graph.
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License information was derived automatically
Explore our detailed website traffic dataset featuring key metrics like page views, session duration, bounce rate, traffic source, and conversion rates.
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Security and Privacy: Our Commitment Understanding the sensitivity of web traffic data and contact information, our solution is built with security and privacy at its core. We adhere to strict data protection regulat...
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The global website visitor tracking software market is experiencing robust growth, driven by the increasing need for businesses to understand online customer behavior and optimize their digital strategies. The market, estimated at $5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This expansion is fueled by several key factors, including the rising adoption of digital marketing strategies, the growing importance of data-driven decision-making, and the increasing sophistication of website visitor tracking tools. Cloud-based solutions dominate the market due to their scalability, accessibility, and cost-effectiveness, particularly appealing to Small and Medium-sized Enterprises (SMEs). However, large enterprises continue to invest significantly in on-premise solutions for enhanced data security and control. The market is highly competitive, with numerous established players and emerging startups offering a range of features and functionalities. Technological advancements, such as AI-powered analytics and enhanced integration with other marketing tools, are shaping the future of the market. The market's geographical distribution reflects the global digital landscape. North America, with its mature digital economy and high adoption rates, holds a significant market share. However, regions like Asia-Pacific are showing rapid growth, driven by increasing internet penetration and digitalization across various industries. Despite the overall positive outlook, challenges such as data privacy regulations and the increasing complexity of website tracking technology are influencing market dynamics. The ongoing competition among vendors necessitates continuous innovation and the development of more user-friendly and insightful tools. The future growth of the website visitor tracking software market is promising, fueled by the continuing importance of data-driven decision-making within marketing and business strategies. A key factor will be the ongoing adaptation to evolving privacy regulations and user expectations.
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Network Traffic Analysis Market is Segmented by Deployment (On-Premise, Cloud-Based, and Hybrid), Component (Solutions and Services), Organization Size (Large Enterprises and Small and Medium Enterprises), End-User Industry (BFSI, IT and Telecom, and More), and Geography. The Market Sizes and Forecasts are Provided in Value (in USD Million) for all the Above Segments.
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
This dataset was created by YUCHU Shen
Released under Apache 2.0
Click Web Traffic Combined with Transaction Data: A New Dimension of Shopper Insights
Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. Click enhances the unparalleled accuracy of CE Transact by allowing investors to delve deeper and browse further into global online web traffic for CE Transact companies and more. Leverage the unique fusion of web traffic and transaction datasets to understand the addressable market and understand spending behavior on consumer and B2B websites. See the impact of changes in marketing spend, search engine algorithms, and social media awareness on visits to a merchant’s website, and discover the extent to which product mix and pricing drive or hinder visits and dwell time. Plus, Click uncovers a more global view of traffic trends in geographies not covered by Transact. Doubleclick into better forecasting, with Click.
Consumer Edge’s Click is available in machine-readable file delivery and enables: • Comprehensive Global Coverage: Insights across 620+ brands and 59 countries, including key markets in the US, Europe, Asia, and Latin America. • Integrated Data Ecosystem: Click seamlessly maps web traffic data to CE entities and stock tickers, enabling a unified view across various business intelligence tools. • Near Real-Time Insights: Daily data delivery with a 5-day lag ensures timely, actionable insights for agile decision-making. • Enhanced Forecasting Capabilities: Combining web traffic indicators with transaction data helps identify patterns and predict revenue performance.
Use Case: Analyze Year Over Year Growth Rate by Region
Problem A public investor wants to understand how a company’s year-over-year growth differs by region.
Solution The firm leveraged Consumer Edge Click data to: • Gain visibility into key metrics like views, bounce rate, visits, and addressable spend • Analyze year-over-year growth rates for a time period • Breakout data by geographic region to see growth trends
Metrics Include: • Spend • Items • Volume • Transactions • Price Per Volume
Inquire about a Click subscription to perform more complex, near real-time analyses on public tickers and private brands as well as for industries beyond CPG like: • Monitor web traffic as a leading indicator of stock performance and consumer demand • Analyze customer interest and sentiment at the brand and sub-brand levels
Consumer Edge offers a variety of datasets covering the US, Europe (UK, Austria, France, Germany, Italy, Spain), and across the globe, with subscription options serving a wide range of business needs.
Consumer Edge is the Leader in Data-Driven Insights Focused on the Global Consumer
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Daily utilization metrics for data.lacity.org and geohub.lacity.org. Updated monthly
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Network Traffic Analyzer Market size was valued at USD 3.54 Billion in 2024 and is projected to reach USD 5.86 Billion by 2032, growing at a CAGR of 10.6% during the forecast period 2026-2032.
Global Network Traffic Analyzer Market Drivers
The market drivers for the Network Traffic Analyzer Market can be influenced by various factors. These may include:
Growing Risks to Cybersecurity: The increasing sophistication and frequency of cyber threats and attacks are driving the need for network traffic analyzers to improve security protocols. These instruments support the identification and mitigation of dubious network activity. Increasing Network Infrastructure Complexity: Organisations need sophisticated tools to monitor and analyze network traffic because network infrastructures, especially hybrid and multi-cloud systems, are becoming more and more complicated. Network traffic analyzers shed light on these complex infrastructures' security and performance. Growing Cloud Computing Adoption: There is a growing need for network traffic analyzers that can monitor and optimize performance across cloud environments due to the widespread adoption of cloud services and the migration of applications and data to the cloud.
Web traffic statistics for the top 2000 most visited pages on nyc.gov by month.
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License information was derived automatically
Network traffic datasets created by Single Flow Time Series Analysis
Datasets were created for the paper: Network Traffic Classification based on Single Flow Time Series Analysis -- Josef Koumar, Karel Hynek, Tomáš Čejka -- which was published at The 19th International Conference on Network and Service Management (CNSM) 2023. Please cite usage of our datasets as:
J. Koumar, K. Hynek and T. Čejka, "Network Traffic Classification Based on Single Flow Time Series Analysis," 2023 19th International Conference on Network and Service Management (CNSM), Niagara Falls, ON, Canada, 2023, pp. 1-7, doi: 10.23919/CNSM59352.2023.10327876.
This Zenodo repository contains 23 datasets created from 15 well-known published datasets which are cited in the table below. Each dataset contains 69 features created by Time Series Analysis of Single Flow Time Series. The detailed description of features from datasets is in the file: feature_description.pdf
In the following table is a description of each dataset file:
File name | Detection problem | Citation of original raw dataset |
botnet_binary.csv | Binary detection of botnet | S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. |
botnet_multiclass.csv | Multi-class classification of botnet | S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. |
cryptomining_design.csv | Binary detection of cryptomining; the design part | Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 |
cryptomining_evaluation.csv | Binary detection of cryptomining; the evaluation part | Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 |
dns_malware.csv | Binary detection of malware DNS | Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021. |
doh_cic.csv | Binary detection of DoH |
Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020 |
doh_real_world.csv | Binary detection of DoH | Kamil Jeřábek et al. Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42:108310, 2022 |
dos.csv | Binary detection of DoS | Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019. |
edge_iiot_binary.csv | Binary detection of IoT malware | Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022. |
edge_iiot_multiclass.csv | Multi-class classification of IoT malware | Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022. |
https_brute_force.csv | Binary detection of HTTPS Brute Force | Jan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020 |
ids_cic_binary.csv | Binary detection of intrusion in IDS | Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. |
ids_cic_multiclass.csv | Multi-class classification of intrusion in IDS | Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. |
ids_unsw_nb_15_binary.csv | Binary detection of intrusion in IDS | Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015. |
ids_unsw_nb_15_multiclass.csv | Multi-class classification of intrusion in IDS | Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015. |
iot_23.csv | Binary detection of IoT malware | Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23 |
ton_iot_binary.csv | Binary detection of IoT malware | Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021 |
ton_iot_multiclass.csv | Multi-class classification of IoT malware | Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021 |
tor_binary.csv | Binary detection of TOR | Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. |
tor_multiclass.csv | Multi-class classification of TOR | Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. |
vpn_iscx_binary.csv | Binary detection of VPN | Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. |
vpn_iscx_multiclass.csv | Multi-class classification of VPN | Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. |
vpn_vnat_binary.csv | Binary detection of VPN | Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022 |
vpn_vnat_multiclass.csv | Multi-class classification of VPN | Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022 |
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The global Network Traffic Analysis (NTA) Software market size is poised to witness a robust growth trajectory, with a projected market valuation rising from approximately USD 3.5 billion in 2023 to an impressive USD 12.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.2% during the forecast period. The surge in this market is predominantly fueled by the increasing need for sophisticated cybersecurity measures due to the escalating frequency and complexity of cyber threats. Organizations are progressively recognizing the critical importance of NTA software in detecting, monitoring, and responding to potential network anomalies and threats, driving the market's expansion.
A major growth factor contributing to the burgeoning NTA Software market is the exponential growth in data traffic, attributed to the widespread adoption of cloud computing, IoT devices, and the ongoing digital transformation across industries. As enterprises expand their digital footprint, the volume of data traversing networks has seen an unprecedented rise, necessitating advanced network traffic analysis solutions to ensure efficient management and security of data. Moreover, the increasing sophistication of cyber threats, including advanced persistent threats (APTs) and ransomware, has made continuous network monitoring and analysis indispensable for organizations striving to protect sensitive information and maintain business continuity.
Another significant driver for the NTA Software market is the growing regulatory pressures and compliance requirements across various sectors, including BFSI, healthcare, and government. These regulations mandate organizations to implement robust cybersecurity frameworks and ensure data protection, thereby propelling the demand for comprehensive NTA solutions. Companies are increasingly investing in NTA software to comply with standards such as GDPR, HIPAA, and PCI-DSS, which emphasize the importance of network security and data privacy. As regulatory landscapes continue to evolve, the necessity for effective network traffic analysis tools becomes even more pronounced, further accelerating market growth.
The increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies in network traffic analysis is also a key factor driving the market's growth. These technologies enhance the capabilities of NTA software by enabling automated threat detection, predictive analytics, and anomaly detection, thereby improving the overall efficiency and accuracy of network monitoring. The integration of AI and ML has allowed NTA solutions to evolve from traditional reactive systems to proactive security platforms, capable of identifying and mitigating threats in real-time. This technological advancement is particularly attractive to large enterprises and government agencies that require robust security measures to safeguard critical infrastructure and data.
From a regional perspective, North America is anticipated to lead the NTA Software market during the forecast period, owing to the region's well-established IT infrastructure and the presence of major industry players. The Asia Pacific region, however, is expected to witness the fastest growth, driven by rapid technological advancements, increasing internet penetration, and a rising focus on cybersecurity across emerging economies such as India and China. Europe also presents significant growth opportunities, supported by stringent data protection regulations and growing investments in cybersecurity solutions. These regional dynamics highlight the diverse growth trajectories and opportunities present across the global NTA Software market.
The Network Traffic Analysis Software market is segmented into two primary components: software and services. The software segment accounts for the largest share of the market and is expected to continue its dominance throughout the forecast period. This is primarily due to the increasing demand for advanced network traffic analysis solutions that can efficiently monitor, detect, and respond to potential security threats. With the escalating frequency of cyberattacks, organizations are increasingly leveraging sophisticated software to enhance their network security posture and mitigate risks. The software component includes various solutions such as real-time traffic monitoring, anomaly detection, and threat intelligence, which are integral to comprehensive network security strategies.
The services segment, on the other hand, is projected to witness signi
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In 2023, the global network traffic analyzer market size was valued at approximately USD 2.5 billion and is anticipated to grow to USD 6.7 billion by 2032, with a CAGR of 11.5% during the forecast period. The significant growth factor driving this market is the increasing demand for sophisticated network management tools to manage the exponential growth in data traffic. As enterprises continue to digitize their operations, the necessity for advanced network traffic analysis solutions escalates, ensuring network reliability, security, and performance.
The growth of the network traffic analyzer market is propelled by several key factors. Firstly, the rapid expansion of internet usage and the proliferation of connected devices generate vast amounts of data traffic, necessitating robust tools to monitor and analyze this traffic effectively. With the surge in cyber threats and network security breaches, organizations are increasingly adopting network traffic analyzers to detect, respond to, and mitigate potential security risks. The ability of these tools to provide real-time visibility into network operations and detect anomalies is critical in safeguarding enterprise networks.
Secondly, the advent of advanced technologies such as Internet of Things (IoT), cloud computing, and 5G networks has significantly boosted the demand for network traffic analyzers. These technologies generate enormous amounts of network traffic, which need to be monitored and managed efficiently to ensure optimal performance and security. Network traffic analyzers play a vital role in managing this complexity, offering insights that help in optimizing network resources and improving overall operational efficiency.
Another major growth factor is the increasing adoption of network traffic analyzer solutions by small and medium enterprises (SMEs). Traditionally, these solutions were predominantly used by large enterprises due to their high cost and complexity. However, recent advancements have made these tools more accessible and affordable for SMEs, enabling them to harness the benefits of network traffic analysis. This democratization of technology is expected to further drive market growth in the coming years.
From a regional perspective, North America currently holds the largest share of the network traffic analyzer market, driven by strong technological infrastructure and the presence of major market players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid digital transformation in countries like China and India, coupled with increasing investments in network infrastructure and cybersecurity, is propelling the market forward in this region. Europe and Latin America are also expected to see steady growth, driven by regulatory mandates and the increasing need for network security solutions.
The network traffic analyzer market is segmented by component into software, hardware, and services. The software segment holds the largest market share, attributed to the increasing deployment of advanced software solutions for network monitoring and traffic analysis. These software solutions offer comprehensive insights into network performance, enabling organizations to proactively manage network issues and optimize performance. With the integration of AI and machine learning, these solutions are becoming even more sophisticated, capable of predictive analysis and automated responses.
Network Packet Broker (NPB) solutions are becoming increasingly vital in the context of network traffic analysis. These devices play a crucial role in optimizing the flow of data across complex network infrastructures by aggregating, filtering, and directing traffic to specific monitoring tools. As networks grow in complexity with the proliferation of IoT devices and cloud services, NPBs help ensure that only relevant data is sent to analysis tools, thereby enhancing the efficiency and accuracy of network monitoring. By offloading the processing burden from network analyzers, NPBs enable organizations to maintain high performance and reliability in their network operations, making them an indispensable component of modern network management strategies.
The hardware segment, although smaller than the software segment, plays a crucial role in the network traffic analyzer market. Hardware components such as network probes, packet brokers, an
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The global network traffic analysis solutions market size was estimated at USD 3.5 billion in 2023 and is projected to reach USD 9.8 billion by 2032, reflecting a compound annual growth rate (CAGR) of 12.1%. This substantial growth is largely driven by the increasing demand for robust cybersecurity measures across various sectors. With an ever-growing volume of network traffic due to the proliferation of connected devices and the adoption of digital transformation initiatives, organizations are compelled to deploy sophisticated traffic analysis tools to effectively monitor, manage, and secure their networks. The expansion of cloud services, coupled with the rise in cyber threats, further accentuates the need for advanced traffic analysis capabilities.
The surge in cyber threats, including sophisticated hacking techniques and ransomware attacks, has become a pivotal growth factor for the network traffic analysis solutions market. As organizations strive to protect sensitive data and ensure the integrity of their networks, there is a heightened demand for solutions that can provide real-time visibility and control over network traffic. This growing emphasis on cybersecurity is not limited to large enterprises but is increasingly becoming a priority for small and medium enterprises (SMEs) as well. Consequently, the increasing cyber threat landscape is stimulating the adoption of network traffic analysis solutions across different organizational sizes, driving market growth.
Moreover, the rise of Internet of Things (IoT) devices is significantly contributing to the increased need for network traffic analysis. IoT devices generate vast amounts of data that need to be managed effectively to prevent network congestion and potential security breaches. By leveraging traffic analysis solutions, organizations can optimize IoT device performance and ensure seamless data flow while maintaining robust security protocols. As the IoT ecosystem continues to expand, it is expected to further fuel the demand for network traffic analysis solutions, facilitating better management and security of network resources.
In addition to cybersecurity concerns and IoT proliferation, regulatory compliance is another critical growth driver for the network traffic analysis solutions market. Organizations across various industries, such as BFSI, healthcare, and government sectors, are under increasing pressure to comply with stringent data protection regulations. Network traffic analysis solutions help these organizations monitor compliance effectively by providing detailed insights into network activity and data flows. As regulations continue to evolve and become more complex, the role of network traffic analysis solutions in ensuring compliance and mitigating risks is expected to become increasingly important, further bolstering market growth.
Network Telemetry Solutions are becoming increasingly essential in the realm of network traffic analysis. These solutions provide real-time data collection and analysis, enabling organizations to gain deeper insights into their network operations. By leveraging network telemetry, businesses can proactively identify and address potential issues before they escalate into significant problems. This capability is particularly valuable in today's fast-paced digital environment, where network performance and security are critical to maintaining operational efficiency. As the demand for more granular visibility into network activities grows, network telemetry solutions are poised to play a pivotal role in enhancing the capabilities of traffic analysis tools, offering a more comprehensive approach to network management and security.
From a regional perspective, North America is anticipated to maintain a dominant position in the network traffic analysis solutions market. This can be attributed to the presence of major technology companies, a high adoption rate of advanced technologies, and stringent cybersecurity regulations. The region's established digital infrastructure and focus on innovation also contribute to market growth. Meanwhile, the Asia Pacific region is projected to witness the highest growth rate due to rapid digitalization, increasing internet penetration, and growing investments in IT infrastructure. As businesses in this region continue to adopt digital technologies and face rising cyber threats, the demand for network traffic analysis solutions is expected to surge significantly.
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Data Quality and Credibility: We take data quality seriously. Our data sourcing practices are designed to ensure responsible and reliable data collection. We implement rigorous data cleaning, validation, and verification processes, guaranteeing the accuracy and reliability of our clickstream data. You can confidently rely on our data to drive your decision-making processes.
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The network traffic analytics market size was valued at USD 3.44 billion in 2024 and is likely to cross USD 13.2 billion by 2037, registering more than 10.9% CAGR during the forecast period i.e., between 2025-2037. North America industry is expected to account for largest revenue share of 35% by 2037, due to presence of two significant economies, including the USA and Canada.
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This traffic dataset contains a balance size of encrypted malicious and legitimate traffic for encrypted malicious traffic detection and analysis. The dataset is a secondary csv feature data that is composed of six public traffic datasets.
Our dataset is curated based on two criteria: The first criterion is to combine widely considered public datasets which contain enough encrypted malicious or encrypted legitimate traffic in existing works, such as Malware Capture Facility Project datasets. The second criterion is to ensure the final dataset balance of encrypted malicious and legitimate network traffic.
Based on the criteria, 6 public datasets are selected. After data pre-processing, details of each selected public dataset and the size of different encrypted traffic are shown in the “Dataset Statistic Analysis Document”. The document summarized the malicious and legitimate traffic size we selected from each selected public dataset, the traffic size of each malicious traffic type, and the total traffic size of the composed dataset. From the table, we are able to observe that encrypted malicious and legitimate traffic equally contributes to approximately 50% of the final composed dataset.
The datasets now made available were prepared to aim at encrypted malicious traffic detection. Since the dataset is used for machine learning or deep learning model training, a sample of train and test sets are also provided. The train and test datasets are separated based on 1:4. Such datasets can be used for machine learning or deep learning model training and testing based on selected features or after processing further data pre-processing.
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Global Network Traffic Analytics market size is expected to reach $6.22 billion by 2029 at 13.4%, segmented as by solution, network traffic analytics software, hardware appliances, cloud-based solutions
Web traffic statistics for the several City-Parish websites, brla.gov, city.brla.gov, Red Stick Ready, GIS, Open Data etc. Information provided by Google Analytics.