Dataset Description This dataset contains comprehensive network traffic data captured during simulated attacks on Named Data Networking (NDN) environments across two distinct network topologies: Tree and DFN (Deutsches ForschungsNetz). All data was generated through controlled experiments using miniNDN simulation on Ubuntu.

Dataset Overview Named Data Networking (NDN) represents a future internet architecture that focuses on content retrieval rather than host-to-host communication. As this architecture gains traction, understanding its security vulnerabilities becomes increasingly important. This dataset provides researchers with real traffic patterns observed during various attack scenarios on NDN networks.

The dataset captures traffic parameters across:

1. Tree Topology: A hierarchical network structure commonly used in organizational networks
2. DFN Topology: Based on the German Research Network topology, representing a more complex, real-world network configuration

Data Collection Methodology All data was systematically collected through:

1. Setting up miniNDN environments on Ubuntu
2. Configuring both Tree and DFN network topologies
3. Executing controlled attack scenarios
4. Capturing comprehensive network traffic parameters
5. Labeling data with attack types and relevant metadata

Features https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F17020645%2F9e3da0ea20cf30dd62d34a2ab7a1c58b%2Ftree.png?generation=1747460661383218&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F17020645%2Ff1459323e4195379bd2a8e8ea186eef1%2Fdfn.png?generation=1747460683137915&alt=media" alt=""> The dataset includes essential NDN traffic parameters:

1. Packet type (Interest, Data, Nack)
2. Node and interface identifiers
3. Packet size and hop count metrics
4. Interest lifetime values
5. Content Store, PIT, and FIB entries
6. Attack classification labels
7. Topology identifiers

Applications This dataset is valuable for:

• Developing NDN-specific intrusion detection systems
• Comparing attack propagation across different network architectures
• Training machine learning models for attack detection
• Benchmarking security solutions for content-centric networks
• Understanding how topology affects security vulnerability

The dataset is a set of network traffic traces in pcap/csv format captured from a single user. The traffic is classified in 5 different activities (Video, Bulk, Idle, Web, and Interactive) and the label is shown in the filename. There is also a file (mapping.csv) with the mapping of the host's IP address, the csv/pcap filename and the activity label.

Activities:

Interactive: applications that perform real-time interactions in order to provide a suitable user experience, such as editing a file in google docs and remote CLI's sessions by SSH. Bulk data transfer: applications that perform a transfer of large data volume files over the network. Some examples are SCP/FTP applications and direct downloads of large files from web servers like Mediafire, Dropbox or the university repository among others. Web browsing: contains all the generated traffic while searching and consuming different web pages. Examples of those pages are several blogs and new sites and the moodle of the university. Vídeo playback: contains traffic from applications that consume video in streaming or pseudo-streaming. The most known server used are Twitch and Youtube but the university online classroom has also been used. Idle behaviour: is composed by the background traffic generated by the user computer when the user is idle. This traffic has been captured with every application closed and with some opened pages like google docs, YouTube and several web pages, but always without user interaction.

The capture is performed in a network probe, attached to the router that forwards the user network traffic, using a SPAN port. The traffic is stored in pcap format with all the packet payload. In the csv file, every non TCP/UDP packet is filtered out, as well as every packet with no payload. The fields in the csv files are the following (one line per packet): Timestamp, protocol, payload size, IP address source and destination, UDP/TCP port source and destination. The fields are also included as a header in every csv file.

The amount of data is stated as follows:

Bulk : 19 traces, 3599 s of total duration, 8704 MBytes of pcap files Video : 23 traces, 4496 s, 1405 MBytes Web : 23 traces, 4203 s, 148 MBytes Interactive : 42 traces, 8934 s, 30.5 MBytes Idle : 52 traces, 6341 s, 0.69 MBytes

The code of our machine learning approach is also included. There is a README.txt file with the documentation of how to use the code.

The Network Traffic Dataset from IoT23 is a subset of the publicly available IoT23 dataset from Stratosphere IPS. This dataset captures network traffic collected from various IoT devices, providing labeled instances of both malicious and benign traffic.

Dataset Overview Source: Derived from the IoT23 dataset Size: ~25MB Purpose: Designed for machine learning and AI applications in network security Key Feature: The dataset contains a malicious column that indicates whether a given traffic instance is malicious or benign Usage This dataset is particularly useful for training and evaluating AI models that classify network traffic within cloud environments or IoT ecosystems, helping to detect potential cyber threats. However, before use, data preprocessing is required as it contains several irrelevant columns that should be cleaned to improve model efficiency.

Potential Applications Intrusion Detection Systems (IDS) Network Security Monitoring Anomaly Detection in Cloud and IoT Networks This dataset serves as a valuable resource for cybersecurity research, enabling AI-driven solutions for identifying and mitigating cyber threats in IoT and cloud-based infrastructures.

General Description

This dataset was developed from real data on the usage of the corporate data network at the Universidade Federal do Rio Grande do Norte (UFRN). The main objective is to enable detailed observation of the university's network infrastructure and make this data available to the academic community. Data collection started on August 30, 2023, with the last query conducted on February 7, 2025, covering a total of approximately 19 months of continuous observations. During this period, about 1.5 months of data were lost due to failures in the data collection process or maintenance of the system responsible for capturing the data.

The data collections cover administrative, academic, and classroom sectors, spanning a total of 13 buildings within the university, providing a broad view of the network across different environments.

The dataset contains a total of 1,675,843 entries, each with 49 attributes.

Dataset Attributes, by Category

1. Connected Machines and ARP (8 attributes)

• Number of Access, Wi-Fi, Security, and VoIP Machines: Indicates the number of machines connected to each type of network, providing insight into the network size and device load.
• ARP Value for Access, Wi-Fi, Security, and VoIP: Refers to the number of entries in the Address Resolution Protocol (ARP) table associated with each type of network. ARP is used to map IP addresses to MAC addresses and can indicate potential connectivity issues.

2. Traffic Metrics (18 attributes)

• Packet and Byte: Indicates whether the information queried is accounted in packets or transmitted bytes, with positive (1) or negative (-1) values.
• Downlink and Uplink Bandwidth by Packets (Access, Wi-Fi, Security, VoIP): Refers to the number of packets received or sent by devices connected to each network type.
• Downlink and Uplink Bandwidth by Bytes (Access, Wi-Fi, Security, VoIP): Refers to the number of bytes received or sent by devices connected to each network type.

3. Collection Context (5 attributes)

• Sector: The sector from which the data was collected (academic, administrative, or classroom).
• Date: The date of the data collection.
• Time of Day: The time period of the collection (morning, afternoon, or evening).
• Day of the Week: The day of the week when the collection occurred.
• Hour: The hour of the collection.

4. Asset Identification (4 attributes)

• Asset IP: The IP address of the monitored device.
• Asset Model: The model of the network device.
• Asset Part Number: The part number of the device.
• Asset Firmware: The firmware version in use on the device.

5. Asset Performance (6 attributes)

• CPU Usage (% - 1 min and 5 min): The percentage of CPU usage on the device in the last minute and the last five minutes.
• Memory Used (%): The percentage of memory used by the device.
• Total and Used Memory (Kb): The total amount and the used amount of memory on the device, measured in Kb.
• Temperature (°C): The temperature of the device in degrees Celsius.

6. Port Packet Metrics (8 attributes)

• Packet In and Out Counter: The number of packets of data that have entered and exited all the device's ports.
• Broadcast Packet In and Out Counter: The number of broadcast packets that have entered and exited all the device's ports.
• Multicast Packet In and Out Counter: The number of multicast packets that have entered and exited all the device's ports.
• Packet Error In and Out Counter: The number of error packets that have entered and exited all the device's ports.

Size and Format

The dataset contains approximately 1,675,843 entries, with 49 attributes per entry. It is available in CSV format.

Global Data Center Networking Market size valued at US$ 23.03 Billion in 2023, set to reach US$ 44.09 Billion by 2032 at a CAGR of about 7.48% from 2024 to 2032

Description: This dataset is designed for research in energy-aware load optimization and routing in fog-based vehicular networks, focusing on dynamic traffic conditions and 5G-enabled communication. It includes key parameters affecting network performance, such as node mobility, traffic density, energy consumption, latency, packet delivery ratio (PDR), and throughput, along with optimized load balancing metrics. Key Features: Mobility & Traffic Metrics: Node speed, traffic density, and network topology types (Urban, Highway, Grid).

Routing Protocols: Includes GYTAR, AODV, and DSR for evaluating energy-efficient routing.

Energy Efficiency Metrics: Energy consumption, residual energy, and an Energy Efficiency Score (0–1 scale).

Network Performance: Latency, packet loss, throughput, and load distribution efficiency.

Target Variables:

Optimal Load Balance (Categorical: 0 = Poor, 1 = Moderate, 2 = Good)

Energy Efficiency Score (Continuous: 0–1 scale)

Use Cases: Optimizing Routing Protocols for Fog-Based Vehicular Networks.

Machine Learning Applications in Traffic Management & Load Balancing.

Energy-Aware Optimization Algorithms for Next-Gen Vehicular Communication.

5G and IoT-Based Smart Transportation Research.

The Japan Data Center Networking Market report segments the industry into By Component (By Product, By Services) and End-User (IT & Telecommunication, BFSI, Government, Media & Entertainment, Other End-Users). Get five years of historical data and five-year forecasts.

A Flexible Machine Learning-Aware Architecture for Future WLANs

Authors: Francesc Wilhelmi, Sergio Barrachina-Muñoz, Boris Bellalta, Cristina Cano, Anders Jonsson & Vishnu Ram.

Abstract: Lots of hopes have been placed in Machine Learning (ML) as a key enabler of future wireless networks. By taking advantage of the large volumes of data generated by networks, ML is expected to deal with the ever-increasing complexity of networking problems. Unfortunately, current networking systems are not yet prepared for supporting the ensuing requirements of ML-based applications, especially for enabling procedures related to data collection, processing, and output distribution. This article points out the architectural requirements that are needed to pervasively include ML as part of future wireless networks operation. To this aim, we propose to adopt the International Telecommunications Union (ITU) unified architecture for 5G and beyond. Specifically, we look into Wireless Local Area Networks (WLANs), which, due to their nature, can be found in multiple forms, ranging from cloud-based to edge-computing-like deployments. Based on ITU's architecture, we provide insights on the main requirements and the major challenges of introducing ML to the multiple modalities of WLANs.

Dataset description: This is the dataset generated for training a Neural Network (NN) in the Access Point (AP) (re)association problem in IEEE 802.11 Wireless Local Area Networks (WLANs).

In particular, the NN is meant to output a prediction function of the throughput that a given station (STA) can obtain from a given Access Point (AP) after association. The features included in the dataset are:

Identifier of the AP to which the STA has been associated.

RSSI obtained from the AP to which the STA has been associated.

Data rate in bits per second (bps) that the STA is allowed to use for the selected AP.

Load in packets per second (pkt/s) that the STA generates.

Percentage of data that the AP is able to serve before the user association is done.

Amount of traffic load in pkt/s handled by the AP before the user association is done.

Airtime in % that the AP enjoys before the user association is done.

Throughput in pkt/s that the STA receives after the user association is done.

The dataset has been generated through random simulations, based on the model provided in https://github.com/toniadame/WiFi_AP_Selection_Framework. More details regarding the dataset generation have been provided in https://github.com/fwilhelmi/machine_learning_aware_architecture_wlans.

The data center networking market is experiencing robust growth, driven by the increasing adoption of cloud computing, the proliferation of data-intensive applications, and the rise of edge computing. The market's expansion is fueled by the need for high-bandwidth, low-latency networks capable of handling the ever-increasing volume of data traffic. Key trends shaping the market include the adoption of software-defined networking (SDN), network function virtualization (NFV), and the integration of artificial intelligence (AI) and machine learning (ML) for enhanced network management and automation. The shift towards 5G and the expansion of the Internet of Things (IoT) are further accelerating demand for advanced data center networking solutions. While the market faces certain restraints, such as the high cost of infrastructure and the complexity of implementing new technologies, these are being offset by significant long-term growth projections. Competition is fierce among major players such as Alcatel Lucent, Cisco, Dell, EMC, IBM, Extreme, HP, Intel, Microsoft, VMware, NEC, Juniper, Fujitsu, and Equinix, each vying for market share with innovative solutions and strategic partnerships. The market is segmented by component (switches, routers, optical transport), by technology (Ethernet, Fibre Channel), and by application (cloud computing, enterprise data centers). Based on a typical CAGR of 10% (a reasonable estimate given industry trends), and a 2025 market size of $200 billion (this is an estimated figure based on typical industry valuations), we can project significant growth throughout the forecast period. The market will likely see continued consolidation, with larger vendors acquiring smaller players to gain access to cutting-edge technologies and expand their market reach. Geographical growth will likely be driven by developing economies in Asia-Pacific and Latin America, where the demand for data center infrastructure is rapidly increasing. The focus on sustainability and energy efficiency in data center operations will also influence technology choices and investment strategies. Overall, the data center networking market is poised for continued expansion, driven by powerful technological advancements and the increasing reliance on digital infrastructure globally.

The Mexico Data Center Networking Market report segments the industry into By Component (By Product, By Services) and End-User (IT & Telecommunication, BFSI, Government, Media & Entertainment, Other End-Users). Get five years of historic data and five-year forecasts.

Information technology IT computers, wired and wireless digital networks, electronic data and information, IT devices and systems, and software applications?today provides indispensable infrastructure for activities across all facets of society. Throughout the IT revolution, the United States has led the world in the invention and applications of these technologies. Ongoing research and development R and D to provide advanced IT capabilities for Federal missions has fueled the creation of new ideas, and innovations addressing key national priorities, including national security, national defense, economic prosperity, scientific discovery, energy and environment, health, individual privacy, and quality of life...

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Software-Defined Networking (SDN) Market Size 2024-2028

The software-defined networking (SDN) market size is forecast to increase by USD 67.38 billion at a CAGR of 29.04% between 2023 and 2028. The market is experiencing remarkable growth due to several key drivers. First, the increasing adoption of cloud solutions by various industries, including banking, financial services, and insurance (BFSI), is fueling the demand for SDN technology. Second, the proliferation of the Internet of Things (IoT) and edge computing is creating complex network design challenges that SDN is well-positioned to address. Network policies and security configurations can be centrally managed and applied consistently across the organization, ensuring optimal application performance. The BFSI segment, in particular, stands to benefit from SDN's ability to improve network agility and security while reducing operational costs. This trend is expected to continue, making SDN an essential technology for businesses looking to streamline their IT infrastructure and enhance their customer experience. In summary, the SDN market is witnessing substantial growth due to the adoption of cloud solutions, the emergence of 5G technology, and the need for advanced security technologies.

Market Analysis

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Software-Defined Networking (SDN) is a revolutionary network architecture that separates the control plane from the data plane, enabling network managers to centrally manage network resources and traffic flows in real-time. This approach offers numerous benefits, including increased network agility, improved network performance, and enhanced security. However, the adoption of SDN also presents certain challenges and emerging threats that require continuous monitoring and advanced security technologies. SDN architectures offer control functions that enable network administrators to make quick decisions and respond to disruptions, unauthorized access, malicious activities, and system failures.

Moreover, these functions include authentication mechanisms, encryption protocols, and continuous monitoring. However, the centralized controller used in SDN can be a potential target for targeted attacks, making security a top priority. The SDN market is witnessing significant growth due to the increasing demand for intelligent network architectures in the 5G ecosystem. The 5G ecosystem requires high network bandwidth, low latency, and network redundancy to support the massive data flows generated by IoT devices and other connected devices. SDN provides the flexibility and agility required to meet these demands and address reliability concerns. Despite the benefits, SDN adoption faces challenges related to network outages and collaboration between enterprises segment.

Network outages can lead to significant downtime and revenue loss, making network reliability a critical concern. Collaboration between enterprises is also essential to ensure seamless communication and data exchange in SDN environments. Advanced security technologies, such as machine learning and artificial intelligence, are being integrated into SDN to address emerging threats. These technologies enable real-time threat detection and response, ensuring the security and integrity of network resources. In conclusion, the SDN market is experiencing significant growth due to the increasing demand for agile networks and intelligent network architectures in the 5G ecosystem. While SDN offers numerous benefits, it also presents certain challenges related to security and network reliability. Continuous monitoring, advanced security technologies, and collaboration between enterprises are essential to ensure the successful adoption and implementation of SDN.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  BFSI
  Healthcare
  Retail
  Education
  Others


Component

  Physical network infrastructure
  SDN applications
  Controller software


Geography

  North America

    US


  APAC

    China
    Japan


  Europe

    UK


  South America



  Middle East and Africa

By End-user Insights

The BFSI segment is estimated to witness significant growth during the forecast period. Software-Defined Networking (SDN) is a cutting-edge technology that offers significant benefits to secure networks in various industries, particularly in high-performance sectors like financial services. In the US market, the Banking, Finance, Services, and Insurance (BFSI) segment is poised for substantial growth in the SDN domain. This technology enables financial institutions to manage their networks more effectively, ensuring compliance and optimizing traffic flows. SDN's centralized management system

Indonesia Data Center Networking Market Report Segments the Industry Into Components (By Product, by Services), End-Users (IT & Telecommunication, BFSI, Other End-Users). Data-Center Type(Colocation, Hyperscalers/Cloud Service Providers, and More). And Bandwidth( ≤10 GbE, 25–40 GbE, and More). The Market Forecasts are Provided in Terms of Value (USD).

reliability

This dataset presents network traffic captures for Internet of Things (IoT) device identification and profiling

The Time-Sensitive Networking Report is Segmented by Component (Ethernet Switches, Network Interface Cards, and More), Application (Factory Automation and Control, Automotive In-Vehicle Networking, and More), End-User Industry (Process Industries, Utilities, and More), Network Topology (Wired Deterministic Ethernet, Hybrid Wired-Wireless TSN, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

This data set contains data related to the paper 'TrafPy: Benchmarking Data Centre Network Systems'. The data have been split into 3 files to avoid needing to download all data sets if only some are needed:1) plotData: The data plotted in the paper for each of the benchmarks averaged across 5 runs.2) trafficData: The flow-centric traffic requests used in each of the simulations.3) simulationData: Each individual benchmark run. Contains full access to the simulation history, metrics, and so on. When unzipped, this file is ~2.5 TB in size.

The Dataset SDN_Cyber consists of 4,23,046 instances of normal and attack classes maintained as 16 csv files that covers 5 types of cyber-attacks. It includes 11 attack and 5 normal classes. It is generated from the SDN environment simulated using Ryu controller and Mininet. KaliLinux and Metasploitable are used as intruder and victim machines. The ofctl_rest application of Ryu controller is used to collect the 27 features of the flows. This synthetic generated dataset can be efficiently used to train an intrusion detection system to be deployed in SDN environment.

The pathway representation consists of segments and intersection elements. A segment is a linear graphic element that represents a continuous physical travel path terminated by path end (dead end) or physical intersection with other travel paths. Segments have one street name, one address range and one set of segment characteristics. A segment may have none or multiple alias street names. Segment types included are Freeways, Highways, Streets, Alleys (named only), Railroads, Walkways, and Bike lanes. SNDSEG_PV is a linear feature class representing the SND Segment Feature, with attributes for Street name, Address Range, Alias Street name and segment Characteristics objects. Part of the Address Range and all of Street name objects are logically shared with the Discrete Address Point-Master Address File layer. Appropriate uses include: Cartography - Used to depict the City's transportation network location and connections, typically on smaller scaled maps or images where a single line representation is appropriate. Used to depict specific classifications of roadway use, also typically at smaller scales. Used to label transportation network feature names typically on larger scaled maps. Used to label address ranges with associated transportation network features typically on larger scaled maps. Geocode reference - Used as a source for derived reference data for address validation and theoretical address location Address Range data repository - This data store is the City's address range repository defining address ranges in association with transportation network features. Polygon boundary reference - Used to define various area boundaries is other feature classes where coincident with the transportation network. Does not contain polygon features. Address based extracts - Used to create flat-file extracts typically indexed by address with reference to business data typically associated with transportation network features. Thematic linear location reference - By providing unique, stable identifiers for each linear feature, thematic data is associated to specific transportation network features via these identifiers. Thematic intersection location reference - By providing unique, stable identifiers for each intersection feature, thematic data is associated to specific transportation network features via these identifiers. Network route tracing - Used as source for derived reference data used to determine point to point travel paths or determine optimal stop allocation along a travel path. Topological connections with segments - Used to provide a specific definition of location for each transportation network feature. Also provides a specific definition of connection between each transportation network feature. (defines where the streets are and the relationship between them ie. 4th Ave is west of 5th Ave and 4th Ave does intersect with Cherry St) Event location reference - Used as source for derived reference data used to locate event and linear referencing.Data source is TRANSPO.SNDSEG_PV. Updated weekly.

As required by statute, the President`s Council of Advisors on Science and Technology (PCAST) is tasked with periodically reviewing the Networking and Information Technology Research and Development (NITRD) Program, the Nation's primary source of federally funded research and development in advanced information technologies such as computing, networking, and software. This report examines the NITRD Program's progress since the last review was conducted in 2015, explores emerging areas of interest relevant to the NITRD Program, and presents PCAST's findings and recommendations.