The Network Topology Software market is experiencing robust growth, driven by the increasing complexity of IT infrastructures and the rising demand for efficient network management and visualization. The market's expansion is fueled by several key factors, including the proliferation of cloud computing, the adoption of Software-Defined Networking (SDN), and the increasing need for enhanced network security and performance monitoring. Businesses across various sectors, from telecommunications and finance to healthcare and education, are actively seeking solutions to optimize network performance, troubleshoot issues swiftly, and improve overall network visibility. The market is witnessing a shift towards cloud-based solutions, offering scalability, accessibility, and cost-effectiveness compared to traditional on-premise deployments. This trend is further accelerating the adoption of network topology mapping tools among small and medium-sized businesses (SMBs), which previously lacked the resources to invest in sophisticated network management systems. Competition is fierce, with established players like SolarWinds and Paessler alongside emerging agile companies vying for market share through innovative features and competitive pricing. The forecast period (2025-2033) anticipates continued expansion, though the CAGR might moderate slightly compared to the historical period (2019-2024) due to market saturation in certain segments. Despite this, growth will be propelled by the adoption of advanced analytics and AI-powered features in network topology software, enabling predictive maintenance and proactive network optimization. The integration of network topology mapping with other IT management tools will also be a significant driver. Geographical expansion, particularly in developing economies with rapidly growing IT infrastructure, presents lucrative opportunities. However, challenges remain, including the need for skilled personnel to manage and interpret the complex data generated by these systems, along with potential security vulnerabilities associated with network mapping tools. The market will continue to evolve, with a focus on improved user experience, enhanced automation capabilities, and seamless integration with existing IT infrastructure.

The development of chemical reaction models aids understanding and prediction in areas ranging from biology to electrochemistry and combustion. A systematic approach to building reaction network models uses observational data not only to estimate unknown parameters but also to learn model structure. Bayesian inference provides a natural approach to this data-driven construction of models. Yet traditional Bayesian model inference methodologies that numerically evaluate the evidence for each model are often infeasible for nonlinear reaction network inference, as the number of plausible models can be combinatorially large. Alternative approaches based on model-space sampling can enable large-scale network inference, but their realization presents many challenges. In this paper, we present new computational methods that make large-scale nonlinear network inference tractable. First, we exploit the topology of networks describing potential interactions among chemical species to design improved ‘between-model’ proposals for reversible-jump Markov chain Monte Carlo. Second, we introduce a sensitivity-based determination of move types which, when combined with network-aware proposals, yields significant additional gains in sampling performance. These algorithms are demonstrated on inference problems drawn from systems biology, with nonlinear differential equation models of species interactions.

The Network Slicing Market size was valued at USD 759.0 USD Million in 2023 and is projected to reach USD 10732.77 USD Million by 2032, exhibiting a CAGR of 46.0 % during the forecast period. Network slicing is a technology by which multiple virtual networks are generated on top of the shared physical network to enable the use and allocation of network resources to be customized according to the necessities of users and applications. Every sliced network has the freedom of creating a suitable network topology with given constraints which include security rules, performance limits and the physical networks that enable them to run. Various section are capable of reserving capacity for needed application, scheduling services in such away, and isolating and intended traffic different user groups and device categories. Firstly, carriers can cut down the service provision cost and at the same time provide dynamic and adaptive network services that can be scheduled and assigned on demand by allocating network resources according to service demands. Thus, it adds values to the networks, explores the monetization of carriers, and invigorates various industries with digital technology. Recent developments include: February 2024: Ericsson, BT Bunch, and Qualcomm Advances, Inc. have effectively illustrated end-to-end enterprise and consumer 5G network slicing. This is empowered by Ericsson's 5G Center and Radio Access Network Technology to Organize innovation within the UK., August 2023: T-Mobile launched 5G network slicing beta for engineers. The beta enables engineers to supercharge video calling applications by giving upgraded arrange conditions, steady uplink and downlink speeds, lower idleness, and expanded unwavering quality, all through T-Mobile's 5G SA (Standalone) arrangement., June 2023: Nokia reported that it has effectively trialled an inventive modern arrangement that empowers Android smartphone clients to buy and actuate network slices on-demand from their administrator. The move, which is accessible to Android 14 clients, will permit conclusion clients to improve their encounters over a wide range of applications, such as gaming, broadcasting, spilling, and social media. , July 2022: Ericsson collaborated with Telefonica to reveal its end-to-end, automated network slicing in 5G standalone in Madrid., June 2022: Amdocs and A1 Telekom Austria Group achieved a 5G network slicing proof of concept. This proof of concept determines the capability of Amdocs’ service and network offering to fuel on-demand connectivity and next-generation experiences for enterprises and consumers through the management, deployment, and monetization of 5G network slices.. Key drivers for this market are: Increasing Demand for Next-generation 5G Networks and Proliferation of SDN and NFV to Drive Market Growth. Potential restraints include: Security Breaches Leading to Massive Operational and Financial Loss to Hamper the Market Growth. Notable trends are: Increasing Demand for IoT Solutions to Propel the Market.

Stochastic chemical reaction networks constitute a model class to quantitatively describe dynamics and cell-to-cell variability in biological systems. The topology of these networks typically is only partially characterized due to experimental limitations. Current approaches for refining network topology are based on the explicit enumeration of alternative topologies and are therefore restricted to small problem instances with almost complete knowledge. We propose the reactionet lasso, a computational procedure that derives a stepwise sparse regression approach on the basis of the Chemical Master Equation, enabling large-scale structure learning for reaction networks by implicitly accounting for billions of topology variants. We have assessed the structure learning capabilities of the reactionet lasso on synthetic data for the complete TRAIL induced apoptosis signaling cascade comprising 70 reactions. We find that the reactionet lasso is able to efficiently recover the structure of these reaction systems, ab initio, with high sensitivity and specificity. With only < 1% false discoveries, the reactionet lasso is able to recover 45% of all true reactions ab initio among > 6000 possible reactions and over 102000 network topologies. In conjunction with information rich single cell technologies such as single cell RNA sequencing or mass cytometry, the reactionet lasso will enable large-scale structure learning, particularly in areas with partial network structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks. We provide software to allow for wide applicability of the reactionet lasso.

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Network Interface Cards Market Size 2025-2029

The network interface cards market size is forecast to increase by USD 3.65 billion, at a CAGR of 5.4% between 2024 and 2029.

The Network Interface Cards (NIC) market is driven by the increasing demand for high-speed Internet connectivity, fueled by the proliferation of data-intensive applications and the growing number of connected devices. The emergence of bring-your-own-device (BYOD) policies in organizations further amplifies this trend, necessitating the deployment of advanced NICs to support diverse connectivity requirements. However, the market faces challenges in the form of heightened security concerns. With the growing adoption of cloud computing and the increasing number of network attacks, ensuring data security through NICs has become a critical concern. The integration of advanced security features, such as encryption and firewalls, into NICs is essential to mitigate these risks and maintain the confidence of businesses and consumers alike. Effectively addressing these challenges while capitalizing on the market's growth opportunities requires a strategic focus on innovation and security, enabling companies to differentiate themselves and capture a larger share in the competitive NIC landscape.

What will be the Size of the Network Interface Cards Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe network interface cards (NICs) market continues to evolve, driven by advancements in industrial automation, routing protocols, Ethernet switches, high-performance computing, and cloud computing. Industrial automation applications require NICs that support real-time data transmission and reliability, making fiber channel and TCP/IP essential for this sector. In the realm of high-performance computing, NICs optimized for Ethernet switches and 4G LTE enable faster data transfer and improved network performance. Routing protocols, such as IP addressing and network management, play a crucial role in ensuring efficient data flow and network security. The ongoing development of 5G NR and wireless LAN technologies pushes the boundaries of wireless networking, offering increased bandwidth and lower latency. Network upgrades and network segmentation are essential for maintaining network security and optimizing network performance. Mesh networking and remote monitoring are transforming industries, allowing for seamless connectivity and real-time data access. Ethernet switches and TCP/IP are integral components of these applications, enabling reliable and efficient communication between devices. In the realm of cloud computing, NICs optimized for high-performance computing and network management are essential for managing large-scale data centers and ensuring optimal network performance. The integration of network drivers, network security, and intrusion detection systems further enhances the capabilities of NICs, providing robust protection against cyber threats and ensuring data privacy. Embedded systems and consumer electronics also benefit from the ongoing advancements in NIC technology, enabling faster data transmission and improved connectivity. The network topology landscape is continually evolving, with bus, star, and ring topologies being replaced by more advanced architectures. As the demand for faster and more reliable network connectivity grows, the NIC market will continue to innovate and adapt to meet the needs of various industries and applications.

How is this Network Interface Cards Industry segmented?

The network interface cards industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypeEthernet interface cardToken ring interface cardApplicationPCsPortable PCsSwitchesModemsEnd-userData centreNetworking service providersOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaJapanSouth KoreaTaiwanRest of World (ROW)

By Type Insights

The ethernet interface card segment is estimated to witness significant growth during the forecast period.The global network interface cards (NICs) market is experiencing significant growth, driven by the increasing demand for high-speed internet and efficient data transfer solutions in various industries. Ethernet interface cards, a dominant segment in the market, are essential components in both wired and wireless communication networks. They facilitate seamless data exchange and enhance network performance by enabling fast and reliable data transmission. In 2023, technological advancements included Intel's launch of its 14th-generation processors, which integrate NICs for desktops. This integration simplifies networ

The Network Mapping Tool market is experiencing robust growth, driven by the increasing complexity of IT infrastructures and the rising demand for enhanced network visibility and security. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors, including the widespread adoption of cloud computing, the rise of hybrid and multi-cloud environments, and the increasing prevalence of cyber threats. Businesses are increasingly relying on network mapping tools to optimize network performance, proactively identify and resolve issues, and ensure compliance with regulatory requirements. The need for real-time network monitoring and automated incident response further contributes to market expansion. The competitive landscape is characterized by a mix of established players like SolarWinds, Cisco, and Tufin, along with emerging innovative companies like 10-Strike and ManageEngine. These vendors offer a diverse range of solutions catering to different enterprise needs and budget considerations. Future growth will be influenced by the ongoing development of AI-powered analytics within network mapping tools, enabling predictive maintenance and automated remediation. Furthermore, the integration of network mapping with other security and IT management platforms will play a crucial role in shaping market evolution. The market's segmentation based on deployment type (cloud, on-premise), organization size, and industry vertical offers substantial opportunities for specialized vendors to cater to specific niche requirements. Geographic expansion, particularly in developing economies, will also contribute to overall market growth throughout the forecast period.

Network Mapping Tool Market Outlook

The global Network Mapping Tool market size is anticipated to grow significantly, reaching an approximate value of USD 10.5 billion by 2032 from USD 4.3 billion in 2023, driven by a Compound Annual Growth Rate (CAGR) of 10.7%. The primary growth factors for this market include the increasing complexity of IT infrastructure, the rising necessity for network security, and the growing adoption of cloud-based services. As organizations continuously seek to enhance network performance and security, the demand for comprehensive network mapping tools is expected to surge.

One of the critical growth drivers for the Network Mapping Tool market is the escalating complexity of IT environments. With the advent of the Internet of Things (IoT), cloud computing, and virtualization, networks have become increasingly sophisticated. Organizations must manage vast arrays of interconnected devices and applications, necessitating advanced tools that can visualize and monitor these networks effectively. Network mapping tools provide crucial insights into network topology, device connections, and data flow, enabling IT teams to manage and troubleshoot networks more efficiently, thereby driving market growth.

Another significant growth factor is the rising need for network security. Cybersecurity threats are becoming more sophisticated, and organizations must continuously monitor their networks to identify and mitigate potential vulnerabilities. Network mapping tools play a vital role in this regard by offering real-time visualization of network components, which helps in identifying unusual patterns and potential breach points. By providing a comprehensive view of the network, these tools aid in implementing robust security measures, thereby ensuring the integrity and safety of organizational data.

The increasing adoption of cloud-based services is also a key driver of market growth. As more organizations shift their operations to the cloud, the need for tools that can manage and map these cloud networks becomes critical. Cloud-based network mapping tools offer flexibility, scalability, and real-time updates, making them indispensable for modern IT infrastructure management. Moreover, they provide cost-effective solutions with reduced need for physical hardware, appealing to both large enterprises and Small and Medium Enterprises (SMEs). This trend is expected to continue, further propelling the market.

Regionally, North America is anticipated to hold a significant share of the Network Mapping Tool market, driven by technological advancements and high adoption rates of innovative IT solutions. Additionally, the presence of key market players and significant investments in cybersecurity and network infrastructure contribute to the regional market's growth. Asia Pacific is also expected to witness substantial growth, fueled by rapid digitalization, increasing internet penetration, and rising investments in IT infrastructure. Europe, Latin America, and the Middle East & Africa regions are projected to experience steady growth, supported by gradual adoption of advanced network management solutions.

Component Analysis

The Network Mapping Tool market is segmented by component into software and services. The software segment is anticipated to dominate the market due to the increasing demand for sophisticated network visualization and management solutions. Network mapping software provides advanced features such as automated discovery, real-time monitoring, and detailed analytics, which are essential for managing complex network environments. With the continuous advancements in AI and machine learning, software solutions are becoming more intelligent, offering predictive analytics and automated troubleshooting capabilities. This segment is expected to witness robust growth as organizations seek to optimize their network operations and enhance security.

On the other hand, the services segment is also poised for significant growth, driven by the rising need for professional services such as consulting, implementation, training, and support. As network environments become more complex, organizations often require expert assistance to deploy and manage network mapping tools effectively. Professional services help organizations to customize solutions according to their specific needs, ensure seamless integration with existing systems, and provide ongoing support to address any technical issues. The gr

The Network Visualization System market is experiencing robust growth, driven by the increasing complexity of network infrastructures and the rising demand for efficient network management solutions. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant growth is fueled by several key factors, including the proliferation of IoT devices, the adoption of cloud computing and virtualization technologies, and the growing need for enhanced network security and performance monitoring. Businesses across various sectors, including telecommunications, IT, and finance, are increasingly adopting network visualization systems to gain real-time insights into network performance, identify potential bottlenecks, and proactively address security threats. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are integrating into these systems, enabling predictive analytics and automated troubleshooting, further enhancing their value proposition. The market is segmented by various factors, including deployment type (on-premise, cloud), organization size (small, medium, large), and application (network monitoring, security management, capacity planning). Key players in the market, such as Shenzhen Sinovatio Technology Co., Ltd, Beijing Haohan Data Technology Co., Ltd, and Hangzhou Dptech Technologies Co.,Ltd, are constantly innovating to offer advanced features and cater to the evolving needs of their customer base. While the market is experiencing substantial growth, challenges remain, including the high initial investment costs associated with implementing these systems and the complexity involved in integrating them with existing network infrastructure. However, the long-term benefits of improved network efficiency, enhanced security, and reduced operational costs are expected to drive market expansion significantly in the coming years.

Multi Hop Networks Solutions Market Outlook

In 2023, the global market size for Multi Hop Networks Solutions was valued at approximately USD 4.5 billion, and it is projected to reach USD 9.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.4% during the forecast period. The primary growth factors contributing to this market include the rising demand for seamless connectivity in smart cities, advancements in industrial automation, and the increasing integration of IoT devices across various sectors.

The growth of smart cities is one of the major driving factors for the Multi Hop Networks Solutions market. As urban areas continue to expand, there is a pressing need for efficient and reliable communication networks that can handle large volumes of data transfer and connectivity requirements. Multi-hop network solutions offer a robust and scalable approach to meet these demands by enabling the interconnection of multiple nodes, which helps in extending the network range and improving data transmission efficiency. This makes them particularly well-suited for smart city applications, where seamless connectivity is crucial for various services such as traffic management, public safety, and environmental monitoring.

Another significant growth factor is the advancements in industrial automation. Industries are increasingly adopting automation technologies to enhance operational efficiency, reduce costs, and improve safety. Multi-hop networks provide a reliable communication backbone for various industrial applications, including monitoring and controlling machinery, predictive maintenance, and ensuring the safety of personnel. The flexibility and scalability of multi-hop networks make them an ideal choice for industrial environments, where wired networks may be impractical or costly to implement. As industries continue to invest in automation technologies, the demand for multi-hop network solutions is expected to rise accordingly.

The increasing integration of IoT devices across various sectors is also driving the market for multi-hop network solutions. IoT devices rely on robust and reliable communication networks to function effectively. Multi-hop networks enable the seamless connection of numerous IoT devices by creating a network where each device can act as a relay point, extending the overall network coverage and improving data transmission reliability. This is particularly important in environments where direct communication between devices and a central hub is not feasible due to distance or physical obstructions. The growing adoption of IoT devices in applications such as smart homes, healthcare, transportation, and environmental monitoring is expected to fuel the demand for multi-hop network solutions.

Wireless Mesh Network WMN technology plays a pivotal role in the expansion of multi-hop networks, particularly in urban environments. As cities grow and the demand for connectivity increases, WMNs offer a flexible and cost-effective solution to extend network coverage without the need for extensive cabling. These networks consist of interconnected nodes that communicate wirelessly, allowing for dynamic routing of data and enhancing the network's resilience. The ability of WMNs to self-configure and adapt to changes in the network topology makes them particularly valuable in smart city applications, where infrastructure needs to be both robust and adaptable. As the demand for seamless connectivity in urban areas continues to rise, the adoption of Wireless Mesh Network WMN technology is expected to grow, further driving the market for multi-hop network solutions.

From a regional perspective, North America is anticipated to hold the largest market share during the forecast period, driven by the presence of advanced technology infrastructure and significant investments in smart city projects. The Asia Pacific region is expected to witness the highest growth rate, owing to rapid urbanization, increasing adoption of IoT technologies, and government initiatives to develop smart cities. Europe is also projected to experience steady growth, supported by advancements in industrial automation and the growing integration of IoT devices in various applications.

Component Analysis

The Multi Hop Networks Solutions market, segmented by component, comprises hardware, software, and services. Each component plays a crucial role in the functioning and deployment of multi-hop networks. The hardware segment includes devices such as

The Network Topology Mapping Software market is rapidly evolving, driven by the increasing complexity of network infrastructures and the vital need for organizations to optimize their operations. This specialized software provides visual representations of various network components, enabling businesses to analyze,

The Global River Topology (GRIT) is a vector-based, global river network that not only represents the tributary components of the global drainage network but also the distributary ones, including multi-thread rivers, canals and delta distributaries. It is also the first global hydrography (excl. Antarctica and Greenland) produced at 30m raster resolution. It is created by merging Landsat-based river mask (GRWL) with elevation-generated streams to ensure a homogeneous drainage density outside of the river mask (rivers narrower than approx. 30m). Crucially, it uses a new 30m digital terrain model (FABDEM, based on TanDEM-X) that shows greater accuracy over the traditionally used SRTM derivatives. After vectorisation and pruning, directionality is assigned by a combination of elevation, flow angle, heuristic and continuity approaches (based on RivGraph). The network topology (lines and nodes, upstream/downstream IDs) is available as layers and attribute information in the GeoPackage files (readable by QGIS/ArcMap/GDAL).

Regions

Vector files are provided in 6 continental regions with the following codes:

• AF - Africa
• AS - Asia
• EU - Europe
• NA - North America
• SA - South America
• SP - South Pacific/Australia

The domain polygons (GRITv04_domain_GLOBAL.gpkg.zip) provide 60 subcontinental catchment groups that are available as vector attributes. They allow for more fine-grained subsetting of data (e.g. with ogr2ogr --where).

Network segments

Lines between inlet, outlet, confluence and bifurcation nodes. Files have lines and nodes layers.

Attribute description of lines layer

Name	Data type	Description
cat	integer	domain internal feature ID
global_id	integer	global river segment ID, same as FID
catchment_id	integer	global catchment ID
upstream_node_id	integer	global segment node ID at upstream end of line
downstream_node_id	integer	global segment node ID at downstream end of line
upstream_line_ids	text	comma-separated list of global river segment IDs connecting at upstream end of line
downstream_line_ids	text	comma-separated list of global river segment IDs connecting at downstream end of line
direction_algorithm	float	code of RivGraph method used to set the direction of line
width_adjusted	float	median river width in m without accounting for width of segments connecting upstream/downstream
length_adjusted	float	segment length in m without accounting for width of segments connecting upstream/downstream in m
is_mainstem	integer	1 if widest segment of bifurcated flow or no bifurcation upstream, otherwise 0
cycle	integer	>0 if segment is part of an unresolved cycle, 0 otherwise
length	float	segment length in m
azimuth	float	direction of line connecting upstream-downstream nodes in degrees from North
sinuous	float	ratio of line length and Euclidean distance between upstream-downstream nodes, i.e. 1 meaning a perfectly straight line
domain	text	catchment group ID, see domain index file

Attribute description of nodes layer

Name	Data type	Description
cat	integer	domain internal feature ID
global_id	integer	global river node ID, same as FID
catchment_id	integer	global catchment ID
upstream_line_ids	text	comma-separated list of global river segment IDs flowing into node
downstream_line_ids	text	comma-separated list of global river segment IDs flowing out of node
node_type	text	description of node, one of bifurcation, confluence, inlet, coastal_outlet, sink_outlet, grwl_change
grwl_value	integer	GRWL code at node
grwl_transition	text	GRWL codes of change at grwl_change nodes
cycle	integer	>0 if segment is part of an unresolved cycle, 0 otherwise
continuity_violated	integer	1 if flow continuity is violated, otherwise 0
domain	text	catchment group, see domain index file

Network reaches

Segment lines split to not exceed 1km in length, i.e. these lines will be shorter than 1km and longer than 500m unless the segment is shorter. A simplified version with no vertices between nodes is also provided. Files have lines and nodes layers.

Attribute description of lines layer

Name	Data type	Description
cat	integer	domain internal feature ID
segment_id	integer	global segment ID of reach
global_id	integer	global river reach ID, same as FID
catchment_id	integer	global catchment ID
upstream_node_id	integer	global reach node ID at upstream end of line
downstream_node_id	integer	global reach node ID at downstream end of line
upstream_line_ids	text	comma-separated list of global river reach IDs connecting at upstream end of line
downstream_line_ids	text	comma-separated list of global river reach IDs connecting at downstream end of line
length	float	length of reach in m
sinuousity	float	ratio of line length and Euclidian distance between upstream-downstream nodes, i.e. 1 meaning a perfectly straight line
azimuth	float	direction of line connecting upstream-downstream nodes in degrees from North
domain	text	catchment group, see domain index file

Attribute description of nodes layer

Name	Data type	Description
cat	integer	domain internal feature ID
segment_node_id	integer	global ID of segment node at segment intersections, otherwise blank
n_segments	integer	number of segments attached to node
global_id	integer	global river reach node ID, same as FID
upstream_line_ids	text	comma-separated list of global river reach IDs flowing into node
downstream_line_ids	text	comma-separated list of global river reach IDs flowing out of node
domain	text	catchment group, see domain index file

Catchments

Catchment outlines for entire river basins (network components, including coastal drainage areas), segments (aka. subbasins) and reaches.

Attribute description

Name	Data type	Description
cat	integer	domain internal feature ID
global_id	integer	global catchment ID, same as global_id of segment/reach ID if is_coastal == 0 for respective catchments or the catchment_id for component_catchments, same as FID
area	float	catchment area in km2
is_coastal	integer	1 for coastal drainage areas, 0 otherwise
domain	text	catchment group, see domain index file

Raster

Upstream drainage area, flow direction and other raster-based products are also available upon request.

This a model from the article: Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network. Kühn C, Wierling C, Kühn A, Klipp E, Panopoulou G, Lehrach H, Poustka AJ. BMC Syst Biol.2009 Aug 23;3:83. 19698179, Abstract: BACKGROUND: Gene Regulatory Networks (GRNs) control the differentiation, specification and function of cells at the genomic level. The levels of interactions within large GRNs are of enormous depth and complexity. Details about many GRNs are emerging, but in most cases it is unknown to what extent they control a given process, i.e. the grade of completeness is uncertain. This uncertainty stems from limited experimental data, which is the main bottleneck for creating detailed dynamical models of cellular processes. Parameter estimation for each node is often infeasible for very large GRNs. We propose a method, based on random parameter estimations through Monte-Carlo simulations to measure completeness grades of GRNs. RESULTS: We developed a heuristic to assess the completeness of large GRNs, using ODE simulations under different conditions and randomly sampled parameter sets to detect parameter-invariant effects of perturbations. To test this heuristic, we constructed the first ODE model of the whole sea urchin endomesoderm GRN, one of the best studied large GRNs. We find that nearly 48% of the parameter-invariant effects correspond with experimental data, which is 65% of the expected optimal agreement obtained from a submodel for which kinetic parameters were estimated and used for simulations. Randomized versions of the model reproduce only 23.5% of the experimental data. CONCLUSION: The method described in this paper enables an evaluation of network topologies of GRNs without requiring any parameter values. The benefit of this method is exemplified in the first mathematical analysis of the complete Endomesoderm Network Model. The predictions we provide deliver candidate nodes in the network that are likely to be erroneous or miss unknown connections, which may need additional experiments to improve the network topology. This mathematical model can serve as a scaffold for detailed and more realistic models. We propose that our method can be used to assess a completeness grade of any GRN. This could be especially useful for GRNs involved in human diseases, where often the amount of connectivity is unknown and/or many genes/interactions are missing. The paper describes several models, Mi, i=1...n, where M0 correspond to the unperturbed model and all the others correspond to the perturbed model. This model is the unperturbed model. The model reproduces figure 5 of the reference publication. The figures were generated by running 1 simulation, whereas in the paper the plotted values are the means of 800 simulations using randomly samples parameter sets. Additional information from the Author: The parameter that were randomly samples are the transcription parameters c_Proteins... and k_Proteins. The parameter were sampled from a lognormal distribution with sigma = 1.5 and mu = 0.5 This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.For more information see the terms of use.To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

1.Introduction

In the digital era of the Industrial Internet of Things (IIoT), the conventional Critical Infrastructures (CIs) are transformed into smart environments with multiple benefits, such as pervasive control, self-monitoring and self-healing. However, this evolution is characterised by several cyberthreats due to the necessary presence of insecure technologies. DNP3 is an industrial communication protocol which is widely adopted in the CIs of the US. In particular, DNP3 allows the remote communication between Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA). It can support various topologies, such as Master-Slave, Multi-Drop, Hierarchical and Multiple-Server. Initially, the architectural model of DNP3 consists of three layers: (a) Application Layer, (b) Transport Layer and (c) Data Link Layer. However, DNP3 can be now incorporated into the Transmission Control Protocol/Internet Protocol (TCP/IP) stack as an application-layer protocol. However, similarly to other industrial protocols (e.g., Modbus and IEC 60870-5-104), DNP3 is characterised by severe security issues since it does not include any authentication or authorisation mechanisms. More information about the DNP3 security issue is provided in [1-3]. This dataset contains labelled Transmission Control Protocol (TCP) / Internet Protocol (IP) network flow statistics (Common-Separated Values - CSV format) and DNP3 flow statistics (CSV format) related to 9 DNP3 cyberattacks. These cyberattacks are focused on DNP3 unauthorised commands and Denial of Service (DoS). The network traffic data are provided through Packet Capture (PCAP) files. Consequently, this dataset can be used to implement Artificial Intelligence (AI)-powered Intrusion Detection and Prevention (IDPS) systems that rely on Machine Learning (ML) and Deep Learning (DL) techniques.

2.Instructions

This DNP3 Intrusion Detection Dataset was implemented following the methodological frameworks of A. Gharib et al. in [4] and S. Dadkhah et al in [5], including eleven features: (a) Complete Network Configuration, (b) Complete Traffic, (c) Labelled Dataset, (d) Complete Interaction, (e) Complete Capture, (f) Available Protocols, (g) Attack Diversity, (h) Heterogeneity, (i) Feature Set and (j) Metadata.

A network topology consisting of (a) eight industrial entities, (b) one Human Machine Interfaces (HMI) and (c) three cyberattackers was used to implement this DNP3 Intrusion Detection Dataset. In particular, the following cyberattacks were implemented.

On Thursday, May 14, 2020, the DNP3 Disable Unsolicited Messages Attack was executed for 4 hours.

On Friday, May 15, 2020, the DNP3 Cold Restart Message Attack was executed for 4 hours.

On Friday, May 15, 2020, the DNP3 Warm Restart Message Attack was executed for 4 hours.

On Saturday, May 16, 2020, the DNP3 Enumerate Attack was executed for 4 hours.

On Saturday, May 16, 2020, the DNP3 Info Attack was executed for 4 hours.

On Monday, May 18, 2020, the DNP3 Initialisation Attack was executed for 4 hours.

On Monday, May 18, 2020, the Man In The Middle (MITM)-DoS Attack was executed for 4 hours.

On Monday, May 18, 2020, the DNP3 Replay Attack was executed for 4 hours.

On Tuesday, May 19, 2020, the DNP3 Stop Application Attack was executed for 4 hours.

The aforementioned DNP3 cyberattacks were executed, utilising penetration testing tools, such as Nmap and Scapy. For each attack, a relevant folder is provided, including the network traffic and the network flow statistics for each entity. In particular, for each cyberattack, a folder is given, providing (a) the pcap files for each entity, (b) the Transmission Control Protocol (TCP)/ Internet Protocol (IP) network flow statistics for 120 seconds in a CSV format and (c) the DNP3 flow statistics for each entity (using different timeout values in terms of second (such as 45, 60, 75, 90, 120 and 240 seconds)). The TCP/IP network flow statistics were produced by using the CICFlowMeter, while the DNP3 flow statistics were generated based on a Custom DNP3 Python Parser, taking full advantage of Scapy.

1. Dataset Structure

The dataset consists of the following folders:

20200514_DNP3_Disable_Unsolicited_Messages_Attack: It includes the pcap and CSV files related to the DNP3 Disable Unsolicited Message attack.

20200515_DNP3_Cold_Restart_Attack: It includes the pcap and CSV files related to the DNP3 Cold Restart attack.

20200515_DNP3_Warm_Restart_Attack: It includes the pcap and CSV files related to DNP3 Warm Restart attack.

20200516_DNP3_Enumerate: It includes the pcap and CSV files related to the DNP3 Enumerate attack.

20200516_DNP3_Ιnfo: It includes the pcap and CSV files related to the DNP3 Info attack.

20200518_DNP3_Initialize_Data_Attack: It includes the pcap and CSV files related to the DNP3 Data Initialisation attack.

20200518_DNP3_MITM_DoS: It includes the pcap and CSV files related to the DNP3 MITM-DoS attack.

20200518_DNP3_Replay_Attack: It includes the pcap and CSV files related to the DNP3 replay attack.

20200519_DNP3_Stop_Application_Attack: It includes the pcap and CSV files related to the DNP3 Stop Application attack.

Training_Testing_Balanced_CSV_Files: It includes balanced CSV files from CICFlowMeter and the Custom DNP3 Python Parser that could be utilised for training ML and DL methods. Each folder includes different sub-folder for the corresponding flow timeout values used by the DNP3 Python Custom Parser. For CICFlowMeter, only the timeout value of 120 seconds was used.

Each folder includes respective subfolders related to the entities/devices (described in the following section) participating in each attack. In particular, for each entity/device, there is a folder including (a) the DNP3 network traffic (pcap file) related to this entity/device during each attack, (b) the TCP/IP network flow statistics (CSV file) generated by CICFlowMeter for the timeout value of 120 seconds and finally (c) the DNP3 flow statistics (CSV file) from the Custom DNP3 Python Parser. Finally, it is noteworthy that the network flows from both CICFlowMeter and Custom DNP3 Python Parser in each CSV file are labelled based on the DNP3 cyberattacks executed for the generation of this dataset. The description of these attacks is provided in the following section, while the various features from CICFlowMeter and Custom DNP3 Python Parser are presented in Section 5.

4.Testbed & DNP3 Attacks

The following figure shows the testbed utilised for the generation of this dataset. It is composed of eight industrial entities that play the role of the DNP3 outstations/slaves, such as Remote Terminal Units (RTUs) and Intelligent Electron Devices (IEDs). Moreover, there is another workstation which plays the role of the Master station like a Master Terminal Unit (MTU). For the communication between, the DNP3 outstations/slaves and the master station, opendnp3 was used.

Table 1: DNP3 Attacks Description

DNP3 Attack

Description

Dataset Folder

DNP3 Disable Unsolicited Message Attack

This attack targets a DNP3 outstation/slave, establishing a connection with it, while acting as a master station. The false master then transmits a packet with the DNP3 Function Code 21, which requests to disable all the unsolicited messages on the target.

20200514_DNP3_Disable_Unsolicited_Messages_Attack

DNP3 Cold Restart Attack

The malicious entity acts as a master station and sends a DNP3 packet that includes the “Cold Restart” function code. When the target receives this message, it initiates a complete restart and sends back a reply with the time window before the restart process.

20200515_DNP3_Cold_Restart_Attack

DNP3 Warm Restart Attack

This attack is quite similar to the “Cold Restart Message”, but aims to trigger a partial restart, re-initiating a DNP3 service on the target outstation.

20200515_DNP3_Warm_Restart_Attack

DNP3 Enumerate Attack

This reconnaissance attack aims to discover which DNP3 services and functional codes are used by the target system.

20200516_DNP3_Enumerate

DNP3 Info Attack

This attack constitutes another reconnaissance attempt, aggregating various DNP3 diagnostic information related the DNP3 usage.

20200516_DNP3_Ιnfo

Data Initialisation Attack

This cyberattack is related to Function Code 15 (Initialize Data). It is an unauthorised access attack, which demands from the slave to re-initialise possible configurations to their initial values, thus changing potential values defined by legitimate masters

20200518_Initialize_Data_Attack

MITM-DoS Attack

In this cyberattack, the cyberattacker is placed between a DNP3 master and a DNP3 slave device, dropping all the messages coming from the DNP3 master or the DNP3 slave.

20200518_MITM_DoS

DNP3 Replay Attack

This cyberattack replays DNP3 packets coming from a legitimate DNP3 master or DNP3 slave.

20200518_DNP3_Replay_Attack

DNP3 Step Application Attack

This attack is related to the Function Code 18 (Stop Application) and demands from the slave to stop its function so that the slave cannot receive messages from the master.

20200519_DNP3_Stop_Application_Attack

1. Features

The TCP/IP network flow statistics generated by CICFlowMeter are summarised below. The TCP/IP network flows and their statistics generated by CICFlowMeter are labelled based on the DNP3 attacks described above, thus allowing the training of ML/DL models. Finally, it is worth mentioning that these statistics are generated when the flow timeout value is equal with 120 seconds.

Table

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Malaysia Dark Fiber Market Size 2024-2028

The malaysia dark fiber market size is forecast to increase by USD 173.9 million, at a CAGR of 12.87% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing global Internet traffic and rising investments in ultra-long-haul networks. These trends reflect the evolving digital landscape, where high-speed connectivity is essential for businesses and consumers alike. However, the market faces challenges, including the high initial investments and leasing costs of dark fiber. This presents both opportunities and obstacles for market participants. On one hand, the growing demand for bandwidth and the increasing adoption of cloud services create potential for revenue growth. On the other hand, the high upfront costs may deter some potential entrants and limit market penetration. Companies seeking to capitalize on this market's dynamics must navigate these challenges effectively, focusing on cost optimization, strategic partnerships, and innovative business models. In doing so, they can seize opportunities in the expanding dark fiber market and position themselves as key players in Malaysia's digital infrastructure landscape.

What will be the size of the Malaysia Dark Fiber Market during the forecast period?

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In the dynamic Malaysian dark fiber market, network availability and infrastructure investment play crucial roles in driving market growth. Wavelength routing and right-of-way acquisition are key strategies for service providers looking to expand capacity. With the increasing demand for high-speed connectivity, 10 gigabit ethernet and 100 gigabit ethernet are becoming the new norm. Mesh networks and fault tolerance ensure uninterrupted service delivery, while energy consumption and network topology are essential considerations for cost optimization. Capacity expansion is a continuous process, with network upgrades including protection switching, optical isolators, and optical circulators. Outage management and ring networks ensure minimal downtime, while point-to-point links and Raman amplification enable long-haul connectivity. As the market evolves, service providers must balance the need for network upgrades with the importance of energy efficiency and cost control.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. ServiceLong-haul servicesShort-haul servicesColocation facilities servicesTypeMulti-modeSingle-modeGeographyAPACMalaysia

By Service Insights

The long-haul services segment is estimated to witness significant growth during the forecast period.

Long-haul dark fiber services involve the transmission of light signals through optical fiber cables over long distances with minimal repeater usage. These networks transport vast quantities of data between cities and across coasts, spanning hundreds of miles. An illustrative instance of long-haul services is submarine optical fiber cables, which are laid beneath the seabed and connect land-based stations. Dark fiber offers several advantages for long-haul services: 1. Exceptional bandwidth: Dark optical fiber cables can accommodate bandwidths of up to 100 gigabytes per second, significantly surpassing copper wiring's capabilities. 2. Increased transmission distance: Dark fiber enables signals to be transmitted over longer distances due to its minimal power loss. 3. Enhanced network security: Dark fiber provides greater security as the fiber strand is exclusively leased to the client, eliminating the risk of data breaches from sharing the network with other users. 4. Improved capacity planning: Dark fiber allows for customized capacity planning as clients can lease only the required fiber strands, enabling them to scale their network according to their needs. 5. Superior network infrastructure: Dark fiber networks boast advanced infrastructure, including optical amplifiers, testing and measurement tools, and network management systems, ensuring optimal network performance. 6. Flexibility for high-bandwidth applications: Dark fiber services cater to high-bandwidth applications, such as cloud computing, financial institutions, healthcare providers, and data centers, ensuring seamless data transfer and processing. 7. Cost-effective: Dark fiber leasing is a cost-effective solution for businesses requiring large bandwidth and long-term network connectivity. Dark fiber's advantages make it an increasingly popular choice for long-haul services, including submarine cable systems, network infrastructure, and enterprise networks. The use of a

The Global River Topology (GRIT) is a vector-based, global river network that not only represents the tributary components of the global drainage network but also the distributary ones, including multi-thread rivers, canals and delta distributaries. It is also the first global hydrography (excl. Antarctica and Greenland) produced at 30m raster resolution. It is created by merging Landsat-based river mask (GRWL) with elevation-generated streams to ensure a homogeneous drainage density outside of the river mask (rivers narrower than approx. 30m). Crucially, it uses a new 30m digital terrain model (FABDEM, based on TanDEM-X) that shows greater accuracy over the traditionally used SRTM derivatives. After vectorisation and pruning, directionality is assigned by a combination of elevation, flow angle, heuristic and continuity approaches (based on RivGraph). The network topology (lines and nodes, upstream/downstream IDs) is available as layers and attribute information in the GeoPackage files (readable by QGIS/ArcMap/GDAL).

A map of GRIT segments labelled with OSM river names is available here: https://michelwortmann.com/research/gritv05-segments-river-names/

Regions

Vector files are provided in 7 regions with the following codes:

• AF - Africa
• AS - Asia (excl. Siberia)
• EU - Europe
• NA - North America
• SA - South America
• SI - Siberia
• SP - South Pacific/Australia

The domain polygons (GRITv04_domain_GLOBAL.gpkg.zip) provide 60 subcontinental catchment groups that are available as vector attributes. They allow for more fine-grained subsetting of data (e.g. with ogr2ogr --where and the domain attribute).

Vector files are provided both in the original equal-area Equal Earth Greenwich projection (EPSG:8857) as well as in geographic WGS84 coordinates (EPSG:4326).

Network segments

Lines between inlet, outlet, confluence and bifurcation nodes. Files have lines and nodes layers.

Attribute description of lines layer

Name	Data type	Description
cat	integer	domain internal feature ID
global_id	integer	global river segment ID, same as FID
catchment_id	integer	global catchment ID
upstream_node_id	integer	global segment node ID at upstream end of line
downstream_node_id	integer	global segment node ID at downstream end of line
upstream_line_ids	text	comma-separated list of global river segment IDs connecting at upstream end of line
downstream_line_ids	text	comma-separated list of global river segment IDs connecting at downstream end of line
direction_algorithm	float	code of RivGraph method used to set the direction of line
width_adjusted	float	median river width in m without accounting for width of segments connecting upstream/downstream
length_adjusted	float	segment length in m without accounting for width of segments connecting upstream/downstream in m
is_mainstem	integer	1 if widest segment of bifurcated flow or no bifurcation upstream, otherwise 0
strahler_order	integer	Strahler order of segment, can be used to route in topological order
length	float	segment length in m
azimuth	float	direction of line connecting upstream-downstream nodes in degrees from North
sinuousity	float	ratio of Euclidean distance between upstream-downstream nodes and line length, i.e. 1 meaning a perfectly straight line
drainage_area_in	float	drainage area at beginning of segment, partitioned by width at bifurcations, in km2
drainage_area_out	float	drainage area at end of segment, partitioned by width at bifurcations, in km2
drainage_area_mainstem_in	float	drainage area at beginning of segment, following the mainstem, in km2
drainage_area_mainstem_out	float	drainage area at end of segment, following the mainstem, in km2
bifurcation_balance_out	float	(drainage_area_out - drainage_area_mainstem_out) / max(drainage_area_out, drainage_area_mainstem_out), dimensionless ratio
grwl_overlap	float	fraction of the segment overlapping with the GRWL river mask
grwl_value	integer	dominant GRWL value of segment
name	text	river name from Openstreetmap where available, English preferred
name_local	text	river name from Openstreetmap where available, local name
n_bifurcations_upstream	integer	number of bifurcations upstream of segment
domain	text	catchment group ID, see domain index file

Attribute description of nodes layer

Name	Data type	Description
cat	integer	domain internal feature ID
global_id	integer	global river node ID, same as FID
catchment_id	integer	global catchment ID
upstream_line_ids	text	comma-separated list of global river segment IDs flowing into node
downstream_line_ids	text	comma-separated list of global river segment IDs flowing out of node
node_type	text	description of node, one of bifurcation, confluence, inlet, coastal_outlet, sink_outlet, grwl_change
grwl_value	integer	GRWL code at node
grwl_transition	text	GRWL codes of change at grwl_change nodes
cycle	integer	>0 if segment is part of an unresolved cycle, 0 otherwise
continuity_violated	integer	1 if flow continuity is violated, otherwise 0
drainage_area	float	drainage area, partitioned by width at bifurcations, in km2
drainage_area_mainstem	float	drainage area, following the mainstem, in km2
n_bifurcations_upstream	integer	number of bifurcations upstream of node
domain	text	catchment group, see domain index file

Network reaches

Segment lines split to not exceed 1km in length, i.e. these lines will be shorter than 1km and longer than 500m unless the segment is shorter. A simplified version with no vertices between nodes is also provided. Files have lines and nodes layers.

Attribute description of lines layer

Name	Data type	Description
cat	integer	domain internal feature ID
segment_id	integer	global segment ID of reach
global_id	integer	global river reach ID, same as FID
catchment_id	integer	global catchment ID
upstream_node_id	integer	global reach node ID at upstream end of line
downstream_node_id	integer	global reach node ID at downstream end of line
upstream_line_ids	text	comma-separated list of global river reach IDs connecting at upstream end of line
downstream_line_ids	text	comma-separated list of global river reach IDs connecting at downstream end of line
grwl_overlap	float	fraction of the reach overlapping with the GRWL river mask
grwl_value	integer	dominant GRWL value of node
grwl_width_median	float	median width of the GRWL river mask, meters
grwl_width_std	float	standard deviation of width of the GRWL river mask, meters
length	float	length of reach in meters
sinuousity	float	ratio of eucledian distance betwen upstream-downstream nodes and line length, i.e. 1 meaning a perfectly straight line
azimuth	float	direction of line connecting upstream-downstream nodes in degrees from North
domain	text	catchment group, see domain index file

Attribute description of nodes layer

Name	Data type	Description
cat	integer	domain internal feature ID
segment_node_id	integer	global ID of segment node at segment intersections, otherwise blank
n_segments	integer	number of segments attached to node
global_id	integer	global river reach node ID,

This dataset is the frequency histogram of latency components in Matter message exchanges when turning on or off a lightbulb: a) over Thread, b) over Wi-Fi. The physical action of turning on an LED requires more time than turning off an LED, thus the greater Lightbulb Response time for the turn-on command. Experimental results were obtained in an indoor, home environment by using lighting application software over Matter. 100 lightbulb turn on and off actions were triggered from a remote switch in a 2-hop network topology. This test has been reproduced in two different testbeds, using Matter over Thread and Matter over Wi-Fi.

Network Management Software Market size was valued at USD 10485.32 Million in 2023 and is projected to reach USD 20008.56 Million by 2031, growing at a CAGR of 9.28% during the forecast period 2024-2031.

Network Management Software Market: Definition/ Overview

Network Management Software(NMS) serves as the central nervous system for managing and controlling the complex web of devices, applications, and connections that make up a contemporary network. It provides network managers with a comprehensive toolkit for monitoring network health, optimizing performance, and troubleshooting problems rapidly. NMS accomplishes this by using several protocols, such as SNMP (Simple Network Management Protocol), to collect real-time data from network devices such as routers, switches, and firewalls. This data includes a variety of parameters, such as device availability, bandwidth use, and error rates.

Once collected, NMS displays this data in a user-friendly and unified dashboard, allowing administrators to acquire a comprehensive understanding of their network's overall health. Administrators can detect the exact location of potential faults within the network infrastructure using NMS functions such as network mapping and topology visualization. Furthermore, NMS provides a powerful warning system that alerts administrators to crucial events such as outages, performance bottlenecks, and security concerns. This real-time notification enables preemptive troubleshooting while reducing downtime.

NMS goes beyond monitoring by providing advanced capabilities that improve network performance and automate mundane operations. Administrators can use NMS to remotely configure network devices, automate network configuration changes, and set traffic-shaping policies to prioritize key applications. This automation improves network management operations, minimizes human error, and frees up administrators' time for more strategic activities. Network Management Software enables enterprises to maintain a strong and efficient network by providing a centralized platform for monitoring, troubleshooting, and optimization, resulting in smooth operation and an optimal user experience.

This data enables you to perform offline IP address to Autonomous System Number lookups for any point in time.

The data is based on information provided by Route-Views.

The best way to use this dataset is throught the pyasn python package developed specifically for this purpose (https://pypi.python.org/pypi/pyasn).

Developmental dyslexia may involve deficits in functional connectivity across widespread brain networks that enable fluent reading. We investigated the large-scale organization of electroencephalography (EEG) functional networks at rest in 28 dyslexics and 36 typically reading adults. For each frequency band (delta, theta alpha and beta), we assessed functional connectivity strength with the phase lag index (PLI). Network topology was examined using minimum spanning tree (MST) graphs derived from the functional connectivity matrices. We found significant group differences in the alpha band (8–13 Hz). The graph analysis indicated more interconnected nodes, in dyslexics compared to typical readers. The graph metrics were significantly correlated with age in dyslexics but not in typical readers, which may indicate more heterogeneity in maturation of brain networks in dyslexics. The present findings support the involvement of alpha oscillations in higher cognition and the sensitivity of graph metrics to characterize functional networks in adult dyslexia. Finally, the current results extend our previous findings on children.

The CICIoT2023 dataset is a large-scale, realistic intrusion detection dataset designed to support security analytics and machine learning research in the Internet of Things (IoT) domain. Created by the Canadian Institute for Cybersecurity (CIC), the dataset captures 33 different types of attacks (including DDoS, DoS, Recon, Web-based, Brute Force, Spoofing, and Mirai) executed by malicious IoT devices against other IoT targets.

The testbed consists of 105 real IoT devices of different types and manufacturers, including smart home devices and industrial equipment, configured in a complex network topology to emulate real-world conditions. The dataset includes benign and malicious traffic in various formats and supports feature extraction for both traditional ML and deep learning models.

This dataset aims to address the lack of diversity and scale in previous IoT security datasets, offering a robust benchmark for evaluating intrusion detection systems (IDS) and enabling research in IoT cybersecurity, anomaly detection, and network forensics.

Source https://www.mdpi.com/1424-8220/23/13/5941