This dataset presents the IoT network traffic generated by connected objects. In order to understand and characterise the legitimate behaviour of network traffic, a platform is created to generate IoT traffic under realistic conditions. This platform contains different IoT devices: voice assistants, smart cameras, connected printers, connected light bulbs, motion sensors, etc. Then, a set of interactions with these objects is performed to allow the generation of real traffic. This data is used to identify anomalies and intrusions using machine learning algorithms and to improve existing detection models. Our dataset is available in two formats: pcap and csv and was created as part of the EU CEF VARIoT project https://variot.eu. To download the data in pcap format and for more information, our database is available on this web portal : https://www.variot.telecom-sudparis.eu/.

IoT-SQL Dataset: A Benchmark for Text-to-SQL and IoT Threat Classification

Overview

This dataset accompanies the paper:

"Beyond Text-to-SQL for IoT Defense: A Comprehensive Framework for Querying and Classifying IoT Threats"

Published in TrustNLP: Fifth Workshop on Trustworthy Natural Language Processing, colocated with NAACL 2025.

This dataset is designed to facilitate research in:

• Text-to-SQL: Generating SQL queries from natural language.
• IoT Network Traffic Analysis: Detecting malicious activity in IoT environments.
• Multimodal Learning: Combining structured database queries with network security classification.

Dataset Contents

The dataset consists of three main components:

1. IoT Database (iot_database.sql.gz)

• SQL schema and data from IoT-23 logs and Smart Building Sensor datasets to be put into a database.

2. Text-to-SQL Data (text-to-SQL-data.zip)

• Includes queries with joins, aggregations, temporal conditions, and nested clauses.
• Data split into training (6,591), validation (2,197), and test (2,197) sets.

3. Network Traffic Data (network_traffic_data.zip)

• Each record labeled as benign or malicious.
• Features include timestamps, IPs, ports, protocols, byte counts, and connection history.
• Malicious traffic includes DDoS, C&C, and botnet-related activity.

Usage Instructions

Setting Up the Database

1. Extract the database file:

   gunzip iot_database.sql.gz
   

2. Import into MySQL:

   mysql -u 

3. Verify the schema:

   SHOW TABLES;
   

Citation

If you use this dataset, please cite:

@inproceedings{pavlich2025beyond,
 author = {Ryan Pavlich and Nima Ebadi and Richard Tarbell and Billy Linares and Adrian Tan and Rachael Humphreys and Jayanta Kumar Das and Rambod Ghandiparsi and Hannah Haley and Jerris George and Rocky Slavin and Kim-Kwang Raymond Choo and Glenn Dietrich and Anthony Rios},
 title = {Beyond Text-to-SQL for IoT Defense: A Comprehensive Framework for Querying and Classifying IoT Threats},
 booktitle = {TrustNLP: Fifth Workshop on Trustworthy Natural Language Processing},
 year = {2025},
 organization = {NAACL}
}

Contact

For questions or collaborations, contact Anthony Rios at Anthony.Rios@utsa.edu.

The global Embedded Database Management Systems (eDBMS) market is experiencing robust growth, driven by the increasing adoption of IoT devices, the expansion of edge computing, and the need for real-time data processing in various industries. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant growth is fueled by several key trends: the rising demand for data analytics within resource-constrained environments, the proliferation of connected devices requiring localized data management, and the increasing importance of data security and privacy in embedded systems. Major sectors like healthcare (particularly in medical devices and remote patient monitoring), manufacturing (for industrial automation and predictive maintenance), and automotive (for advanced driver-assistance systems and in-vehicle infotainment) are major contributors to this growth. While challenges remain, such as the complexities of data integration and the need for robust security measures in embedded systems, the overall market outlook remains positive, with substantial opportunities for innovation and expansion. The market segmentation reveals strong demand across diverse operating systems, with Linux maintaining a dominant share due to its open-source nature and suitability for resource-constrained environments. However, Windows and macOS/iOS also hold significant segments, particularly in niche applications. The application-wise segmentation indicates substantial growth across all listed industries, driven by unique requirements for real-time data processing and localized data management. Leading vendors like Microsoft, IBM, Oracle, and others are actively expanding their eDBMS offerings to cater to this growing demand, fostering competition and driving innovation within the market. Future growth will likely be shaped by advancements in technologies like AI and machine learning applied within embedded systems, leading to more sophisticated and efficient data management solutions.

Market Size and Growth: The global IoT Data Management market was valued at USD 40.11 billion in 2025 and is projected to reach USD 131.26 billion by 2033, exhibiting a CAGR of 15.97%. The growing adoption of IoT devices and sensors, the increasing need for data analysis and insights, and advancements in data management technologies are the primary drivers propelling market growth. Key Trends and Restraints: The market is witnessing significant trends such as the rise of edge computing, AI-powered data analytics, and the adoption of cloud-based solutions. These trends are expected to enhance the efficiency and scalability of IoT data management systems. However, challenges such as data security concerns, privacy regulations, and the lack of interoperability standards may restrain market growth. The market is segmented by solution and services (data storage, analytics, security) and organization size (large enterprises, SMEs). The region with the largest market share is North America, followed by Europe and Asia Pacific. Recent developments include: December 2022: An Internet of Things solutions supplier, Aeris, signed an agreement with Ericsson in order to obtain the Connected Vehicle Cloud business and IoT Accelerator of Ericsson. Both companies together will offer software for IoT connection for several enterprises in 190 countries and more than 100 million IoT devices globally., February 2018: Xively from LogMeIn was acquired by Google for USD 50 million, providing Google Cloud with an established IoT platform to add to its product portfolio as it expects to utilize this as a springboard in the growing IoT market., August 2019: A pioneer in hybrid data management, data integration technology, and cloud data warehouse, Actian announced the launch of a novel Actian ZenTM integrated database for both the IoT and mobiles.. Key drivers for this market are: Driver Impact Analysis. Potential restraints include: Restraint Impact Analysis.

To explore the application effect of the deep learning (DL) network model in the Internet of Things (IoT) database query and optimization. This study first analyzes the architecture of IoT database queries, then explores the DL network model, and finally optimizes the DL network model through optimization strategies. The advantages of the optimized model in this study are verified through experiments. Experimental results show that the optimized model has higher efficiency than other models in the model training and parameter optimization stages. Especially when the data volume is 2000, the model training time and parameter optimization time of the optimized model are remarkably lower than that of the traditional model. In terms of resource consumption, the Central Processing Unit and Graphics Processing Unit usage and memory usage of all models have increased as the data volume rises. However, the optimized model exhibits better performance on energy consumption. In throughput analysis, the optimized model can maintain high transaction numbers and data volumes per second when handling large data requests, especially at 4000 data volumes, and its peak time processing capacity exceeds that of other models. Regarding latency, although the latency of all models increases with data volume, the optimized model performs better in database query response time and data processing latency. The results of this study not only reveal the optimized model’s superior performance in processing IoT database queries and their optimization but also provide a valuable reference for IoT data processing and DL model optimization. These findings help to promote the application of DL technology in the IoT field, especially in the need to deal with large-scale data and require efficient processing scenarios, and offer a vital reference for the research and practice in related fields.

This database compiles the collection of research topics identified throughout various Strategic Research and Innovation Agendas published by Industry Associations relevant to the IoT ecosystem between 2020 and 2023. The present data along with the authors' classification was used to produce the STRATEGIC TOPICS AND THEMES RELATED TO THE NGIOT section within Deliverable 2.6 NGIoT Roadmap and Policy Recommendations of CSA Project: EU-IoT - The European IoT HUb - Growing a sustainable and comprehensive ecosystem for Next Generation Internet of Things.

In performing the meta-analysis the following actions were taken to realise the comparison and data collection across the SRIAs and roadmaps:

Within the scope of the identified target communities for the EU-IoT project and NGIoT Initiative7, the latest publications, roadmaps and SRIAs were revised and reviewed. Selected SRIAs to be included met the following criteria:

Relevance to the scope of the NGIoT and latterly the Cloud Edge IoT Continuum. o Levelofdetailandstructuredrepresentation.

Specificity and action ability of thetopic sprovided.

From the selected agendas, individual topics were abstracted and categorised under the following fields to provide a comparable analysis and assessment:

Type

Priority area: considered to be topics of strategic importance, encompassing multiple technologies and applications. E.g., Constraint- based planning and decision making in complex natural environments.

Application: specific implementations of technologies either within a given context or addressing a defined goal. E.g., Data streaming in constraint environments.

Technology: a variety of different technical, electronic, or physical systems, assets, devices or algorithms. E.g., Self-configuring and adaptive sensor nodes.

Theme: definition of the common priority theme taking a bottom-up approach and aligned with the NGIoT technologies.

Position within the EU-IoT framework as described in the previous section:

Layer: Tech, Market, Policy & Standards, Skills, All.

Context: Human Interface, Far Edge, Near Edge, Infrastructure, Data Spaces, All.

Finally, the analysis identified the key trends and themes across the contributing communities and NGIoT framework.

In total 645 topics were abstracted, categorised and analysed across two cycles. The resulting database is provided as a public output for further analysis and reuse by the community and construction of future trend mapping. Within this paper, the latest versions of identified agendas were included in the analysis totalling 590 topics.

This work presents a threat modelling approach to represent changes to the attack paths through an Internet of Things (IoT) environment when the environment changes dynamically, that is, when new devices are added or removed from the system or when whole sub-systems join or leave. The proposed approach investigates the propagation of threats using attack graphs, a popular attack modelling method. However, traditional attack-graph approaches have been applied in static environments that do not continuously change, such as enterprise networks, leading to static and usually very large attack graphs. In contrast, IoT environments are often characterised by dynamic change and interconnections; different topologies for different systems may interconnect with each other dynamically and outside the operator’s control. Such new interconnections lead to changes in the reachability amongst devices according to which their corresponding attack graphs change. This requires dynamic topology and attack graphs for threat and risk analysis. This article introduces an example scenario based on healthcare systems to motivate the work and illustrate the proposed approach. The proposed approach is implemented using a graph database management tool (GDBM), Neo4j, which is a popular tool for mapping, visualising, and querying the graphs of highly connected data. It is efficient in providing a rapid threat modelling mechanism, making it suitable for capturing security changes in the dynamic IoT environment. Our results show that our developed threat modelling approach copes with dynamic system changes that may occur in IoT environments and enables identifying attack paths, whilst allowing for system dynamics. The developed dynamic topology and attack graphs can cope with the changes in the IoT environment efficiently and rapidly by maintaining their associated graphs.

Message Queuing Telemetry Transport (MQTT) protocol is one of the most used standards used in Internet of Things (IoT) machine to machine communication. The increase in the number of available IoT devices and used protocols reinforce the need for new and robust Intrusion Detection Systems (IDS). However, building IoT IDS requires the availability of datasets to process, train and evaluate these models.

MQTT-IoT-IDS2020 is the first dataset to simulate an MQTT-based network. The dataset is generated using a simulated MQTT network architecture. The network comprises twelve sensors, a broker, a simulated camera, and an attacker. Five scenarios are recorded: (1) normal operation, (2) aggressive scan, (3) UDP scan, (4) Sparta SSH brute-force, and (5) MQTT brute-force attack. The raw pcap files are saved, then features are extracted. Three abstraction levels of features are extracted from the raw pcap files: (a) packet features, (b) Unidirectional flow features and (c) Bidirectional flow features. The csv feature files in the dataset are suited for Machine Learning (ML) usage. Also, the raw pcap files are suitable for the deeper analysis of MQTT IoT networks communication and the associated attacks.

This is the data set of IoT data from the Fischertechnik Smart Factory Model deployed at the Institute of Computer Science at the University of St.Gallen. It is used as basis for the interactive identification of process activity executions from the IoT data. The corresponding publication can be found here:

Seiger, R., Franceschetti, M., & Weber, B. (2023). An Interactive Method for Detection of Process Activity Executions from IoT Data. Future Internet, 15(2), 77. https://doi.org/10.3390/fi15020077

The data set contains:

cps_log.txt: A file of all sensor and actuator readings (in JSON format) from the smart factory during the execution of 3 instances of the storage process and 3 instances of the production process. For visualization, it can be fed line-by-line into an Influx database and Grafana can then be used to create visualizations of the data.

wfms_log.txt: A file containing the corresponding event log (in JSON format) recorded and extracted from the Camunda Platform workflow management system during the execution of the process instances. For visualization, it can be fed line-by-line into an Influx database and Grafana can then be used to create visualizations of the data.

storage_process.bpmn: Executable BPMN 2.0 model of the storage process executed in the smart factory model.

production_process.bpmn: Executable BPMN 2.0 model of the storage process executed in the smart factory model.

More details on the systems architecture used to execute the processes and record the data from the smart factory can be found in the follow publication:

Ronny Seiger, Lukas Malburg, Barbara Weber, Ralph Bergmann, Integrating process management and event processing in smart factories: A systems architecture and use cases, Journal of Manufacturing Systems, Volume 63, 2022, Pages 575-592, ISSN 0278-6125, https://doi.org/10.1016/j.jmsy.2022.05.012

The global Open Source Time Series Database (TSDB) market size was valued at USD 447.17 million in 2025 and is projected to reach USD 1,922.95 million by 2033, growing at a CAGR of 19.9% from 2025 to 2033. The growing adoption of IoT devices, the increasing need for real-time data analysis, and the rise of the Industrial Internet of Things (IIoT) are driving the growth of the Open Source TSDB market. Cloud-based TSDBs are expected to witness the fastest growth during the forecast period due to their scalability, cost-effectiveness, and ease of use. IoT industry is the largest application segment, and the financial industry is expected to witness the fastest growth during the forecast period. North America held the largest market share in 2025, and Asia Pacific is expected to register the highest CAGR during the forecast period. The key players in the Open Source TSDB market include InfluxData, Timescale, Prometheus, OpenTSDB, VictoriaMetrics, and QuestDB.

The relational database management system (RDBMS) market is poised to grow at a substantial CAGR of 6.62% over the forecast period from 2025 to 2033. This growth can be attributed to the increasing adoption of cloud-based RDBMS, the growing volume of data generated by businesses, and the need for efficient data management solutions. Additionally, the adoption of RDBMS in emerging sectors such as healthcare, retail, and telecommunications is expected to drive market growth. Key market drivers include the growing demand for data analytics, the need for improved data management, and the increasing adoption of cloud computing. However, the market growth may be hindered by the high cost of implementation and maintenance of RDBMS and the lack of skilled professionals. Some of the key players in the RDBMS market include IBM, SAP SE, Oracle, Amazon, and MariaDB Corporation. The market is expected to be dominated by North America and Europe, owing to the presence of a large number of established vendors and early adoption of RDBMS in these regions. The global relational database management system (RDBMS) market is projected to reach USD 128.02 billion by 2028, exhibiting a CAGR of 5.0% during the forecast period. The increasing adoption of cloud-based RDBMS solutions, growing demand for data analytics, and stringent data privacy regulations are key factors driving market growth. Key drivers for this market are: Cloud-based RDBMS adoption, Integration with AI technologies; Enhanced data security solutions; Growth in IoT applications; Demand for real-time analytics. Potential restraints include: Cloud adoption increase, Data security concerns; Rising big data analytics; Growing IoT integration; Demand for automation tools.

The size and share of the market is categorized based on Application (Web Development, Enterprise Applications, Big Data Analytics, IoT, Mobile Apps) and Product (Relational Database Management Systems (RDBMS), NoSQL Databases, NewSQL Databases, Graph Databases, Time-Series Databases) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

The IoT Solutions and Services Market is projected to grow at 18.4% CAGR, reaching $683.31 Billion by 2029. Where is the industry heading next? Get the sample report now!

Two datasets:

1. the database of standards relevant to the Cloud-Edge-IoT continuum and to the ACES-EDGE Research and Innovation Action funded under the grant agreement No. 101093126 call HORIZON-CL4-2022-DATA-01-02.
2. the Excel workbook with the analysis of the assessment of the standards in relation to the needs of the ACES-EDGE implementation.

Both datasets will be used for a more deep assessment of the standardisation requirements of the ongoing technolgical developments.

The NoSQL database software market is anticipated to reach USD 3101.3 million by 2033, advancing at a CAGR of 6.5% from 2025 to 2033. This market's expansion is primarily attributed to the increasing need for flexible and scalable data management solutions in various industries. The growing adoption of cloud computing, the increasing volume of data generated by IoT devices, and the need for real-time data analytics are some of the key factors driving the market. Additionally, the rising popularity of NoSQL databases among large enterprises and SMEs for handling unstructured and semi-structured data is fueling the market growth. MongoDB, Amazon, ArangoDB, Azure Cosmos DB, and Couchbase are some of the prominent players in the NoSQL database software market.

The Online Transaction Processing (OLTP) market is experiencing robust growth, driven by the increasing adoption of cloud-based solutions, the expanding digital economy, and the imperative for real-time data processing across diverse sectors. The market's expansion is fueled by the need for high-performance databases capable of handling massive transaction volumes, particularly within sectors like Smart Government, Information Security, and Digital Industrialization. The preference for agile and scalable NoSQL databases is growing, challenging the traditional dominance of Relational Database Management Systems (RDBMS). However, the legacy systems still hold a significant market share, particularly in established industries, leading to a dynamic market landscape with both established players and innovative newcomers vying for dominance. We estimate the 2025 market size at $150 billion, based on observable market trends and growth patterns within adjacent technology sectors. A compound annual growth rate (CAGR) of 12% is projected through 2033, indicating a substantial increase in market value and influence over the next decade. This growth is further segmented by database type (RDBMS and NoSQL), application (Smart Government, Information Security, etc.), and geographic region. The restraints on market growth primarily stem from concerns regarding data security and compliance, the complexities of data migration, and the high initial investment required for implementing advanced OLTP solutions. Despite these challenges, the overall trend demonstrates significant potential. The increasing reliance on real-time data analytics, coupled with the rising adoption of Internet of Things (IoT) technologies, will further accelerate the demand for robust and scalable OLTP systems. This necessitates a focus on developing advanced security measures, streamlined integration processes, and cost-effective cloud-based solutions to overcome existing limitations and unlock the full potential of the OLTP market. North America currently holds a leading market share due to high technological adoption and established digital infrastructure, but Asia Pacific is expected to witness significant growth in the coming years due to rapid digitalization efforts in major economies like India and China.

According to Cognitive Market Research, the global cloud database and DBaaS marketsize will be USD 21.9 billion in 2024 and will increase at a compound annual growth rate (CAGR) of 21.6% from 2024 to 2031. Market Dynamics of Cloud Database and DBaaS Market Key Drivers for Cloud Database and DBaaS Market Mobile and IoT Adoption - The rise of mobile and IoT technologies fuels demand for cloud databases and DBaaS solutions. Data generation surges as mobile usage skyrockets and IoT devices flourish, necessitating scalable, accessible storage options. Cloud databases offer flexibility and scalability to accommodate these dynamic workloads while enabling seamless integration with mobile and IoT applications. The shift towards digital transformation initiatives also amplifies the need for agile, cloud-native database solutions to support modernization efforts across industries. Automated administration reduces operational complexity, which drives the cloud database and DBaaS market's expansion in the years ahead. Key Restraints for Cloud Database and DBaaS Market Compatibility issues with existing systems hinder the adoption of the cloud database and DBaaS in the industry. The market also faces significant difficulties related to data migration challenges that hinder adoption and scalability.. Introduction of the Cloud Database and DBaaS Market Cloud databases and Database-as-a-Service (DBaaS) offer scalable and managed storage solutions where data is hosted and accessed over the internet. Market drivers for these services include the imperative for scalability to accommodate growing data volumes, cost efficiencies achieved through a shift from capital to operational expenditure, enhanced accessibility enabling collaboration and innovation from any location, heightened demand for robust security features to address data privacy concerns, simplified management through automated administration, and elasticity to handle fluctuating workloads seamlessly. These drivers collectively address modern business needs for flexibility, cost-effectiveness, security, and performance. As organizations increasingly depend on data as a strategic asset, cloud databases, and DBaaS solutions provide the agility and efficiency required to meet evolving demands while leveraging the benefits of cloud computing infrastructure.

The global Database as a Service (DaaS) market is experiencing robust growth, driven by the increasing adoption of cloud computing, the need for scalable and cost-effective data management solutions, and the rising demand for big data analytics. The market is characterized by a high concentration of major players, including Amazon Web Services, IBM, Microsoft, and Oracle, who are constantly innovating and expanding their DaaS offerings. The market's growth is further fueled by the proliferation of mobile and IoT devices generating massive amounts of data that need efficient storage and processing. Businesses across diverse sectors are migrating their on-premise databases to the cloud to benefit from enhanced security, improved performance, and reduced infrastructure costs. While the initial investment might be a barrier to entry for some smaller businesses, the long-term cost savings and scalability advantages are significant drivers of market expansion. Segments such as cloud-based databases (Type) and their applications in diverse sectors like finance, healthcare, and e-commerce are witnessing particularly strong growth. Regional variations exist, with North America and Europe currently holding significant market shares, driven by early adoption of cloud technologies and a robust IT infrastructure. However, the Asia-Pacific region is expected to experience rapid growth in the coming years, fueled by increasing digitalization and expanding internet penetration. The forecast period (2025-2033) anticipates continued expansion of the DaaS market. While competitive intensity amongst major players will remain high, opportunities abound for niche players focusing on specific industry solutions or offering specialized DaaS services. Furthermore, the emergence of new technologies, such as serverless databases and AI-powered database management tools, is expected to further drive innovation and market expansion. Challenges such as data security concerns and regulatory compliance requirements will need to be addressed to ensure continued growth and maintain consumer trust. We can expect to see further consolidation in the market as companies seek to expand their capabilities and market reach. The overall trend points towards an increasingly sophisticated and competitive DaaS landscape that caters to a diverse range of user needs and technological advancements.

SolutionsDevice Management: Platforms for managing, monitoring, and updating IoT devices.Analytics Platforms: Tools for collecting, analyzing, and visualizing IoT data.Data Storage and Management: Solutions for storing and organizing IoT data.Connectivity Management: Services for managing IoT device connectivity to networks.Security Solutions: Technologies for protecting IoT systems from cyber threats.ServicesConsulting: Guidance on IoT strategy, architecture, and implementation.Implementation: Assistance in deploying and integrating IoT solutions.Managed Services: Ongoing support and maintenance of IoT systems.Training and Education: Programs to develop skills and knowledge in IoT. Recent developments include: April 2023: Through a collaborative effort with Oracle, the Norwegian Institute of Bioeconomy Research chose the Autonomous Database and AI functionalities on the Oracle Cloud Infrastructure (OCI). The objective was to acquire a more profound understanding of the forest value chain and improve practices that ensure long-term sustainability., April 2023: Google unveiled a novel artificial intelligence (AI) system that empowers the Claims Acceleration Suite to optimize health insurance prior to authorization and claims processing. By implementing the recently created Claims Data Activator, this solution endeavors to mitigate administrative complexities and decrease expenditures for health plans and providers.. Key drivers for this market are: IoT enables real-time monitoring, predictive maintenance, and process automation, significantly improving operational efficiency and reducing costs. Potential restraints include: The interconnected nature of IoT systems increases vulnerability to cyberattacks, deterring adoption in critical applications. Notable trends are: The integration of artificial intelligence with IoT is enabling smarter, more autonomous systems for predictive analytics and decision-making.