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The work involved in developing the dataset and benchmarking its use of machine learning is set out in the article ‘IoMT-TrafficData: Dataset and Tools for Benchmarking Intrusion Detection in Internet of Medical Things’. DOI: 10.1109/ACCESS.2024.3437214.
Please do cite the aforementioned article when using this dataset.
The increasing importance of securing the Internet of Medical Things (IoMT) due to its vulnerabilities to cyber-attacks highlights the need for an effective intrusion detection system (IDS). In this study, our main objective was to develop a Machine Learning Model for the IoMT to enhance the security of medical devices and protect patients’ private data. To address this issue, we built a scenario that utilised the Internet of Things (IoT) and IoMT devices to simulate real-world attacks. We collected and cleaned data, pre-processed it, and provided it into our machine-learning model to detect intrusions in the network. Our results revealed significant improvements in all performance metrics, indicating robustness and reproducibility in real-world scenarios. This research has implications in the context of IoMT and cybersecurity, as it helps mitigate vulnerabilities and lowers the number of breaches occurring with the rapid growth of IoMT devices. The use of machine learning algorithms for intrusion detection systems is essential, and our study provides valuable insights and a road map for future research and the deployment of such systems in live environments. By implementing our findings, we can contribute to a safer and more secure IoMT ecosystem, safeguarding patient privacy and ensuring the integrity of medical data.
The ZIP folder comprises two main components: Captures and Datasets. Within the captures folder, we have included all the captures used in this project. These captures are organized into separate folders corresponding to the type of network analysis: BLE or IP-Based. Similarly, the datasets folder follows a similar organizational approach. It contains datasets categorized by type: BLE, IP-Based Packet, and IP-Based Flows.
To cater to diverse analytical needs, the datasets are provided in two formats: CSV (Comma-Separated Values) and pickle. The CSV format facilitates seamless integration with various data analysis tools, while the pickle format preserves the intricate structures and relationships within the dataset.
This organization enables researchers to easily locate and utilize the specific captures and datasets they require, based on their preferred network analysis type or dataset type. The availability of different formats further enhances the flexibility and usability of the provided data.
Within this dataset, three sub-datasets are available, namely BLE, IP-Based Packet, and IP-Based Flows. Below is a table of the features selected for each dataset and consequently used in the evaluation model within the provided work.
Identified Key Features Within Bluetooth Dataset
| Feature | Meaning |
| btle.advertising_header | BLE Advertising Packet Header |
| btle.advertising_header.ch_sel | BLE Advertising Channel Selection Algorithm |
| btle.advertising_header.length | BLE Advertising Length |
| btle.advertising_header.pdu_type | BLE Advertising PDU Type |
| btle.advertising_header.randomized_rx | BLE Advertising Rx Address |
| btle.advertising_header.randomized_tx | BLE Advertising Tx Address |
| btle.advertising_header.rfu.1 | Reserved For Future 1 |
| btle.advertising_header.rfu.2 | Reserved For Future 2 |
| btle.advertising_header.rfu.3 | Reserved For Future 3 |
| btle.advertising_header.rfu.4 | Reserved For Future 4 |
| btle.control.instant | Instant Value Within a BLE Control Packet |
| btle.crc.incorrect | Incorrect CRC |
| btle.extended_advertising | Advertiser Data Information |
| btle.extended_advertising.did | Advertiser Data Identifier |
| btle.extended_advertising.sid | Advertiser Set Identifier |
| btle.length | BLE Length |
| frame.cap_len | Frame Length Stored Into the Capture File |
| frame.interface_id | Interface ID |
| frame.len | Frame Length Wire |
| nordic_ble.board_id | Board ID |
| nordic_ble.channel | Channel Index |
| nordic_ble.crcok | Indicates if CRC is Correct |
| nordic_ble.flags | Flags |
| nordic_ble.packet_counter | Packet Counter |
| nordic_ble.packet_time | Packet time (start to end) |
| nordic_ble.phy | PHY |
| nordic_ble.protover | Protocol Version |
Identified Key Features Within IP-Based Packets Dataset
| Feature | Meaning |
| http.content_length | Length of content in an HTTP response |
| http.request | HTTP request being made |
| http.response.code | Sequential number of an HTTP response |
| http.response_number | Sequential number of an HTTP response |
| http.time | Time taken for an HTTP transaction |
| tcp.analysis.initial_rtt | Initial round-trip time for TCP connection |
| tcp.connection.fin | TCP connection termination with a FIN flag |
| tcp.connection.syn | TCP connection initiation with SYN flag |
| tcp.connection.synack | TCP connection establishment with SYN-ACK flags |
| tcp.flags.cwr | Congestion Window Reduced flag in TCP |
| tcp.flags.ecn | Explicit Congestion Notification flag in TCP |
| tcp.flags.fin | FIN flag in TCP |
| tcp.flags.ns | Nonce Sum flag in TCP |
| tcp.flags.res | Reserved flags in TCP |
| tcp.flags.syn | SYN flag in TCP |
| tcp.flags.urg | Urgent flag in TCP |
| tcp.urgent_pointer | Pointer to urgent data in TCP |
| ip.frag_offset | Fragment offset in IP packets |
| eth.dst.ig | Ethernet destination is in the internal network group |
| eth.src.ig | Ethernet source is in the internal network group |
| eth.src.lg | Ethernet source is in the local network group |
| eth.src_not_group | Ethernet source is not in any network group |
| arp.isannouncement | Indicates if an ARP message is an announcement |
Identified Key Features Within IP-Based Flows Dataset
| Feature | Meaning |
| proto | Transport layer protocol of the connection |
| service | Identification of an application protocol |
| orig_bytes | Originator payload bytes |
| resp_bytes | Responder payload bytes |
| history | Connection state history |
| orig_pkts | Originator sent packets |
| resp_pkts | Responder sent packets |
| flow_duration | Length of the flow in seconds |
| fwd_pkts_tot | Forward packets total |
| bwd_pkts_tot | Backward packets total |
| fwd_data_pkts_tot | Forward data packets total |
| bwd_data_pkts_tot | Backward data packets total |
| fwd_pkts_per_sec | Forward packets per second |
| bwd_pkts_per_sec | Backward packets per second |
| flow_pkts_per_sec | Flow packets per second |
| fwd_header_size | Forward header bytes |
| bwd_header_size | Backward header bytes |
| fwd_pkts_payload | Forward payload bytes |
| bwd_pkts_payload | Backward payload bytes |
| flow_pkts_payload | Flow payload bytes |
| fwd_iat | Forward inter-arrival time |
| bwd_iat | Backward inter-arrival time |
| flow_iat | Flow inter-arrival time |
| active | Flow active duration |
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Internet of Medical Things (IoMT) Market size is expected to reach US$ 1,211.22 bn by 2034 from US$ 221.84 bn in 2024, at a CAGR of 18.5%.
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The Internet of Medical Things (IoMT) solutions market is experiencing robust growth, projected to reach $8.304 billion in 2025 and demonstrating a remarkable Compound Annual Growth Rate (CAGR) of 23.2%. This expansion is fueled by several key factors. The increasing adoption of telehealth and remote patient monitoring (RPM) technologies is driving demand for connected medical devices and data analytics solutions. Furthermore, the rising prevalence of chronic diseases necessitates continuous health monitoring and personalized care, creating a significant market opportunity for IoMT solutions. Advancements in wearable sensors, cloud computing, and artificial intelligence (AI) are enabling more sophisticated and efficient healthcare delivery, further propelling market growth. Major players like GE Healthcare, Philips, and Medtronic are actively investing in R&D and strategic partnerships to consolidate their market positions and capitalize on this expanding sector. The market is segmented by device type (wearables, implantables, etc.), application (remote patient monitoring, telehealth, etc.), and end-user (hospitals, clinics, home care). While data privacy and security concerns present challenges, the overall market outlook remains positive, driven by ongoing technological innovation and increasing healthcare spending. The forecast period of 2025-2033 anticipates continued exponential growth, driven by the integration of IoMT into various healthcare settings. Improved healthcare infrastructure, particularly in developing economies, will further stimulate adoption. The competitive landscape remains dynamic, with both established medical device manufacturers and technology companies vying for market share. Successful players will need to prioritize data security and interoperability, while simultaneously focusing on user-friendly interfaces and the integration of AI-powered diagnostic tools. The expanding adoption of 5G and other advanced communication technologies will also play a crucial role in improving the scalability and performance of IoMT networks, ultimately contributing to better patient outcomes and healthcare efficiency.
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The Internet of Medical Things Market Report is Segmented by Device Type (Wearable Devices, Stationary/In-Hospital Devices, Implantable Devices, and Other Device Types), Product Type (Vital Signs Monitoring Devices, Implantable Cardiac Devices, and More), End Users (Hospitals, Clinics, and More), Connectivity Technology (Zigbee, Bluetooth, Wi-Fi, and More), and Geography.
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The size of the Internet of Medical Things (IoMT) Market market was valued at USD 181.91 billion in 2023 and is projected to reach USD 667.18 billion by 2032, with an expected CAGR of 20.4 % during the forecast period. The Internet of Medical Things (IoMT) Market refers to the interconnected ecosystem of medical devices and applications that communicate via the internet to enhance healthcare delivery. IoMT includes wearables, remote monitoring devices, and smart sensors that collect and transmit health data to healthcare providers. Key applications encompass chronic disease management, patient monitoring, and medication adherence, facilitating timely interventions and improved patient outcomes. Current trends in the market include the growing emphasis on telehealth solutions, increased adoption of artificial intelligence for data analysis, and a focus on cybersecurity to protect sensitive health information. As healthcare continues to evolve towards personalized and data-driven approaches, the IoMT market is poised for significant growth.
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The Internet of Medical Things (IoMT) solutions market is experiencing robust growth, projected to reach a significant market size driven by several key factors. The market's Compound Annual Growth Rate (CAGR) of 23.2% from 2019 to 2024 indicates a substantial expansion, with a continued upward trajectory anticipated through 2033. This growth is fueled by the increasing adoption of telemedicine, particularly accelerated by recent global events and the rising demand for remote patient monitoring. Furthermore, advancements in connected medical devices, such as implantable sensors and wearable health trackers, contribute significantly to market expansion. The integration of IoMT solutions into clinical workflows, including medication management and inpatient monitoring, enhances efficiency and improves patient outcomes, further driving market demand. Technological advancements in data analytics and artificial intelligence (AI) are also playing a pivotal role, enabling more precise diagnostics and personalized treatment plans. While data security and privacy concerns remain a challenge, the industry is actively addressing these issues through robust security protocols and regulatory compliance measures. The segmentation of the IoMT market into applications (telemedicine, clinical operations, connected imaging, etc.) and types (medical devices, systems and software, services) provides a detailed understanding of market dynamics. North America currently holds a substantial market share due to advanced healthcare infrastructure and high technology adoption rates. However, Asia Pacific is expected to witness the fastest growth in the coming years due to rapid technological advancements and increasing healthcare spending in emerging economies. Major players such as GE Healthcare, Philips, Medtronic, and Cisco are actively investing in R&D and strategic partnerships to strengthen their market positions and capitalize on the growth opportunities within the IoMT landscape. The continuous innovation in sensor technology, cloud computing, and data analytics will further propel the market’s expansion, offering significant potential for both established and emerging companies in the healthcare technology sector.
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The Internet of Medical Things (IoMT) market is experiencing robust growth, driven by the increasing adoption of connected medical devices, the rising prevalence of chronic diseases, and the demand for improved patient care and remote monitoring. The market, estimated at $250 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $800 billion by 2033. Key drivers include advancements in sensor technology, the proliferation of 5G networks enabling faster data transmission, and the increasing integration of artificial intelligence (AI) and machine learning (ML) for data analysis and predictive diagnostics. The integration of IoMT solutions within healthcare systems is streamlining workflows, enhancing operational efficiency, and contributing to better patient outcomes. Major players like Philips, Drägerwerk, and Boston Scientific are actively shaping the market through continuous innovation and strategic partnerships. However, challenges remain, including data security concerns, interoperability issues between different devices and systems, and the need for robust regulatory frameworks to ensure patient privacy and data integrity. The segmentation of the IoMT market reflects its diverse applications, encompassing wearable health trackers, remote patient monitoring systems, connected medical imaging devices, and smart hospital infrastructure. The North American market currently holds a significant share due to advanced healthcare infrastructure and early adoption of new technologies, followed by Europe and Asia-Pacific regions exhibiting strong growth potential. The continued expansion of telehealth services and the increasing emphasis on preventative healthcare will further fuel market growth in the coming years. While cost considerations and the need for skilled professionals to manage and interpret IoMT data pose restraints, the overall trend points towards substantial market expansion, driven by a collective desire to improve healthcare accessibility, affordability, and effectiveness.
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In 2023, the Internet of Medical Things (IoMT) Market reached a value of USD 92.54 billion, and it is projected to surge to USD 419.44 billion by 2030
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According to our latest research, the global Internet of Medical Things (IoMT) market size reached USD 70.8 billion in 2024, reflecting robust adoption across healthcare ecosystems. The market is projected to expand at a CAGR of 18.2% from 2025 to 2033, reaching an estimated USD 323.2 billion by 2033. This impressive growth is primarily driven by the increasing integration of connected medical devices, the rise in telehealth adoption, and a growing demand for real-time patient data to improve clinical outcomes and operational efficiency.
The growth of the IoMT market is fundamentally propelled by the ongoing digital transformation within the healthcare sector. Hospitals, clinics, and care providers are increasingly leveraging IoMT solutions to streamline operations, enhance patient care, and reduce costs. The proliferation of wearable health devices, smart monitoring equipment, and interconnected diagnostic tools is enabling healthcare professionals to capture and analyze patient data in real time, leading to more informed decision-making and timely interventions. The COVID-19 pandemic further accelerated this trend, as healthcare systems worldwide adopted remote monitoring and telemedicine solutions to manage patient loads and minimize infection risks. This shift has set a new standard in healthcare delivery, making IoMT solutions indispensable for healthcare providers seeking to stay competitive and compliant with evolving patient care standards.
Another significant growth factor for the Internet of Medical Things market is the rapid advancement of wireless connectivity technologies and data analytics capabilities. The integration of technologies such as Bluetooth, Wi-Fi, Zigbee, and cellular networks has enabled seamless communication between medical devices and healthcare IT systems. These advancements facilitate the real-time transmission and analysis of patient health data, supporting continuous monitoring and early detection of critical health events. Furthermore, the rise of artificial intelligence and machine learning algorithms within IoMT platforms is enhancing predictive analytics, enabling proactive healthcare management, and reducing hospital readmissions. As healthcare organizations increasingly recognize the value of predictive insights and automation, investment in IoMT infrastructure continues to surge.
A third major driver shaping the IoMT market is the growing emphasis on personalized medicine and patient-centric care. Patients are increasingly empowered to manage their health through connected devices that monitor vital signs, medication adherence, and chronic disease parameters outside traditional clinical settings. This patient-driven approach is fostering the development of innovative IoMT applications tailored to individual health needs, improving patient engagement and outcomes. Additionally, regulatory support for digital health solutions and favorable reimbursement policies in key markets are further accelerating IoMT adoption. As the global population ages and the prevalence of chronic diseases rises, the demand for scalable, cost-effective, and personalized healthcare solutions is expected to continue fueling IoMT market expansion.
From a regional perspective, North America currently dominates the global IoMT market, accounting for the largest revenue share in 2024, followed closely by Europe and the Asia Pacific. The strong presence of leading medical device manufacturers, advanced healthcare infrastructure, and supportive regulatory frameworks are key factors underpinning North America's leadership. Europe benefits from robust investments in digital health and coordinated policy efforts, while the Asia Pacific region is experiencing rapid growth due to increasing healthcare digitization, expanding middle-class populations, and rising government initiatives to modernize healthcare delivery. Latin America and the Middle East & Africa are also witnessing steady adoption, driven by improving healthcare access and growing awareness of connected health technologies.
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The global Internet of Medical Things (IoMT) market is poised for significant expansion, projected to reach an estimated value of $334.08 billion by 2025. This growth is fueled by a Compound Annual Growth Rate (CAGR) of 5%, indicating a steady and robust upward trajectory for the coming years. The IoMT revolutionizes healthcare by connecting medical devices, sensors, and software platforms to the internet, enabling real-time data collection, remote patient monitoring, and improved diagnostic capabilities. This interconnectedness facilitates a more proactive and personalized approach to healthcare delivery, ultimately enhancing patient outcomes and optimizing operational efficiency within healthcare institutions. The increasing prevalence of chronic diseases, the aging global population, and the growing demand for home-based healthcare solutions are major catalysts driving this market's development. Furthermore, advancements in wireless communication technologies and the burgeoning adoption of cloud computing infrastructure are creating a fertile ground for IoMT innovations. The market's dynamism is further characterized by key application segments, with hospitals and clinics being primary adopters due to their need for integrated patient management systems and enhanced medical equipment. The "World Internet of Medical Things Production" segment highlights the global manufacturing landscape. On the device type front, wearable devices are gaining immense traction due to their convenience for continuous health monitoring, while stationary and implantable devices play crucial roles in specialized medical care. Leading players such as GE, Philips, Medtronic, Cisco, and Siemens are at the forefront of this innovation, investing heavily in research and development to introduce cutting-edge IoMT solutions. The market's expansion is observed across all major regions, with North America and Europe currently leading in adoption, while the Asia Pacific region is expected to exhibit the fastest growth due to increasing healthcare investments and a burgeoning patient population. This comprehensive report delves into the dynamic and rapidly evolving landscape of the Internet of Medical Things (IoMT), a transformative force reshaping healthcare delivery and patient outcomes. Spanning a detailed study period from 2019 to 2033, with a base year of 2025 and an estimated year also of 2025, this analysis provides an in-depth examination of the market's trajectory through a rigorous forecast period of 2025-2033, underpinned by robust data from the historical period of 2019-2024. The global IoMT market, projected to witness exponential growth, is meticulously dissected, offering invaluable insights for stakeholders navigating this complex ecosystem. We project the World Internet of Medical Things Production to reach a staggering figure of over 1,000 million units by the end of the forecast period, signifying an unprecedented proliferation of connected medical devices. The report meticulously analyzes the various facets of the IoMT market, including its diverse applications within Hospitals and Clinics, and the groundbreaking advancements in Wearable Devices, Stationary Devices, and Implantable Devices. Furthermore, it scrutinizes the overarching World Internet of Medical Things Production trends and identifies key Industry Developments that are shaping the future of connected healthcare. This report is an indispensable resource for anyone seeking to understand the current state and future potential of IoMT, armed with detailed market intelligence and strategic foresight.
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The Internet of Medical Things (IoMT) market is experiencing robust growth, driven by the increasing adoption of connected medical devices, the rise of telehealth, and the need for improved patient care and remote monitoring. A compound annual growth rate (CAGR) of 23.40% from 2019 to 2024 suggests a significant expansion, and this momentum is expected to continue through 2033. The market's segmentation reveals key opportunities across device types (wearables, stationary, implantable), product types (vital signs monitoring, cardiac devices, respiratory devices, imaging systems), and end-users (hospitals, clinics, home care). The substantial involvement of major players like GE Healthcare, Philips, Medtronic, and Cisco underscores the market's maturity and potential for further innovation. Growth is further fueled by the increasing prevalence of chronic diseases requiring ongoing monitoring and management, the demand for cost-effective healthcare solutions, and advancements in data analytics and artificial intelligence enabling better diagnostic capabilities and personalized treatment. While data privacy and security concerns represent a restraint, the development of robust security protocols and regulatory frameworks is mitigating these risks. Geographic expansion, particularly in developing economies with rising healthcare infrastructure investments, will significantly contribute to future market growth. The substantial market size (let's assume a 2025 market size of $50 Billion based on the provided CAGR and industry trends) highlights the significant economic opportunity within the IoMT sector. The diverse range of applications, from remote patient monitoring to advanced diagnostic imaging, indicates the sector's wide-reaching impact on healthcare delivery. Further analysis of regional data reveals that North America and Europe currently hold significant market share, due to higher adoption rates and technological advancements, however, the Asia-Pacific region presents substantial untapped potential given its rapidly growing healthcare infrastructure and expanding middle class. Future growth will be shaped by factors such as the development of 5G networks, enhancing connectivity and data transfer speeds, and the integration of IoMT devices with electronic health records (EHRs) and other health information systems. The continued focus on interoperability and data standardization will be crucial in unlocking the full potential of IoMT and ensuring seamless data exchange across healthcare providers. Recent developments include: September 2022: Wipro GE Healthcare launches an AI-powered cath lab called Optima IGS320. It improves imaging vision to facilitate intelligence and accurate healthcare delivery. Social gantry movement allows for flexibility during surgeries, further lightening the load and increasing the attention of the doctors., May 2022: Nuance, a division of Microsoft, has joined forces with The Health Management Academy (the Academy) to launch the AI Collaborative, an industry group focused on advancing healthcare through artificial intelligence and machine learning. The AI Collaborative accelerates innovation in precision medicine, drug development, clinical decision support, and other promising use cases throughout the whole healthcare ecosystem while addressing physician fatigue, patient engagement, and the financial viability of the health system., March 2022: Microsoft Corp. announced advancements in cloud technologies for healthcare and life sciences, including the general availability of Azure Health Data Services and updates to Microsoft Cloud for Healthcare. With the recent completion of its acquisition of Nuance Communications, Microsoft is uniquely positioned to amplify an organization's ability to help others by leveraging trusted AI to address the most pressing challenges and transform the future of healthcare for all.. Key drivers for this market are: Increasing Need for Cost Reduction in Medicinal Delivery, Increasing Penetration of Connected Devices; Improving Healthcare Outcomes; Evolution of High-Speed Networking Technologies. Potential restraints include: Increasing Need for Cost Reduction in Medicinal Delivery, Increasing Penetration of Connected Devices; Improving Healthcare Outcomes; Evolution of High-Speed Networking Technologies. Notable trends are: Increasing Penetration of Connected Devices.
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The Internet of Medical Things (IoMT) market is booming, with a projected CAGR of 23.40% and significant growth opportunities in remote patient monitoring, telehealth, and connected medical devices. Learn about key market drivers, restraints, and leading companies shaping this dynamic sector. Key drivers for this market are: Increasing Need for Cost Reduction in Medicinal Delivery, Increasing Penetration of Connected Devices; Improving Healthcare Outcomes; Evolution of High-Speed Networking Technologies. Potential restraints include: Lack of Proper IoT Technology Skills across Healthcare Organizations, High Deployment Cost of Necessary Infrastructure and Connected Medical Devices. Notable trends are: Increasing Penetration of Connected Devices.
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According to our latest research and analysis, the global Internet of Medical Things (IoMT) market size reached USD 78.2 billion in 2024, demonstrating robust momentum with a CAGR of 17.6% anticipated through the forecast period. By 2033, the market is expected to achieve a value of USD 267.4 billion, driven by the proliferation of connected medical devices, advancements in wireless connectivity, and the increasing integration of digital health systems across healthcare infrastructures. The growth trajectory is further fueled by the rising demand for real-time patient monitoring, improved healthcare outcomes, and the ongoing digital transformation in the medical sector.
One of the primary growth factors propelling the IoMT market is the exponential increase in the adoption of connected medical devices for remote patient monitoring and chronic disease management. With the aging global population and a surge in chronic diseases such as diabetes, cardiovascular conditions, and respiratory disorders, healthcare providers are increasingly leveraging IoMT solutions to enable continuous, real-time monitoring. These devices not only facilitate early detection and intervention but also reduce hospital readmissions and healthcare costs. The COVID-19 pandemic further accelerated this trend, highlighting the necessity for remote healthcare delivery and boosting investments in IoMT infrastructure. This shift towards patient-centric care models is expected to sustain the marketÂ’s high growth rate in the coming years.
Connected Medical Devices are at the forefront of this transformation, playing a pivotal role in enhancing patient care and operational efficiency. These devices, which include everything from wearable health monitors to sophisticated imaging systems, are designed to communicate seamlessly with healthcare providers and cloud-based platforms. This connectivity allows for real-time data exchange and analysis, which is crucial for timely medical interventions and personalized treatment plans. As the healthcare landscape continues to evolve, the integration of connected medical devices is expected to become even more prevalent, driving further advancements in patient monitoring and healthcare delivery.
Another significant factor contributing to the market expansion is the continuous innovation in wireless connectivity technologies, such as Bluetooth, Wi-Fi, Zigbee, and cellular networks. These technologies have enabled seamless data transmission between medical devices, healthcare providers, and cloud-based platforms, enhancing interoperability and data analytics capabilities. The integration of artificial intelligence and machine learning with IoMT devices has further improved diagnostic accuracy, personalized treatment, and predictive healthcare analytics. Additionally, regulatory frameworks promoting the adoption of digital health solutions and the growing emphasis on value-based care are encouraging healthcare organizations to invest in IoMT solutions, thereby fostering market growth.
The increasing focus on operational efficiency and workflow optimization within healthcare facilities is another key driver for the IoMT market. Hospitals and clinics are deploying IoMT-enabled solutions for clinical operations, asset tracking, medication management, and connected imaging, which streamline processes and enhance patient outcomes. The digitization of healthcare operations not only improves resource utilization but also minimizes errors and enhances compliance with regulatory standards. Furthermore, the rising trend of homecare and telehealth services, supported by IoMT devices, is expanding the marketÂ’s reach beyond traditional clinical settings. These factors collectively contribute to the sustained growth and evolution of the IoMT ecosystem.
Regionally, North America remains the dominant market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The presence of advanced healthcare infrastructure, high adoption of digital health technologies, and significant investments in research and development are key factors supporting North America's leadership. Europe is also witnessing substantial growth due to favorable government initiatives and increasing awareness of connected healthcare solutions. Meanwhile, the Asia Pacific regio
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The Asia Pacific Internet Of Medical Things Iomt report features an extensive regional analysis, identifying market penetration levels across major geographic areas. It highlights regional growth trends and opportunities, allowing businesses to tailor their market entry strategies and maximize growth in specific regions.
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The Internet of Medical Things (IoMT) Market size was valued at USD 80.56 billion in 2023 and is projected to reach USD 307.70 billion by 2032, exhibiting a CAGR of 21.1 % during the forecasts period.
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Global Internet of Medical Things market size valued $47.12 Bn in 2023 and is expected to reach $597.17 Bn by 2032, with a projected CAGR of 32.6%.
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Explore the rapidly expanding Internet of Medical Things (IoMT) market, driven by telehealth, remote monitoring, and smart hospital innovations. Discover market size, growth drivers, key trends, and regional insights for 2025-2033.
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Global Internet of Medical Things (IoMT) Market is segmented by Application (Healthcare_Pharmaceuticals_Insurance_Consumer Electronics_Telemedicine), Type (Remote Patient Monitoring Devices_Wearable Health Devices_Smart Medical Sensors_Smart Hospital Equipment_Diagnostic Imaging Devices), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)
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About the Dataset This dataset comprises anonymized health records of 60,000 patients, collected from multiple reputed medical institutions as part of a broader Internet of Medical Things (IoMT) data initiative. Each record includes vital physiological parameters essential for monitoring cardiovascular and respiratory health. The dataset is designed to support research and development in healthcare analytics, remote patient monitoring, and IoMT-based diagnostic systems. Key Points Type: Synthetic healthcare monitoring dataset simulating IoMT-based patient data.
Purpose: Designed to mimic real-world vital sign measurements, AI predictions, and alert generation.
Completeness: No missing values; all records are complete and clean.
Format: CSV file with mixed numeric and categorical data types.
Dataset Features Patient Number – Unique identifier for each patient record.
Heart Rate (bpm) – Beats per minute reading.
SpO₂ Level (%)– Blood oxygen saturation percentage.
Systolic Blood Pressure (mmHg)– Systolic blood pressure value.
Diastolic Blood Pressure (mmHg) – Diastolic blood pressure value.
Body Temperature (°C) – Body temperature in Celsius.
Fall Detection– Indicates whether a fall was detected (Yes/No).
Predicted Disease – AI-predicted medical condition.
Data Accuracy (%) – Model’s prediction confidence.
Heart Rate Alert – Status: NORMAL / ABNORMAL.
SpO₂ Level Alert – Status: NORMAL / ABNORMAL.
Blood Pressure Alert – Status: NORMAL / ABNORMAL.
Temperature Alert – Status: NORMAL / ABNORMAL.
Total Records 60,000 records
13 attributes (6 numerical, 7 categorical)
Data Source Origin: Synthetic data generated for research and educational purposes.
Provenance: Simulates readings from IoT-enabled health monitoring devices (e.g., wearable sensors, medical monitors).
Note: Not based on real patients; avoids privacy concerns while preserving realistic patterns.
Application Domain Internet of Medical Things (IoMT) and AI-driven healthcare systems.
Possible uses:
Chronic disease monitoring (e.g., diabetes, hypertension, asthma).
Predictive modeling for early diagnosis.
Alert-based anomaly detection in vitals.
Simulation for IoT and healthcare research.
Testing real-time health monitoring dashboards.
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The work involved in developing the dataset and benchmarking its use of machine learning is set out in the article ‘IoMT-TrafficData: Dataset and Tools for Benchmarking Intrusion Detection in Internet of Medical Things’. DOI: 10.1109/ACCESS.2024.3437214.
Please do cite the aforementioned article when using this dataset.
The increasing importance of securing the Internet of Medical Things (IoMT) due to its vulnerabilities to cyber-attacks highlights the need for an effective intrusion detection system (IDS). In this study, our main objective was to develop a Machine Learning Model for the IoMT to enhance the security of medical devices and protect patients’ private data. To address this issue, we built a scenario that utilised the Internet of Things (IoT) and IoMT devices to simulate real-world attacks. We collected and cleaned data, pre-processed it, and provided it into our machine-learning model to detect intrusions in the network. Our results revealed significant improvements in all performance metrics, indicating robustness and reproducibility in real-world scenarios. This research has implications in the context of IoMT and cybersecurity, as it helps mitigate vulnerabilities and lowers the number of breaches occurring with the rapid growth of IoMT devices. The use of machine learning algorithms for intrusion detection systems is essential, and our study provides valuable insights and a road map for future research and the deployment of such systems in live environments. By implementing our findings, we can contribute to a safer and more secure IoMT ecosystem, safeguarding patient privacy and ensuring the integrity of medical data.
The ZIP folder comprises two main components: Captures and Datasets. Within the captures folder, we have included all the captures used in this project. These captures are organized into separate folders corresponding to the type of network analysis: BLE or IP-Based. Similarly, the datasets folder follows a similar organizational approach. It contains datasets categorized by type: BLE, IP-Based Packet, and IP-Based Flows.
To cater to diverse analytical needs, the datasets are provided in two formats: CSV (Comma-Separated Values) and pickle. The CSV format facilitates seamless integration with various data analysis tools, while the pickle format preserves the intricate structures and relationships within the dataset.
This organization enables researchers to easily locate and utilize the specific captures and datasets they require, based on their preferred network analysis type or dataset type. The availability of different formats further enhances the flexibility and usability of the provided data.
Within this dataset, three sub-datasets are available, namely BLE, IP-Based Packet, and IP-Based Flows. Below is a table of the features selected for each dataset and consequently used in the evaluation model within the provided work.
Identified Key Features Within Bluetooth Dataset
| Feature | Meaning |
| btle.advertising_header | BLE Advertising Packet Header |
| btle.advertising_header.ch_sel | BLE Advertising Channel Selection Algorithm |
| btle.advertising_header.length | BLE Advertising Length |
| btle.advertising_header.pdu_type | BLE Advertising PDU Type |
| btle.advertising_header.randomized_rx | BLE Advertising Rx Address |
| btle.advertising_header.randomized_tx | BLE Advertising Tx Address |
| btle.advertising_header.rfu.1 | Reserved For Future 1 |
| btle.advertising_header.rfu.2 | Reserved For Future 2 |
| btle.advertising_header.rfu.3 | Reserved For Future 3 |
| btle.advertising_header.rfu.4 | Reserved For Future 4 |
| btle.control.instant | Instant Value Within a BLE Control Packet |
| btle.crc.incorrect | Incorrect CRC |
| btle.extended_advertising | Advertiser Data Information |
| btle.extended_advertising.did | Advertiser Data Identifier |
| btle.extended_advertising.sid | Advertiser Set Identifier |
| btle.length | BLE Length |
| frame.cap_len | Frame Length Stored Into the Capture File |
| frame.interface_id | Interface ID |
| frame.len | Frame Length Wire |
| nordic_ble.board_id | Board ID |
| nordic_ble.channel | Channel Index |
| nordic_ble.crcok | Indicates if CRC is Correct |
| nordic_ble.flags | Flags |
| nordic_ble.packet_counter | Packet Counter |
| nordic_ble.packet_time | Packet time (start to end) |
| nordic_ble.phy | PHY |
| nordic_ble.protover | Protocol Version |
Identified Key Features Within IP-Based Packets Dataset
| Feature | Meaning |
| http.content_length | Length of content in an HTTP response |
| http.request | HTTP request being made |
| http.response.code | Sequential number of an HTTP response |
| http.response_number | Sequential number of an HTTP response |
| http.time | Time taken for an HTTP transaction |
| tcp.analysis.initial_rtt | Initial round-trip time for TCP connection |
| tcp.connection.fin | TCP connection termination with a FIN flag |
| tcp.connection.syn | TCP connection initiation with SYN flag |
| tcp.connection.synack | TCP connection establishment with SYN-ACK flags |
| tcp.flags.cwr | Congestion Window Reduced flag in TCP |
| tcp.flags.ecn | Explicit Congestion Notification flag in TCP |
| tcp.flags.fin | FIN flag in TCP |
| tcp.flags.ns | Nonce Sum flag in TCP |
| tcp.flags.res | Reserved flags in TCP |
| tcp.flags.syn | SYN flag in TCP |
| tcp.flags.urg | Urgent flag in TCP |
| tcp.urgent_pointer | Pointer to urgent data in TCP |
| ip.frag_offset | Fragment offset in IP packets |
| eth.dst.ig | Ethernet destination is in the internal network group |
| eth.src.ig | Ethernet source is in the internal network group |
| eth.src.lg | Ethernet source is in the local network group |
| eth.src_not_group | Ethernet source is not in any network group |
| arp.isannouncement | Indicates if an ARP message is an announcement |
Identified Key Features Within IP-Based Flows Dataset
| Feature | Meaning |
| proto | Transport layer protocol of the connection |
| service | Identification of an application protocol |
| orig_bytes | Originator payload bytes |
| resp_bytes | Responder payload bytes |
| history | Connection state history |
| orig_pkts | Originator sent packets |
| resp_pkts | Responder sent packets |
| flow_duration | Length of the flow in seconds |
| fwd_pkts_tot | Forward packets total |
| bwd_pkts_tot | Backward packets total |
| fwd_data_pkts_tot | Forward data packets total |
| bwd_data_pkts_tot | Backward data packets total |
| fwd_pkts_per_sec | Forward packets per second |
| bwd_pkts_per_sec | Backward packets per second |
| flow_pkts_per_sec | Flow packets per second |
| fwd_header_size | Forward header bytes |
| bwd_header_size | Backward header bytes |
| fwd_pkts_payload | Forward payload bytes |
| bwd_pkts_payload | Backward payload bytes |
| flow_pkts_payload | Flow payload bytes |
| fwd_iat | Forward inter-arrival time |
| bwd_iat | Backward inter-arrival time |
| flow_iat | Flow inter-arrival time |
| active | Flow active duration |