Article Information

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.

Abstract

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.

ZIP Folder Content

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.

Datasets' Content

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

Global internet of medical things (iomt) market size is expected at $192.02 Bn by 2028 at a growth rate of 24.6% and analysis by The Business Research Company.

Internet of medical things (IoMT) market size is estimated to reach USD 789.6 Billion by 2034, at a CAGR of 20.5% during the projected period. North America is expected to dominate the Market.

The Internet of Medical Things (IoMT) Market size was valued at USD 41.17 USD billion in 2023 and is projected to reach USD 158.16 USD billion by 2032, exhibiting a CAGR of 21.2 % during the forecast period. The increasing need for cost-effective and efficient healthcare delivery models, the rising prevalence of chronic diseases, and the advancements in wireless technologies are driving the market growth. The Internet of Medical Things (IoMT) is the network of Internet-connected medical devices, hardware infrastructure, and software applications used to connect healthcare information technology. Sometimes referred to as IoT in healthcare, IoMT allows wireless and remote devices to securely communicate over the Internet to allow rapid and flexible analysis of medical data. IoMT increases the amount of health data available to caregivers, the variety of sources it comes from and the speed at which it is collected, transmitted, and analyzed. More transmitted data improves both patients' and providers' decision-making capabilities. IoMT provides the devices and networks that enable telemedicine and virtual care. Remote healthcare capabilities became popular during the height of the COVID-19 pandemic as a way to limit the number of patients traveling to healthcare facilities and to alleviate the stress on overburdened hospitals and other medical facilities. Key drivers for this market are: Increasing Public Awareness for Safer Medicines to Stimulate Market Value. Potential restraints include: Surging Risk of Data Breach/Cyberattacks to Restrict Adoption of IoMT Technology. Notable trends are: Increasing Number of Hospitals and ASCs Identified as Significant Market Trend.

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. The Internet of Medical Things (IoMT) revolutionizes healthcare by interconnecting medical devices and applications through the internet. IoMT encompasses a wide array of devices, from wearable fitness trackers to advanced surgical robots, enabling remote patient monitoring, real-time data collection, and predictive analytics. This interconnectedness enhances patient care by providing continuous health monitoring, early disease detection, and personalized treatment plans. IoMT also improves operational efficiency in healthcare settings, streamlining workflows and reducing costs. However, it raises concerns about data security and privacy due to the sensitive nature of medical information. Despite challenges, IoMT promises to reshape healthcare delivery, making it more proactive, precise, and accessible.

Internet of Medical Things (IoMT) Market Overview

The global Internet of Medical Things (IoMT) Market size was valued at USD 92.54 billion in 2023 and is predicted to reach USD 419.44 billion by 20

The market for Internet of Medical Things (IoMT) is expected to grow at a CAGR of around 27.2% from 2020 to 2027 and expected to reach the market value of around US$ 155.8 Bn by 2027.

As of 2022, 21 percent of surveyed healthcare organizations in the United States reported spending between 500 thousand and one million U.S. dollars on Internet of Things (IoT) or Internet of Medical Things (IoMT) device security annually. Furthermore, 21 percent said their spending on device security amounted to between one and 2.5 million U.S. dollars.

Internet of Medical Things Market Outlook

The global Internet of Medical Things (IoMT) market size is projected to expand significantly from $56.2 billion in 2023 to $285.5 billion by 2032, growing at a robust compound annual growth rate (CAGR) of 20.3% during the forecast period. This explosive growth can be attributed to advancements in medical technology, the increasing adoption of connected devices in healthcare, and a rising emphasis on personalized medicine.

One of the primary growth factors driving the IoMT market is the increasing prevalence of chronic diseases and the subsequent need for continuous monitoring and management. Chronic conditions such as diabetes, heart disease, and respiratory disorders require constant monitoring, which IoMT devices can provide efficiently and accurately. The integration of IoMT in healthcare allows for real-time data collection and analysis, enabling healthcare providers to make timely and informed decisions, potentially saving lives and reducing hospital readmissions.

Another significant growth factor is the growing demand for remote patient monitoring (RPM) solutions, especially in the wake of the COVID-19 pandemic. The necessity for maintaining social distancing and reducing in-person consultations has accelerated the adoption of telemedicine and RPM technologies. IoMT devices facilitate seamless remote monitoring of patients, ensuring continuous care without the need for frequent hospital visits. This shift not only optimizes healthcare resources but also enhances patient convenience and satisfaction.

Technological advancements and innovations in IoT and medical devices are also propelling the IoMT market forward. The development of sophisticated sensors, wearable devices, and mobile health applications has revolutionized patient care. These technologies enable the collection of comprehensive health data, which can be integrated with electronic health records (EHRs) and analyzed using advanced analytics and artificial intelligence (AI) algorithms. This integration enhances the accuracy of diagnoses, improves treatment plans, and supports personalized medicine.

Regionally, North America is expected to dominate the IoMT market due to its advanced healthcare infrastructure, high adoption rates of innovative technologies, and significant investments in healthcare IT. Europe and Asia Pacific are also anticipated to witness substantial growth, driven by increasing healthcare expenditure, growing awareness about connected healthcare devices, and favorable government initiatives. The Asia Pacific region, in particular, is projected to exhibit the highest CAGR due to its large patient pool and rapid technological advancements.

Component Analysis

The IoMT market's components can be broadly categorized into devices, software, and services. Each of these segments plays a critical role in the overall ecosystem of connected healthcare. The devices segment, which includes various types of sensors, wearables, and diagnostic equipment, constitutes the backbone of the IoMT infrastructure. These devices are responsible for collecting real-time health data from patients and transmitting it to healthcare providers for analysis. Continuous improvements in device miniaturization, battery life, and data accuracy are driving the growth of this segment.

Software solutions are integral to the functionality of IoMT devices, facilitating data integration, storage, and analysis. These software platforms enable seamless communication between various devices and healthcare systems, ensuring that data is accurately captured and processed. Advanced software solutions leverage AI and machine learning (ML) technologies to analyze vast amounts of health data, providing valuable insights for personalized treatment plans and predictive analytics. The increasing demand for integrated healthcare solutions is propelling the growth of the software segment.

Services represent a crucial component of the IoMT market, encompassing a wide range of offerings such as installation, maintenance, training, and consulting. Healthcare providers rely on these services to ensure the optimal performance of IoMT devices and systems. Service providers play a vital role in assisting healthcare organizations with the implementation of IoMT solutions, data management, and compliance with regulatory standards. The growing complexity of IoMT ecosystems and the need for continuous support and updates are driving the expansion of the services segment.

Report Scope<

Internet of Medical Things (IoMT) Market

Get the sample copy of Internet of Medical Things Market Report 2024 (Global Edition) which includes data such as Market Size, Share, Growth, CAGR, Forecast, Revenue, list of Internet of Medical Things Companies (GE, Philips, Medtronic, Cisco, IBM, Siemens, Hill Rom, Johnson & Johnson, Biotronik), Market Segmented by Type (Wearable Devices, Stationary Devices, Implantable Devices), by Application (Hospitals, Clinics)

As of 2022, the cybersecurity activities at healthcare organizations in the United States involving the Internet of Things (IoT) and Internet of Medical Things (IoMT) were in the middle stage of maturity for 35 percent of surveyed healthcare providers. Furthermore, 22 percent of the organizations were in their early stage of maturity regarding IoT and IoMT activities, with a similar share of respondents being in their late-middle stage. Only about 21 percent of respondents reported being in the mature stage.

Between 2020 and 2022, around a quarter of surveyed healthcare institutions in the United States experienced nine to 15 cyberattacks involving Internet of Things (IoT) and Internet of Medical Things (IoMT) devices. A further 24 percent reported experiencing four to eight cyberattacks in the measured period.

As of 2022, 45 percent of surveyed healthcare organizations in the United States said that a severe hacking incident might lead the organization to rethink their Internet of Things (IoT) or Internet of Medical Things cybersecurity budget. Furthermore, around four in ten healthcare providers said new regulations would be the driving factor for making changes in their cybersecurity budget.

The Global Internet of Medical Things Market is expected to grow at a significant CAGR of close to 28.1% during the forecast period 2023 to 2031.

The CICIoMT2024 dataset is a comprehensive dataset designed for cybersecurity research focused on the Internet of Medical Things (IoMT). Developed by the Canadian Institute for Cybersecurity, it simulates realistic IoMT network traffic, representing the diverse and evolving threats faced by connected healthcare devices. The dataset comprises network traffic data from various IoMT devices, with labeled instances for 18 distinct types of cyberattacks, alongside benign traffic data.

Each cyberattack type is meticulously crafted to reflect common and advanced threats, such as Distributed Denial of Service (DDoS), ransomware, man-in-the-middle attacks, and malware injections. With a balanced mix of attacks and benign instances, CICIoMT2024 enables robust training and testing for various cybersecurity models.

The CICIoMT2024 dataset’s detailed packet-level information and multi-class labels make it ideal for developing and evaluating machine learning and deep learning algorithms. Its design emphasizes real-world application, making it a valuable resource for researchers aiming to enhance IoMT security, protect patient data, and ensure the resilience of medical networks against cyber threats.