The number of Internet of Things (IoT) cyber attacks worldwide amounted to over *** million in 2022. Over the recent years, this figure has increased significantly from around ** million detected cases in 2018. In the latest measured year, the year-over-year increase in the number of Internet of Things (IoT) malware incidents was ** percent.

The number of Internet of Things (IoT) attacks in the world reached over ***** million in December 2022. However, in the same month of 2021, the number of reported IoT attacks dropped to nearly ***********. The highest number of monthly attacks was detected in June 2022, with approximately ** million attacks.

IoT Cybersecurity Market Outlook

The global IoT cybersecurity market size is anticipated to grow significantly from 2023, when it was valued at approximately USD 12 billion, to a projected USD 30 billion by 2032, registering a compound annual growth rate (CAGR) of 11%. This remarkable expansion is primarily driven by the surging adoption of Internet of Things (IoT) devices across various industries, which has necessitated robust cybersecurity measures to protect against rising threats. The growing connectivity of devices increases the exposure to potential cyber attacks, compelling industries to invest heavily in cybersecurity solutions and services tailored specifically for IoT environments. Moreover, regulatory pressures and the increasing sophistication of cyber threats are further propelling the demand for comprehensive IoT cybersecurity solutions.

A critical growth factor for the IoT cybersecurity market is the exponential increase in the deployment of IoT devices across various sectors, including healthcare, manufacturing, and smart homes. With the burgeoning number of connected devices, each serving as a potential entry point for cyber threats, the need for enhanced security measures has become paramount. Companies are recognizing the vulnerabilities associated with IoT ecosystems, and this realization is driving the adoption of sophisticated cybersecurity solutions designed to safeguard data integrity, privacy, and overall network security. Additionally, the integration of IoT technology with critical infrastructure has amplified the focus on security to prevent disruptions that could lead to significant operational and financial losses.

Furthermore, the escalation of cyber attacks targeting IoT networks is prompting organizations to prioritize cybersecurity investments. High-profile incidents involving data breaches and ransomware attacks have underscored the vulnerabilities inherent in IoT systems, highlighting the necessity for robust security frameworks. The market is witnessing a surge in demand for advanced threat detection solutions, which leverage artificial intelligence and machine learning to identify potential threats and respond in real-time. This proactive approach to cybersecurity is gaining traction, as organizations strive to mitigate risks and protect sensitive information from unauthorized access and exploitation.

Another driving force behind the market's growth is the increasing regulatory landscape aimed at ensuring the security of IoT devices. Governments and regulatory bodies worldwide are implementing stringent regulations and standards to safeguard data and privacy. Compliance with these regulations necessitates the adoption of comprehensive cybersecurity solutions, thereby fueling market growth. Moreover, collaboration between public and private sectors is fostering the development of innovative security solutions, as stakeholders work together to address the evolving threat landscape. Such initiatives are further augmenting the market's expansion by creating a conducive environment for the adoption of IoT cybersecurity technologies.

Medical Cyber Security is becoming increasingly crucial as the healthcare sector continues to embrace IoT technologies. With the integration of connected medical devices and systems, there is a heightened risk of cyber threats that could compromise patient data and disrupt critical healthcare services. Ensuring the security of these devices is paramount to protecting patient privacy and maintaining the integrity of healthcare operations. As cyber threats become more sophisticated, healthcare providers are investing in advanced cybersecurity solutions to safeguard their IoT ecosystems. This includes implementing robust security frameworks that address vulnerabilities specific to medical devices and networks. The focus on Medical Cyber Security is also driven by regulatory requirements, which mandate stringent data protection measures to ensure compliance and mitigate the risks associated with data breaches.

Regionally, North America is poised to dominate the IoT cybersecurity market, owing to the presence of a robust IT infrastructure, a high concentration of IoT device manufacturers, and early adoption of advanced technologies. The region's companies are at the forefront of developing innovative cybersecurity solutions, which is driving market growth. Meanwhile, the Asia Pacific region is expected to witness significant growth, driven by the rapid digital transformation and increasing IoT adoption across various sectors, particularly in countrie

Context

This is a dataset of DDoS Botnet attacks from IOT devices.

Content

Contains all features about packets from bots.

Inspiration:

For making DDoS attack preventable.

The share of IoT attacks has increased significantly starting 2020. However, in the fourth quarter of 2021, the share of IoT attacks dropped at **** percent, from ** percent in the same quarter in the previous year.

This dataset accompanies the research article on MQTTEEB-D and is intended for public use in cybersecurity research. The MQTTEEB-D dataset is a practical real-world data set for intrusion detection improvement in Message Queuing Telemetry Transport (MQTT)-based Internet of Things (IoT) networks. In contrast to already existing datasets that are constructed on simulated network traffic, MQTTEEB-D is obtained from a real-time IoT deployment at the International University of Rabat (UIR), Morocco. Using MySignals IoT health sensors, Raspberry Pi 4, and an MQTT broker server, this dataset represents the actual complexity of the active IoT communication process, which synthetic data fails to offer. To narrow the gap between simulated and real-world attack scenarios, various cyberattacks including Denial of Service (DoS), Slow DoS against Internet of Things Environments (SlowITe), Malformed Data Injection, Brute Force, and MQTT publish flooding were carried out in real-time, permitting close monitoring of network traffic anomalies. The data was captured using Python wrapper for tshark (PyShark) and organized into multiple Comma-Separated Values (CSV) files. To ensure high data quality, we performed pre-processing steps, such as outlier removal, normalization, standardization, and class balance. Several processed forms (raw, cleaned, normalized, standardized, Synthetic Minority Over-sampling Technique (SMOTE)) applied for this dataset are provided, along with detailed metadata to facilitate ease of use in cybersecurity research. This dataset provides an opportunity for researchers to develop and validate intrusion detection models in a real-world MQTT environment - a critical ingredient in Artificial Intelligence (AI)-driven cybersecurity solutions for IoT networks. The dataset will support future research IoT security and anomaly detection domains.

Dragon_Pi

For a more in depth description of the Dragon_Pi dataset, please consult the journal article of the same name:

Lightbody et al., Future Internet, 2024, https://doi.org/10.3390/fi16030088 - specifically Section 3.2: Dataset Overview.

Dragon_Pi is an intrusion detection dataset for IoT devices. In the field of IoT security there are few datasets, and those which do exist tend to focus solely on network traffic. The Dragon_Pi dataset seeks to provide not only more data for the field of IoT security, but also, data of a somewhat under-published type: linear time series power consumption data.

Dragon_Pi is a fully labelled Intrusion Detection dataset for IoT devices. It is composed of both normal and under-attack power consumption data obtained from two separate testbeds - one using a DragonBoard 410c and the other a Raspberry Pi Model 3 - Hence the moniker Dragon_Pi.

These testbeds were set up with predefined normal behavour as described in the attached publications. The normal linear time series power consumption was sampled from the testbed under these normal conditions. Both testbeds were then attacked using some common attacks on IoT - the linear time series power consumption captured under these condtions as well.

Specifically, the testbeds were subjected to the Port Scan (using Nmap), SSH Brute Force (using Hydra) and SYNFlood Denial of Service (using Hping3) attacks. These attacks were repeated to gain insight to what their signatures looked like and also how varying the tool settings effected the resultant signature. A fourth type of scenario was also conducted on the testbeds - the "Capture the Flag" scenarios. In these files multiple attack types were used with a more specific target - to exfiltrate a hidden file from the testbeds.

Each file has three hierarchical levels of annotation for each sample within:

A simple "Normal or Anomaly" label for the specific sample

A specifc attack type label e.g. "SSH Bruteforce", for the specific sample

A specific tool setting for that attack e.g. "Hydra_T16", for the specific sample

Users can decide for themselves what level of annotation they require for their specific task.

Each file in the Dragon_Pi dataset is accompanied by its own legend file. This file explains the contents of the specific .csv file and the specific indexes of the events within.

The Dragon_Pi dataset consists of approximately 67 files, as shown in Table 1. Compressed, the datset totals approximately 13GB. Completely decompressed the dataset is approximately 80GB ( 30GB Pi data, 50 GB Dragon data).

Label Type Specific Label Number of Files DragonBoard 410c Number of Files Raspberry Pi

Normal Normal 3 2

Port Scan Attack Nmap_T5 2 1

Nmap_T4 1 1

Nmap_T3 1 1

Nmap_T2 1 1

SSH Brute Force Hydra_T32 4 2

Hydra_T16 16 2

Hydra_T3 8 2

Hydra_T1 5 2

SYNFlood DOS SYNFlood DOS 1 1

Capture the Flag Misc Attacks 3 5

Table 1. Enumeration of the in the Dragon_Pi dataset.

For a more in depth description of the Dragon_Pi dataset, please consult the journal article of the same name:

Lightbody et al., Future Internet, 2024, https://doi.org/10.3390/fi16030088 - specifically Section 3.2: Dataset Overview.

Publication of this dataset:

This dataset was published in Lightbody et al., Future Internet, 2024, https://doi.org/10.3390/fi16030088. Consult and cite this article for a more in depth dataset description, as well as an in depth review of first AI Intrusion Detection model trained on this dataset.

See article Lightbody et al., Future Internet, 2023, https://doi.org/10.3390/fi15050187 for a detailed investigation on the attack signatures discovered while creating this dataset. This work was an inital investigation of the dataset and can serve as a part 1 to the Dragon_Pi paper.

How to cite this dataset in your work:

Please cite these two DOIs when publishing using this dataset:

Dragon_Pi release publication: https://doi.org/10.3390/fi16030088 (most important)

Zenodo Dataset DOI: https://doi.org/10.5281/zenodo.10784947

This dataset simulates secure routing behavior in Internet of Things (IoT) environments with a focus on data integrity, encryption, and intrusion detection.

🧾 Dataset Features Column Name Description Packet_ID Unique identifier for each packet Timestamp Time when the packet was generated or transmitted Source_Node Node that sent the packet Destination_Node Node that received the packet Packet_Size Size of the packet in bytes (64 to 1500) Protocol Communication protocol used (TCP or UDP) Encryption_Type AES encryption level applied (AES-128, AES-192, AES-256) Hash_Match Whether SHA-256 hash matched at the receiver (Yes or No) Packet_Delay(ms) Delay in milliseconds to simulate latency Attack_Type Type of attack (Normal, Replay, Drop, Blackhole) Is_Attack Binary target column (0 = Normal, 1 = Attack)

🔐 Security Context Encryption: Simulated using various AES levels.

Integrity: Ensured with simulated SHA-256 hash validation.

Anomalies/Threats: Labeled attacks such as replay attacks, drop attacks, and blackhole routing attacks.

Target: Is_Attack column can be used for binary classification tasks.

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.

North America IoT Security Market size was valued at USD 13.8 Billion in 2023 and is projected to reach USD 34.1 Billion by 2031 growing at a CAGR of 12.1% from 2024 to 2031.

Key Market Drivers:

Escalating Cyber Attacks and Data Breaches: According to the FBI's 2023 Internet Crime Report, there were 800,944 cyber-attack complaints in the United States, with losses totaling more than USD 10.3 Billion. Approximately 22% of these events featured IoT-related vulnerabilities, highlighting the crucial need for improved IoT security measures to protect against cyber threats and data breaches.

Growing IoT Device Adoption Across Industries: According to the US Bureau of Labor Statistics, industrial IoT adoption in North American manufacturing increasing by 84% between 2021 and 2023. This spike, with devices per facility increasing from 1,650 to over 3,000, offers new potential security vulnerabilities, necessitating stronger IoT security solutions.

In 2022, almost a third of cyber attacks targeting IoT devices were aimed at a denial of service, while the outcome pursued by nearly ** percent of them was overflow. The number of IoT devices worldwide is forecast to be close to ** billion in 2030.

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

IoT Security Market Outlook

According to our latest research, the global IoT security market size reached USD 22.1 billion in 2024 and is projected to grow at a robust CAGR of 19.2% during the forecast period, reaching approximately USD 87.6 billion by 2033. This remarkable growth is primarily driven by the rapid proliferation of connected devices across industries, increasing incidences of cyber threats, and the urgent need for comprehensive security solutions tailored for the Internet of Things (IoT) ecosystem.

The primary growth factor for the IoT security market is the exponential increase in the adoption of IoT devices across various sectors such as manufacturing, healthcare, energy, and smart homes. With organizations and consumers integrating smart devices into their daily operations and lifestyles, the attack surface for cybercriminals has expanded dramatically. This surge in connected endpoints has created an urgent demand for advanced IoT security solutions that can safeguard sensitive data, ensure device integrity, and maintain operational continuity. Additionally, the evolving regulatory landscape mandating stricter security standards for IoT deployments is compelling organizations to invest heavily in robust security frameworks, further fueling market expansion.

Another significant driver is the growing sophistication and frequency of cyberattacks targeting IoT infrastructure. High-profile breaches, ransomware attacks, and data theft incidents have highlighted the vulnerabilities inherent in IoT systems, particularly in critical sectors like healthcare and manufacturing. This heightened threat environment has accelerated the adoption of multi-layered security solutions that encompass network, endpoint, application, and cloud security. Organizations are increasingly prioritizing proactive threat detection, real-time monitoring, and automated response capabilities to mitigate risks and ensure resilience against evolving cyber threats. The integration of artificial intelligence and machine learning technologies in IoT security solutions is also enhancing threat intelligence and response efficiency.

Furthermore, the ongoing digital transformation initiatives, coupled with the rise of Industry 4.0, are propelling the demand for IoT security. As enterprises embrace automation, smart manufacturing, and connected supply chains, the need to secure complex IoT environments becomes paramount. The convergence of IT and OT (Operational Technology) networks introduces new security challenges, necessitating comprehensive solutions that can address both legacy and modern systems. The increasing investment in smart cities, connected vehicles, and energy grids is also contributing to the marketÂ’s growth trajectory, as public and private sector entities seek to protect critical infrastructure from cyber threats.

As the IoT landscape continues to evolve, the integration of IoT Security Solution for Unified Threat Management (UTM) is becoming increasingly vital. UTM solutions are designed to provide a comprehensive security framework that addresses the diverse threats targeting IoT ecosystems. By consolidating multiple security functions such as firewall, intrusion prevention, and antivirus into a single platform, UTM solutions offer streamlined protection and simplified management. This approach not only enhances security posture but also reduces the complexity and cost associated with managing disparate security tools. As organizations seek to protect their IoT deployments from sophisticated cyber threats, the adoption of UTM solutions is expected to rise, driving further growth in the IoT security market.

From a regional perspective, North America continues to dominate the IoT security market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The regionÂ’s leadership is attributed to the high concentration of IoT device manufacturers, advanced technology adoption, and stringent regulatory frameworks. However, Asia Pacific is expected to witness the fastest growth during the forecast period, driven by rapid urbanization, expanding industrial base, and increasing government initiatives to promote IoT adoption and security. The Middle East & Africa and Latin America are also emerging as significant markets, supported

IoT Security Solution Market Outlook

The global IoT security solution market size was valued at approximately $16.55 billion in 2023 and is anticipated to reach $51.42 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 13.4% during the forecast period. The substantial growth is driven by the increasing adoption of IoT devices across various industries and the subsequent need to secure these devices and networks against cyber threats. The rising frequency of cyber-attacks and the growing importance of data privacy have made IoT security solutions crucial for organizations globally.

A key growth factor in the IoT security solution market is the rapid increase in the number of connected devices. As industries across the globe embrace digital transformation, the deployment of IoT devices has surged, resulting in a greater attack surface for cybercriminals. Consequently, the demand for robust security solutions to safeguard these devices and the data they generate has significantly increased. Organizations are investing heavily in advanced security technologies to protect their IoT ecosystems from potential threats, thereby propelling market growth.

Another major growth driver is the stringent regulatory landscape surrounding data protection and privacy. Governments and regulatory bodies worldwide are implementing strict regulations to ensure the security of IoT devices and networks. Compliance with these regulations necessitates the adoption of comprehensive IoT security solutions by organizations. For instance, the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States have mandated stringent data protection measures, encouraging companies to invest in IoT security solutions.

The proliferation of smart cities and smart home devices is also contributing to the market's expansion. As urban areas become increasingly connected, the need for secure IoT infrastructure has become paramount. Smart cities rely on a vast network of connected devices to manage infrastructure, utilities, and services efficiently. Similarly, the growing adoption of smart home devices, such as smart speakers, thermostats, and security systems, has heightened the need for robust security measures to protect user data and privacy, thereby boosting the IoT security solution market.

Regionally, North America holds a dominant position in the IoT security solution market, driven by the presence of major technology companies and early adopters of IoT technology. The region's robust technological infrastructure and high awareness regarding cybersecurity further fuel market growth. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, owing to increasing investments in IoT technology, rising cyber threats, and supportive government initiatives aimed at securing digital infrastructure.

Component Analysis

The IoT security solution market by component is segmented into hardware, software, and services. Each of these components plays a vital role in ensuring the security and integrity of IoT devices and networks. The hardware segment includes security devices such as routers, firewalls, and gateways that act as the first line of defense against cyber threats. These devices are equipped with advanced security features to monitor and prevent unauthorized access to IoT networks. As the deployment of IoT devices increases, the demand for secure and resilient hardware solutions is expected to rise significantly.

The software segment encompasses various security solutions designed to protect IoT devices and networks. This includes antivirus software, encryption tools, and security management platforms that provide comprehensive protection against cyber threats. The software solutions are constantly evolving to address new and emerging threats in the IoT landscape. The increasing complexity of cyber-attacks and the need for real-time threat detection and response are driving the growth of the software segment in the IoT security solution market.

Services form an integral part of the IoT security solution market, offering support and maintenance for hardware and software solutions. This segment includes consulting services, managed security services, and professional services such as integration and implementation. Organizations often rely on third-party service providers to manage their IoT security needs, ensuring that their systems are up-to-date and compliant with regulatory standards. The demand for specialized IoT security services is exp

IoT Security Market Outlook

According to our latest research, the IoT Security market size reached USD 6.7 billion in 2024, with a robust year-on-year expansion. The market is projected to grow at a CAGR of 18.5% from 2025 to 2033, resulting in a forecasted market size of USD 36.4 billion by 2033. The surge in IoT device adoption across industries, combined with rising concerns about cyber threats and regulatory requirements, is fueling this rapid growth. As per our latest research, the proliferation of connected devices and the increasing sophistication of cyberattacks remain the primary drivers for market expansion.

The growth trajectory of the IoT Security market is underpinned by several critical factors. Firstly, the exponential rise in the number of connected devices across industrial, commercial, and consumer domains has significantly expanded the attack surface for potential cyber threats. This has made robust IoT security solutions indispensable for organizations seeking to protect sensitive data and ensure uninterrupted operations. Regulatory frameworks such as the General Data Protection Regulation (GDPR) in Europe, and similar mandates globally, are compelling enterprises to invest in comprehensive IoT security measures. Additionally, the increasing integration of IoT devices in critical infrastructure sectors, including healthcare, energy, and transportation, has heightened the need for advanced security protocols to prevent catastrophic breaches. The convergence of operational technology (OT) and information technology (IT) environments further accentuates the need for cohesive security strategies, driving demand for innovative IoT security solutions and services.

Another substantial growth factor is the evolution of sophisticated cyber threats targeting IoT ecosystems. Attackers are leveraging advanced persistent threats (APTs), ransomware, and botnets to exploit vulnerabilities in IoT networks. The infamous Mirai botnet attack and subsequent high-profile breaches have heightened awareness among enterprises and governments about the critical importance of IoT security. As a result, organizations are increasingly allocating higher budgets for security solutions that encompass network, endpoint, application, and cloud security layers. The emergence of artificial intelligence and machine learning-powered security tools is also transforming the landscape, enabling proactive threat detection and response mechanisms. Furthermore, the rapid adoption of 5G networks is accelerating IoT deployment, which in turn is amplifying the demand for scalable and agile security frameworks capable of managing complex, distributed environments.

The IoT Security market is also benefiting from a surge in digital transformation initiatives, particularly in sectors such as manufacturing, healthcare, and energy. Enterprises are leveraging IoT technologies to drive operational efficiencies, enhance customer experiences, and unlock new revenue streams. However, these benefits come with increased exposure to cyber risks, necessitating a holistic approach to IoT security that encompasses device authentication, data encryption, and secure communication protocols. The growing emphasis on Zero Trust security models, which advocate for strict identity verification and least-privilege access controls, is gaining traction as organizations seek to mitigate insider threats and unauthorized access. The proliferation of edge computing is further complicating the security landscape, as data processing moves closer to the source, requiring decentralized security architectures. Collectively, these trends are catalyzing investments in both IoT security solutions and managed security services.

From a regional perspective, North America continues to dominate the IoT Security market, driven by high IoT adoption rates, stringent regulatory standards, and a mature cybersecurity ecosystem. Europe follows closely, fueled by robust data protection regulations and significant investments in smart infrastructure projects. The Asia Pacific region is witnessing the fastest growth, propelled by rapid digitalization, expanding industrial IoT applications, and increasing awareness of cybersecurity threats. Countries such as China, Japan, and India are at the forefront of IoT deployment, creating substantial opportunities for security vendors. Latin America and the Middle East & Africa are also experiencing steady growth, albeit from a smaller base, as governments and enterprises ramp up their digital transformation eff

The IoT security market size surpassed USD 21.23 Billion in 2024. With a projected CAGR of 21.80% from 2025-2034, the global market is expected to reach USD 152.55 Billion by 2034. North America plays a key role in this growth, driving advancements in IoT security across industries. The IoT security market is rapidly expanding as IoT networks face increasing cyber-attacks. With IoT device traffic growing, the need for robust security management becomes critical to protect sensitive data from unencrypted transmissions. As IoT devices become integral to industries worldwide, securing IoT networks from potential threats has never been more important. The rising IoT threat landscape highlights the necessity for advanced IoT security solutions to safeguard the Internet of Things, ensuring the protection of data across IoT-enabled environments.

About This Dataset

This dataset is based on the famous IoT-23 dataset, originally created by the Stratosphere Laboratory.

IoT-23 contains labeled network traffic captures (PCAPs) of Internet of Things (IoT) devices performing both benign and malicious activities, such as botnet attacks, scans, and normal operations.

In this version, the original IoT-23 dataset has been preprocessed and converted into 23 separate CSV files, where: - Each CSV file corresponds to one capture from the original dataset. - The CSVs contain structured network flow data along with labels for benign and malicious traffic. - Preprocessing was done to make the data more accessible for machine learning and data analysis tasks without needing to manually process PCAP files.

Features

• 23 CSV files (one for each capture)
• Each file contains:
• ts
• uid
• id.orig_h
• id.orig_p
• id.resp_h
• id.resp_p
• proto
• service
• duration
• orig_bytes
• resp_bytes
• conn_state
• local_orig
• local_resp
• missed_bytes
• history
• orig_pkts
• orig_ip_bytes
• resp_pkts
• resp_ip_bytes

• tunnel_parents label detailed-label

• The last column contains 3 features and needs to be processed and made into separate columns

Acknowledgements

Thanks to the Stratosphere IPS team for developing the IoT-23 dataset.

Ag-IoT Cybersecurity Market Outlook

According to our latest research, the global Ag-IoT Cybersecurity market size was valued at USD 2.1 billion in 2024, and it is expected to reach USD 7.3 billion by 2033, growing at a robust CAGR of 14.5% during the forecast period. The accelerating adoption of IoT solutions in agriculture, coupled with the rising threat landscape targeting digital farming infrastructure, is fueling the demand for advanced cybersecurity measures across the sector. The market growth is strongly driven by the proliferation of connected devices, automation in farming operations, and the increasing sophistication of cyberattacks targeting agricultural assets and data.

One of the core growth drivers for the Ag-IoT Cybersecurity market is the rapid digital transformation in the agricultural sector. As farms and agribusinesses increasingly rely on interconnected sensors, autonomous machinery, and cloud-based analytics, the attack surface for potential cyber threats has expanded significantly. The need to protect sensitive data, ensure the integrity of automated operations, and safeguard critical infrastructure has become paramount. This urgency is further amplified by the adoption of precision farming, which leverages IoT for real-time monitoring and decision-making, making robust cybersecurity solutions a necessity for operational continuity and data privacy.

Another significant factor propelling the Ag-IoT Cybersecurity market is the evolving regulatory landscape and growing awareness among stakeholders. Governments and industry bodies worldwide are introducing stringent data protection regulations and cybersecurity guidelines specific to the agricultural sector. Compliance with these mandates requires farms and agribusinesses to invest in advanced security frameworks, risk assessment tools, and incident response capabilities. Additionally, the increasing frequency of ransomware attacks, data breaches, and supply chain disruptions has heightened the focus on proactive security strategies, driving market growth as organizations seek to mitigate financial losses and reputational damage.

The integration of artificial intelligence (AI) and machine learning (ML) into cybersecurity solutions is also acting as a catalyst for market expansion. These advanced technologies enable real-time threat detection, predictive analytics, and automated response mechanisms, which are crucial for securing complex IoT ecosystems in agriculture. As the volume and diversity of IoT devices continue to grow, AI-driven cybersecurity platforms offer scalable and adaptive protection against emerging threats. This technological evolution is not only enhancing the effectiveness of security measures but also reducing the operational burden on farmers and agribusinesses, thereby supporting broader adoption of Ag-IoT Cybersecurity solutions.

From a regional perspective, North America currently dominates the Ag-IoT Cybersecurity market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The strong presence of technologically advanced agribusinesses, robust digital infrastructure, and proactive government initiatives underpin North America's leadership. Meanwhile, Asia Pacific is poised for the fastest growth, driven by rapid digitization in agriculture, increasing investment in smart farming technologies, and rising awareness about cybersecurity risks. Latin America, the Middle East, and Africa are also witnessing steady growth, supported by government-led digital agriculture programs and the gradual adoption of IoT solutions across the value chain.

Component Analysis

The Ag-IoT Cybersecurity market is segmented by component into hardware, software, and services, each playing a critical role in the overall security architecture. Hardware solutions, such as secure gateways, sensors with embedded security, and dedicated security modules, form the foundational layer of protection for IoT devices deployed in agricultural environments. These hardware components are ess

The European Internet of Things (IoT) security market is experiencing robust growth, driven by the increasing adoption of connected devices across various sectors. The market, valued at approximately €[Estimate based on Market Size XX and assuming XX is in Millions of Euros, if not convert it to Millions of Euros] million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 11.85% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the escalating concerns surrounding data breaches and cyberattacks targeting IoT devices are compelling organizations across industries like automotive, healthcare, and manufacturing to prioritize robust security solutions. Secondly, the ongoing shift towards cloud-based infrastructure and the increasing prevalence of smart devices are creating a larger attack surface, further stimulating demand for comprehensive security measures. Finally, the implementation of stringent data privacy regulations, such as GDPR, is driving businesses to enhance their IoT security posture to ensure compliance and mitigate potential penalties. The market is segmented by security type (Network, Endpoint, Application, Cloud, Other), solution type (Software, Services), and end-user industry (Automotive, Healthcare, Government, Manufacturing, Energy & Power, Retail). Within these segments, growth is particularly strong in cloud security solutions and services due to the increasing adoption of cloud-based IoT deployments. While the market faces certain restraints, such as the high cost of implementation and the complexity of managing IoT security across diverse environments, these challenges are likely to be offset by the considerable benefits of enhanced security and regulatory compliance. The competitive landscape is characterized by a mix of established players and emerging innovative firms, leading to intense competition and continuous advancements in security technologies. Specific regional analysis reveals that the United Kingdom, Germany, and France are currently major contributors to the European market's growth. The projected growth of the European IoT security market through 2033 is underpinned by the continuous evolution of IoT technology and its integration into diverse sectors. The automotive industry, with its increasing reliance on connected vehicles, is a major driver, demanding secure solutions to protect against potential vulnerabilities and ensure driver safety. The healthcare sector, witnessing a surge in the deployment of medical IoT devices, is also a significant contributor, prioritizing the protection of sensitive patient data. Government organizations and critical infrastructure providers are increasingly adopting IoT security to safeguard their systems from cyber threats. The market's continuous expansion will likely see increased investment in research and development, leading to innovations in areas such as AI-powered threat detection, blockchain-based security solutions, and improved cybersecurity awareness training. This sustained growth trajectory is supported by the anticipated increase in IoT device deployments across various industry verticals and the prevailing need for robust security measures to safeguard data and protect critical infrastructure. Recent developments include: November 2022 - Sophos Launched Managed Detection and Response (MDR) Service from an endpoint security provider that integrates vendor-agnostic telemetry. MDR is capable of threat detection and response capabilities. MDR offers unprecedented visibility and detection across diverse operating environments., November 2022 - Wipro launches European cyber security consultancy services. Wipro, a technology services and consulting provider has launched a strategic cyber security consulting service in Europe that is intended to give clients a complete solution to cope with security threats. Customers will have access to the whole range of cyber security capabilities offered by the organization, from strategy and execution to managed services, owing to the new offering accessible through Wipro CRS Europe., September 2022 - The European Commission launches a new Cyber Resilience Act to secure IoT devices in Europe. The Act prescribes minimum security standards for connected devices during product development and throughout the product life cycle to increase the security of European IoT software and hardware. In addition to holding manufacturers responsible for ensuring that their products are digitally secure, the Act will provide customers with further information about the security of their gadgets., January 2022- Based on Govt. United Kingdom report, the government of the United Kingdom provides software and technical assistance to Unite Kingdom entrepreneurs to help their growth. From the beginning of January 2022, applications are set to be open for the government's Help to Grow: Digital schemes, which assist smaller businesses in implementing digital technologies in favor of growth. Moreover, the project also provides businesses with discounts of up to £5,000 ( USD 5266.50) on approved Digital Accounting and Customer Relations Management (CRM) software. The government provides a dedicated website for this scheme, which offers free and impartial support and is currently operational to boost businesses' digital skills.. Key drivers for this market are: Increasing Number of Data Breaches, Emergence of Smart Cities. Potential restraints include: Growing Complexity among Devices, Coupled with the Lack of Ubiquitous Legislation. Notable trends are: Increasing Number of Data Breaches is Expected to Boost the Demand.

This statistic displays the share of enterprises who were victims of cyberattacks to Internet of Things systems in Italy in 2018. That year, approximately **** percent of the respondents reported that they did not experienced any attack to their Internet of Things systems. Only *** percent of respondents had been victims of IoT cyberattacks.