Intruder Detection Systems Market size is set to expand from $ 4.95 Billion in 2023 to $ 8.33 Billion by 2032, with an anticipated CAGR of around 5.3% from 2024 to 2032.

With the continuous expansion of data exchange, the threat of cybercrime and network invasions is also on the rise. This project aims to address these concerns by investigating an innovative approach: an Attentive Transformer Deep Learning Algorithm for Intrusion Detection of IoT Systems using Automatic Xplainable Feature Selection. The primary focus of this project is to develop an effective Intrusion Detection System (IDS) using the aforementioned algorithm. To accomplish this, carefully curated datasets have been utilized, which have been created through a meticulous process involving data extraction from the University of New Brunswick repository. This repository houses the datasets used in this research and can be accessed publically in order to replicate the findings of this research.

The Intrusion Detection System (IDS) Software market is experiencing robust growth, driven by the escalating need for robust cybersecurity solutions across diverse sectors. The increasing frequency and sophistication of cyberattacks, coupled with stringent data privacy regulations like GDPR and CCPA, are compelling organizations of all sizes to invest heavily in advanced threat detection and prevention mechanisms. This market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by the rising adoption of cloud-based IDS solutions, which offer scalability, flexibility, and cost-effectiveness compared to on-premises deployments. The market is segmented by application (large enterprises and SMEs) and type (on-premises and cloud-based), with the cloud-based segment expected to dominate due to its inherent advantages. Furthermore, the growing adoption of Artificial Intelligence (AI) and Machine Learning (ML) in IDS software enhances threat detection accuracy and reduces false positives, further boosting market expansion. Significant regional variations exist. North America currently holds the largest market share, driven by high technological adoption and robust cybersecurity infrastructure. However, Asia-Pacific is expected to witness the fastest growth rate during the forecast period, fueled by rapid digital transformation and increasing internet penetration in emerging economies. The market faces certain restraints, including the rising costs associated with implementation and maintenance of sophisticated IDS systems, as well as the skills gap in cybersecurity professionals capable of effectively managing these systems. Nonetheless, the overall market outlook remains overwhelmingly positive, driven by sustained demand for enhanced cybersecurity and evolving technological advancements within the IDS software landscape. The prominent vendors mentioned—including SolarWinds, ManageEngine, Cisco, and others—are actively innovating and expanding their product portfolios to capitalize on this growing opportunity.

Intrusion Detection System Market share, size, growth, statistics, and trends analysis report by Component (Solutions and Services), By Type (Network-Based, Wireless-Based, Network Behavior Analysis, and Host-Based), By Deployment Type (Cloud and On-Premises), By Vertical (BFSI, Government & Defense, Healthcare, IT & Telecom, Retail & Ecommerce, Manufacturing, and Others) and By Region - Market Scope, Opportunities & Forecast, 2020-2027.

Intrusion Detection System Appliance Market Outlook

According to our latest research, the global Intrusion Detection System (IDS) Appliance market size reached USD 5.8 billion in 2024 and is anticipated to expand to USD 13.4 billion by 2033, growing at a robust CAGR of 9.7% during the forecast period. The primary growth factor driving this market is the escalating sophistication of cyber threats, which compels organizations across industries to invest in advanced security solutions, including IDS appliances, to safeguard their critical digital assets and maintain regulatory compliance.

The growth trajectory of the Intrusion Detection System Appliance market is heavily influenced by the rapidly evolving threat landscape. As cyberattacks become more frequent and complex, organizations are compelled to invest in proactive security measures. IDS appliances, which provide real-time monitoring and alerting for suspicious activities, have become indispensable in modern enterprise security architectures. The increasing adoption of digital transformation initiatives, cloud computing, and the proliferation of connected devices have further expanded the attack surface, making robust intrusion detection capabilities a critical requirement for enterprises of all sizes. Additionally, stringent data protection regulations across sectors such as BFSI, healthcare, and government are driving the deployment of IDS appliances to ensure compliance and avoid costly breaches.

Another significant growth driver is the integration of artificial intelligence (AI) and machine learning (ML) technologies into IDS appliances. These advanced technologies enable IDS solutions to analyze large volumes of network traffic, detect previously unknown threats, and reduce false positives. The evolution from traditional signature-based detection to more sophisticated anomaly-based and behavior-based methods has significantly enhanced the effectiveness of IDS appliances. Organizations are increasingly seeking solutions that can provide automated threat detection and response, which not only improves security posture but also alleviates the burden on IT security teams facing skills shortages. As a result, vendors are focusing on innovation and product differentiation to capture market share, further fueling market expansion.

The rise of remote work and hybrid workplace models post-pandemic has also contributed to the growth of the Intrusion Detection System Appliance market. With employees accessing corporate resources from various locations and devices, the need for comprehensive network visibility and real-time threat detection has intensified. IDS appliances, with their ability to monitor traffic across on-premises, cloud, and hybrid environments, are increasingly being adopted to address these challenges. Furthermore, the growing awareness among small and medium enterprises (SMEs) about the importance of cybersecurity is expanding the addressable market for IDS solutions, as these organizations strive to protect themselves from cyber risks that could threaten business continuity.

Regionally, North America remains the dominant market for IDS appliances, attributed to the presence of major technology vendors, high cybersecurity spending, and stringent regulatory frameworks. However, the Asia Pacific region is witnessing the fastest growth, driven by digitalization initiatives, rising cybersecurity incidents, and increasing investments in IT infrastructure. Europe and Latin America are also experiencing steady growth, supported by regulatory compliance requirements and the increasing adoption of cloud-based security solutions. The Middle East & Africa region, while still emerging, is expected to present significant opportunities as organizations in these markets prioritize cybersecurity in response to growing threat vectors.

Component Analysis

The Component segment of the Intrusion Detection System Appliance market is categorized into hardware, software, and services, each playing a critical role in the overall IDS ecosystem. Hardware IDS appliances, which include dedicated devices installed within network infrastructures, continue to hold a significant market share due to their high reliability and performance in large-scale deployments. These appliances are preferred by organizations with complex network architectures and stringent security requirements, such as financial institutions and government agencies. However, the

Security is the main challenge in Supervisory Control and Data Acquisition (SCADA) systems since SCADA systems must be connected to heterogeneous networks to save costs. SCADA devices such as RTUs have limited resources, so a small-scale cyber attack on a computer network will have a major impact on the SCADA system. This study discusses the SCADA system with the IEC 60870-5-104 protocol which is widely used in the power plant industry. A physical testbed is built to simulate the electrical distribution process. The SCADA system in the distribution section is more vulnerable than other parts because it is located directly in the community environment so that many holes can be entered by attackers. The purpose of this study is to obtain relevant datasets in the SCADA system. The simulation carried out in this study is a normal communication between the HMI and the RTU, then attacked to disrupt the communication. The attack activities carried out are port scan, brute force and DoS. DoS attacks carried out are ICMP flood, Syn flood, and IEC 104 flood. IEC 104 flood attack is a modified attack to attack RTU where RTU is flooded with an unknown typeid ASDU (Application Service Data Unit). Attacks are carried out using Kali Linux operating system. All scenarios are recorded and saved in pcap. To prove that there is attack data traffic on the IDS dataset Snort and Suricata are used to detect it. In this study, there are also intrusion detection performance results from Snort and Suricata

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

The Intrusion Detection and Prevention System Market size is expected to reach a valuation of USD 9.06 billion in 2033 growing at a CAGR of 6.5%. The Intrusion Detection and Prevention System market research report classifies market by share, trend, demand, forecast and based on segmentation.

Market Research Intellect presents the Physical Intrusion Detection And Prevention Systems Market Report-estimated at USD 5.98 billion in 2024 and predicted to grow to USD 10.23 billion by 2033, with a CAGR of 7.4% over the forecast period. Gain clarity on regional performance, future innovations, and major players worldwide.

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Intrusion Detection System Market Size 2024-2028

The intrusion detection system market size is forecast to increase by USD 4.65 billion at a CAGR of 14% between 2023 and 2028.

The market is witnessing significant growth due to the escalating number of cyberattacks and the need to secure IT service infrastructure, particularly in the banking and financial services industry (BFSI). IDS solutions employ two primary identification techniques: signature-based and anomaly detection. Signature-based identification relies on known attack patterns, while anomaly detection identifies deviations from normal behavior.
Additionally, with the rise in digital transactions, there is a growing emphasis on securing security architecture through traffic monitoring and intrusion detection. The market is driven by the increasing demand for BFSI applications and the subsequent need to protect against cyber threats. However, the high cost of maintaining IDS solutions remains a challenge. In conclusion, the IDS market is expected to continue growing as organizations prioritize securing their IT infrastructure against cyber threats.

What will be the Size of the Market During the Forecast Period?

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The Intrusion Detection System (IDS) market is a significant segment of the cybersecurity industry, playing a crucial role in safeguarding IT infrastructure against various cyber threats. IDS solutions help identify and prevent unauthorized access, malicious activities, and potential security breaches. These systems can be categorized into Network Intrusion Detection Systems (NIDS) and Host-based Intrusion Detection Systems (HIDS). IDS and Intrusion Prevention Systems (IPS) are essential components of an organization's cybersecurity strategy. IPS goes beyond simple identification and provides real-time prevention of attacks. Both IDS and IPS are instrumental in mitigating risks from phishing incidents, cyberattacks, and other malicious threats.
Additionally, cybersecurity is a major concern for various sectors, including BFSI applications, telecom, defense, and cloud computing. With the increasing reliance on IT infrastructure and work from home arrangements, cybersecurity expenditure has seen a significant rise. IDS and IPS solutions are integral to securing data and maintaining information security. Cybercrimes are on the rise, with malicious threat actors constantly evolving their tactics. Traditional signature-based identification methods may not be sufficient to detect advanced threats. Anomaly detection, a key feature of modern IDS and IPS solutions, can help identify unusual patterns and potential threats. IDS and IPS solutions are not limited to protecting traditional IT infrastructure.
Simultaneously, they also play a vital role in securing cloud computing environments. IDS and IPS as part of IDP (Intrusion Detection and Prevention) systems offer advanced threat detection and prevention capabilities, ensuring comprehensive protection against cyberattacks. Ransomware attacks have emerged as a major concern, with their disruptive impact on business operations. IDS and IPS solutions can help prevent ransomware attacks by identifying and blocking malicious traffic before it can cause damage. In conclusion, IDS and IPS solutions are essential components of an effective cybersecurity strategy. They help organizations protect their IT infrastructure, data security, and information security against various cyber threats, including phishing incidents, cyberattacks, and malicious threat actors. The market for IDS and IPS solutions is expected to grow as organizations continue to invest in advanced cybersecurity solutions to mitigate risks and maintain business continuity. 

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  On-premises
  Cloud-based


Geography

  North America

    US


  APAC

    China
    Japan


  Europe

    Germany
    UK


  Middle East and Africa



  South America

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

The on-premises segment is projected to dominate the market in the US, with substantial growth in terms of revenue. Large enterprises, particularly those with a global footprint, are the primary consumers of on-premises intrusion detection systems. The primary reason for this preference is the control it offers over managing software assets, including data generated and stored within business applications. This deployment model enables organizations to ensure compliance with licensing agreements and automate tasks, making it an attractive choice for many busine

The global intrusion detection & protection system market has been valued at US$ 6.8 billion in 2024, as revealed in the updated Fact.MR research report. Market revenue has been forecasted to increase at a CAGR of 11% and reach US$ 19.2 billion by the end of 2034.

Country-wise Insights

Category-wise Insights

Accuracy Analysis of Intrusion Detection System

The Perimeter Intrusion Detection System Market size is expected to reach a valuation of USD 44.67 billion in 2033 growing at a CAGR of 8.50%. The Perimeter Intrusion Detection System Market research report classifies market by share, trend, demand, forecast and based on segmentation.

Market Size and Drivers: The global Intrusion Detection System (IDS) & Intrusion Prevention System (IPS) market was valued at USD 2902.6 million in 2025 and is projected to reach USD 4705.5 million by 2033, exhibiting a CAGR of 6.5% from 2025 to 2033. The increasing incidence of cyberattacks and the growing need for data and network security are driving the market growth. The adoption of cloud computing and the proliferation of IoT devices have also created a significant demand for IDS and IPS solutions. Trends and Restraints: The market is witnessing a shift towards cloud-based IDS/IPS solutions as organizations seek to reduce operational costs and enhance scalability. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) in IDS/IPS systems is improving the accuracy and efficiency of threat detection and prevention. However, factors such as the high cost of deployment and the shortage of skilled security professionals may hinder the market's full potential.

Perimeter Intrusion Detection Systems Market valued at $13.26 B in 2023, and is projected to $USD 44.08 B by 2032, at a CAGR of 14.28% from 2023 to 2032.

Automotive Intrusion Detection System Market Outlook

According to our latest research, the global Automotive Intrusion Detection System market size reached USD 1.45 billion in 2024, reflecting a robust demand for cybersecurity solutions in the automotive sector. The market is expected to register a CAGR of 16.8% from 2025 to 2033, with the total market value forecasted to reach USD 5.03 billion by 2033. This expansion is primarily driven by the rapid proliferation of connected vehicles, stringent regulatory requirements, and the increasing sophistication of cyber threats targeting automotive systems worldwide.

The growth of the Automotive Intrusion Detection System market is significantly propelled by the escalating integration of advanced technologies in modern vehicles. As vehicles become increasingly connected through telematics, infotainment, and advanced driver-assistance systems (ADAS), their vulnerability to cyberattacks rises exponentially. Automakers are compelled to invest in robust intrusion detection systems to safeguard critical vehicle functions and passenger safety. The adoption of electric vehicles (EVs) and autonomous driving technologies further amplifies the need for sophisticated cybersecurity solutions, as these platforms rely heavily on software and connectivity, making them prime targets for malicious intrusions.

Another crucial growth factor is the evolving regulatory landscape, which mandates higher cybersecurity standards in the automotive industry. Governments and regulatory bodies across North America, Europe, and Asia Pacific are introducing stringent guidelines to ensure the safety and integrity of automotive networks. For instance, the United Nations Economic Commission for Europe (UNECE) has implemented WP.29 regulations, compelling automakers to integrate cybersecurity measures such as intrusion detection systems into their vehicles. These regulations are fostering a culture of proactive cybersecurity adoption, accelerating market growth by making intrusion detection systems an indispensable component of vehicle architecture.

In addition to regulatory mandates, the increasing awareness among consumers and fleet operators about the risks associated with automotive cyberattacks is fueling demand for intrusion detection systems. High-profile cyber incidents in recent years have underscored the potential consequences of vehicle hacking, from data breaches to compromised safety features. As a result, automotive OEMs and tier-1 suppliers are prioritizing cybersecurity as a key differentiator in their product offerings. The competitive landscape is witnessing substantial investments in R&D to develop adaptive, AI-powered intrusion detection systems capable of identifying and mitigating emerging threats in real time.

From a regional perspective, North America currently leads the Automotive Intrusion Detection System market, accounting for over 37% of global revenue in 2024. This dominance is attributed to the early adoption of connected vehicle technologies, a mature automotive ecosystem, and the presence of leading cybersecurity providers. However, the Asia Pacific region is poised to exhibit the highest CAGR of 18.2% over the forecast period, driven by the rapid expansion of the automotive sector in countries like China, Japan, and South Korea, coupled with increasing regulatory focus on vehicle cybersecurity. Europe remains a critical market, benefiting from stringent data protection laws and a strong emphasis on vehicle safety.

Component Analysis

The Automotive Intrusion Detection System market is segmented by component into hardware, software, and services, each playing a pivotal role in the overall cybersecurity ecosystem. The hardware segment encompasses network gateways, electronic control units (ECUs), and other physical devices designed to monitor and filter data traffic within the vehicle. In 2024, hardware accounted for approximately 38% of the market share, as the deployment of dedicated security modules becomes standard practice among OEMs. These components are essential for ensuring real-time detection and response to threats, especially as vehicles integrate more connected and autonomous features.

The software segment is witnessing the fastest growth, with a projected CAGR of 19.4% through 2033. This surge is attributed to the dynamic nature

Cyber-attack is one of the most challenging aspects of information technology. After the emergence of the Internet of Things, which is a vast network of sensors, technology started moving towards the Internet of Things (IoT), many IoT based devices interplay in most of the application wings like defence, healthcare, home automation etc., As the technology escalates, it gives an open platform for raiders to hack the network devices. Even though many traditional methods and Machine Learning algorithms are designed hot, still it “Have a Screw Loose” in detecting the cyber-attacks. To “Pull the Plug on” an effective “Intrusion Detection System (IDS)” is designed with “Deep Learning” technique. This research work elucidates the importance in detecting the cyber-attacks as “Anomaly” and “Normal”. Fast Region-Based Convolution Neural Network (Fast R-CNN), a deep convolution network is implemented to develop an efficient and adaptable IDS. After hunting many research papers and articles, “Gradient Boosting” is found to be a powerful optimizer algorithm that gives us a best results when compared to other existing methods. This algorithm uses “Regression” tactics, a statistical technique to predict the continuous target variable that correlates between the variables. To create a structured valid dataset, a stacked model is made by implementing the two most popular dimensionality reduction techniques Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) algorithms. The brainwaves made us to hybridize Fast R-CNN and Gradient Boost Regression (GBR) which reduces the loss function, processing time and boosts the model’s performance. All the above said methods are trained and tested with NIDS dataset V.10 2017. Finally, the “Decision Making” model decides the best result by giving an alert message. Our proposed model attains a high accuracy of 99.5% in detecting the “Cyber Attacks”. The experiment results revealed that the effectiveness of our proposed model surpasses other deep neural network and machine learning techniques which have less accuracy.

Check Market Research Intellect's Wireless Intrusion Detection System Market Report, pegged at USD 1.2 billion in 2024 and projected to reach USD 2.5 billion by 2033, advancing with a CAGR of 10.2% (2026-2033).Explore factors such as rising applications, technological shifts, and industry leaders.

Industrial Intrusion Detection System Market is projected to reach USD $ Billion in 2025, at a $% CAGR by driving industry size, share, segments research, top cpany analysis, trends and forecast report 2025 to 2031.