The Global Cybersecurity Index (GCI) is a trusted reference that measures the commitment of countries to cybersecurity at a global level – to raise awareness of the importance and different dimensions of the issue. As cybersecurity has a broad field of application, cutting across many industries and various sectors, each country's level of development or engagement is assessed along five pillars – (i) Legal Measures, (ii) Technical Measures, (iii) Organizational Measures, (iv) Capacity Development, and (v) Cooperation – and then aggregated into an overall score.​

Overview This dataset is a comprehensive, easy-to-understand collection of cybersecurity incidents, threats, and vulnerabilities, designed to help both beginners and experts explore the world of digital security. It covers a wide range of modern cybersecurity challenges, from everyday web attacks to cutting-edge threats in artificial intelligence (AI), satellites, and quantum computing. Whether you're a student, a security professional, a researcher, or just curious about cybersecurity, this dataset offers a clear and structured way to learn about how cyber attacks happen, what they target, and how to defend against them.

With 14134 entries and 15 columns, this dataset provides detailed insights into 26 distinct cybersecurity domains, making it a valuable tool for understanding the evolving landscape of digital threats. It’s perfect for anyone looking to study cyber risks, develop strategies to protect systems, or build tools to detect and prevent attacks.

What’s in the Dataset? The dataset is organized into 16 columns that describe each cybersecurity incident or research scenario in detail:

ID: A unique number for each entry (e.g., 1, 2, 3). Title: A short, descriptive name of the attack or scenario (e.g., "Authentication Bypass via SQL Injection"). Category: The main cybersecurity area, like Mobile Security, Satellite Security, or AI Exploits. Attack Type: The specific kind of attack, such as SQL Injection, Cross-Site Scripting (XSS), or GPS Spoofing. Scenario Description: A plain-language explanation of how the attack works or what the scenario involves. Tools Used: Software or tools used to carry out or test the attack (e.g., Burp Suite, SQLMap, GNURadio). Attack Steps: A step-by-step breakdown of how the attack is performed, written clearly for all audiences. Target Type: The system or technology attacked, like web apps, satellites, or login forms. Vulnerability: The weakness that makes the attack possible (e.g., unfiltered user input or weak encryption). MITRE Technique: A code from the MITRE ATT&CK framework, linking the attack to a standard classification (e.g., T1190 for exploiting public-facing apps). Impact: What could happen if the attack succeeds, like data theft, system takeover, or financial loss. Detection Method: Ways to spot the attack, such as checking logs or monitoring unusual activity. Solution: Practical steps to prevent or fix the issue, like using secure coding or stronger encryption. Tags: Keywords to help search and categorize entries (e.g., SQLi, WebSecurity, SatelliteSpoofing). Source: Where the information comes from, like OWASP, MITRE ATT&CK, or Space-ISAC.

Cybersecurity Domains Covered The dataset organizes cybersecurity into 26 key areas:

AI / ML Security

AI Agents & LLM Exploits

AI Data Leakage & Privacy Risks

Automotive / Cyber-Physical Systems

Blockchain / Web3 Security

Blue Team (Defense & SOC)

Browser Security

Cloud Security

DevSecOps & CI/CD Security

Email & Messaging Protocol Exploits

Forensics & Incident Response

Insider Threats

IoT / Embedded Devices

Mobile Security

Network Security

Operating System Exploits

Physical / Hardware Attacks

Quantum Cryptography & Post-Quantum Threats

Red Team Operations

Satellite & Space Infrastructure Security

SCADA / ICS (Industrial Systems)

Supply Chain Attacks

Virtualization & Container Security

Web Application Security

Wireless Attacks

Zero-Day Research / Fuzzing

Why Is This Dataset Important? Cybersecurity is more critical than ever as our world relies on technology for everything from banking to space exploration. This dataset is a one-stop resource to understand:

What threats exist: From simple web attacks to complex satellite hacks. How attacks work: Clear explanations of how hackers exploit weaknesses. How to stay safe: Practical solutions to prevent or stop attacks. Future risks: Insight into emerging threats like AI manipulation or quantum attacks. It’s a bridge between technical details and real-world applications, making cybersecurity accessible to everyone.

Potential Uses This dataset can be used in many ways, whether you’re a beginner or an expert:

Learning and Education: Students can explore how cyber attacks work and how to defend against them. Threat Intelligence: Security teams can identify common attack patterns and prepare better defenses. Security Planning: Businesses and governments can use it to prioritize protection for critical systems like satellites or cloud infrastructure. Machine Learning: Data scientists can train models to detect threats or predict vulnerabilities. Incident Response Training: Practice responding to cyber incidents, from web hacks to satellite tampering.

Ethical Considerations Purpose: The dataset is for educational and research purposes only, to help improve cybersecurity knowledge and de...

Cybersecurity Defense Training Dataset

  Dataset Description

This dataset contains 2,500 high-quality instruction-response pairs focused on defensive cybersecurity education. The dataset is designed to train AI models to provide accurate, detailed, and ethically-aligned guidance on information security principles while refusing to assist with malicious activities.

  Dataset Summary

Language: English License: Apache 2.0 Format: Parquet Size: 2,500 rows Domain:… See the full description on the dataset page: https://huggingface.co/datasets/AlicanKiraz0/Cybersecurity-Dataset-v1.

The National Institute of Standards and Technology (NIST) provides a Cybersecurity Framework (CSF) for benchmarking and measuring the maturity level of cybersecurity programs across all industries. The City uses this framework and toolset to measure and report on its internal cybersecurity program. The foundation for this measure is the Framework Core, a set of cybersecurity activities, desired outcomes, and applicable references that are common across critical infrastructure/industry sectors. These activities come from the National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF) published standard, along with the information security and customer privacy controls it references (NIST 800 Series Special Publications). The Framework Core presents industry standards, guidelines, and practices in a manner that allows for communication of cybersecurity activities and outcomes across the organization from the executive level to the implementation/operations level. The Framework Core consists of five concurrent and continuous functions: identify, protect, detect, respond, and recover. When considered together, these functions provide a high-level, strategic view of the lifecycle of an organization’s management of cybersecurity risk. The Framework Core identifies underlying key categories and subcategories for each function, and matches them with example references, such as existing standards, guidelines, and practices for each subcategory. This page provides data for the Cybersecurity performance measure. Cybersecurity Framework cumulative score summary per fiscal year quarter (Performance Measure 5.12) The performance measure page is available at 5.12 Cybersecurity. Additional Information Source: Maturity assessment / https://www.nist.gov/topics/cybersecurityContact: Scott CampbellContact E-Mail: Scott_Campbell@tempe.govData Source Type: ExcelPreparation Method: The data is a summary of a detailed and confidential analysis of the city's cybersecurity program. Maturity scores of subcategories within NIST CFS are combined, averaged, and rolled up to a summary score for each major category.Publish Frequency: AnnualPublish Method: ManualData Dictionary

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.

This dataset comprises 100,000 entries of synthesized cybersecurity incidents. It provides extensive details on various attack scenarios, target systems, and response measures. The data is structured across 15 columns, each capturing critical aspects of cybersecurity events, including:

Incident Details:

attack_type: Type of the cyberattack (e.g., DDoS, phishing, ransomware). target_system: Systems targeted during the attack. outcome: The result of the attack (e.g., success, failure). timestamp: Time of the attack occurrence. Attacker and Target Information:

attacker_ip: IP address of the attacker. target_ip: IP address of the target. Attack Metrics:

data_compromised_GB: Volume of data compromised in GB. attack_duration_min: Duration of the attack in minutes. attack_severity: Severity of the attack on a scale. Defense and Response:

security_tools_used: Security tools or defenses employed. response_time_min: Time taken to respond to the incident. mitigation_method: Method used to mitigate the attack. Contextual Information:

user_role: Role of the user or entity involved. location: Geographical location of the incident. industry: Industry targeted by the attack. This dataset is ideal for exploring patterns in cybersecurity incidents, evaluating the effectiveness of response strategies, and building predictive models to enhance security measures. Let me know if you'd like further analysis or visualization of the data!

2017 and 2018 cyber attacks in the HoneySELK environment.

Virtualisation has received widespread adoption and deployment across a wide range of enterprises and industries throughout the years. Network Function Virtualisation (NFV) is a technical concept that presents a method for dynamically delivering virtualised network functions as virtualised or software components. Virtualised Network Function (VNF) has distinct advantages, but it also faces serious security challenges. Cyberattacks such as Denial of Service (DoS), malware/rootkit injection, port scanning, and so on can target VNF appliances just like any other network infrastructure. To create exceptional training exercises for machine or deep learning (ML/DL) models to combat cyberattacks in VNF, a suitable dataset (VNFCYBERDATA) exhibiting an actual reflection, or one that is reasonably close to an actual reflection, of the problem that the ML/DL model could address is required. This article describes a real VNF dataset that contains over seven million data points and twenty-five cyberattacks generated from five VNF appliances. To facilitate a realistic examination of VNF traffic, the dataset includes both benign and malicious traffic.CitationIf you are using this dataset for your research, please reference it as"Ayodele, B.; Buttigieg, V. The VNF Cybersecurity Dataset for Research (VNFCYBERDATA). Data 2024, 9, 132. https://doi.org/10.3390/data9110132"DocumentationDataset documentation is available at: https://www.mdpi.com/2306-5729/9/11/132

The European Repository of Cyber Incidents (EuRepoC) is releasing the Global Dataset of Cyber Incidents in Version 1.3 as an extract of our backend database. This official release contains fully consolidated cyber incident data reviewed by our interdisciplinary experts in the fields of politics, law and technology across all 60 variables covered by the European Repository. Version 1.3 covers the years 2000 – 2024 entirely. The Global Dataset is meant for reliable, evidence-based analysis. If you require real-time data, please refer to the download option in our TableView or contact us for special requirements (including API access).

The dataset now contains data on 3416 cyber incidents which started between 01.01.2000 and 31.12.2024. The European Repository of Cyber Incidents (EuRepoC) gathers, codes, and analyses publicly available information from over 220 sources and 600 Twitter accounts daily to report on dynamic trends in the global, and particularly the European, cyber threat environment.

For more information on the scope and data collection methodology see: https://eurepoc.eu/methodology

Full Codebook available here

Information about each file

please scroll down this page entirely to see all files available. Zenodo only displays the attribution dataset by default.

Global Database (csv or xlsx):
This file includes all variables coded for each incident, organised such that one row corresponds to one incident - our main unit of investigation. Where multiple codes are present for a single variable for a single incident, these are separated with semi-colons within the same cell.

Receiver Dataset (csv or xlsx):
In this file, the data of affected entities and individuals (receivers) is restructured to facilitate analysis. Each cell contains only a single code, with the data "unpacked" across multiple rows. Thus, a single incident can span several rows, identifiable through the unique identifier assigned to each incident (incident_id).

Attribution Dataset (csv or xlsx):
This file follows a similar approach to the receiver dataset. The attribution data is "unpacked" over several rows, allowing each cell to contain only one code. Here too, a single incident may occupy several rows, with the unique identifier enabling easy tracking of each incident (incident_id). In addition, some attributions may also have multiple possible codes for one variable, these are also "unpacked" over several rows, with the attribution_id enabling to track each attribution.

Dyadic Dataset (csv or xlsx):
The dyadic dataset puts state dyads in the focus. Each row in the dataset represents one cyber incident in a specific dyad. Because incidents may affect multiple receivers, single incidents can be duplicated in this format, when they affected multiple countries.

The President`s Cyberspace Policy Review challenges the Federal community to develop a framework for research and development strategies that focus on game-changing technologies that can significantly enhance the trustworthiness of cyberspace. The Cybersecurity Game-Change Research and Development R and D Recommendations, coordinated through the Federal Networking and Information Technology Research and Development NITRD Program www.nitrd.gov and its Cyber Security Information Assurance CSIA Interagency Working Group IWG, have identified three 3 initial R and D themes to exemplify and motivate future Federal cybersecurity research activities: a Moving Target, Tailored Trustworthy Spaces, and Cyber Economic Incentives...

According to a 2024 survey of Chief Information Security Officers (CISO) worldwide, Ransomware attacks were a leading cybersecurity risk, with roughly ** percent naming it as one of the three major cybersecurity threats. A further share of ** percent of the respondents found malware to be a significant risk to their organizations' cybersecurity. Email fraud compromise and DDoS attacks followed closely, with ** percent.

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Cybersecurity Services Market Size 2024-2028

The cybersecurity services market size is forecast to increase by USD 49 billion at a CAGR of 9.23% between 2023 and 2028. The market is experiencing significant growth due to several key drivers. The increasing number of data breaches and cyber-attacks has heightened the awareness and importance of cybersecurity, leading to an increase in demand for these services. Another trend in the market is the integration of artificial intelligence (AI) and machine learning (ML) technologies to enhance threat detection and response capabilities. However, the high cost of implementing cybersecurity services remains a challenge for many organizations, particularly smaller businesses and governments with limited budgets. Despite this, the market is expected to continue growing as businesses recognize the need for cybersecurity to protect their valuable digital assets.

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

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The market is witnessing significant growth due to the increasing reliance on digital technologies and the subsequent rise in cyber threats. With the proliferation of cloud computing, remote work, and digital transactions, enterprises across various sectors including banking, financial services, healthcare, e-commerce platforms, and critical infrastructure are increasingly vulnerable to cyberattacks. Digital technologies have revolutionized the way businesses operate, enabling them to offer new services and reach wider audiences. However, they also introduce new risks. Cybersecurity risks, such as malicious attacks, are a major concern for organizations, particularly those dealing with sensitive data.

Moreover, the energy sector and critical infrastructure are also at risk from physical threats that can have digital consequences. Advanced security solutions are essential to mitigate these risks. AI and machine learning technologies are being increasingly adopted to enhance cybersecurity capabilities. Risk-based security approaches are becoming the norm, with organizations prioritizing resources to protect their most valuable assets. The shift to remote work has further complicated cybersecurity efforts. With employees working from home, the traditional perimeter security model is no longer sufficient. Organizations must ensure their networks and data are secure, regardless of where their employees are located. The cybersecurity skills gap is another challenge.

Similarly, with the increasing complexity of cyber threats, there is a growing demand for skilled cybersecurity professionals. Organizations must invest in training and development to ensure they have the necessary expertise in-house. In conclusion, the market is crucial in helping organizations navigate the digital landscape and protect against cyber threats. The market is expected to grow as businesses continue to adopt digital technologies and as cybercriminals become more sophisticated in their attacks. Organizations must prioritize cybersecurity to safeguard their assets and maintain customer trust.

Market Segmentation

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


End-user

  Government
  BFSI
  ICT
  Manufacturing
  Others


Geography

  North America

    Canada
    US


  APAC

    China
    India
    Japan
    South Korea


  Europe

    Germany
    UK
    France


  Middle East and Africa



  South America

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period. On-premises cybersecurity services offer organizations advanced security solutions to safeguard their infrastructure from cyberattacks. These solutions are installed and managed within an organization's own physical environment, providing a high degree of control and customization. With on-premises cybersecurity, businesses can fine-tune security configurations, set up strict access controls, and maintain direct supervision over their security operations. This level of control is essential for industries with stringent regulatory requirements, sensitive data handling policies, or unique security considerations. Machine Learning (ML) and threat detection technologies are increasingly being integrated into on-premises cybersecurity solutions to enhance their capabilities. Cloud security services are also becoming a significant component of on-premises cybersecurity offerings, allowing organizations to extend their security perimeter to the cloud. The demand for cybersecurity professionals is at an all-time high due to the increasing number of cyberattacks.

However, there is a significant cyber talent shortage, making it ch

The global healthcare cybersecurity market size is estimated to reach $12.6 billion by 2030, growing at CAGR of 14% during the forecast period.

These cybersecurity statistics will help you understand the state of online security and give you a better idea of what it takes to protect yourself.

The CSIAC-DoDIN (V1.0) dataset collects cybersecurity-related policies and issuances developed by the DoD Deputy CIO for Cybersecurity. The dataset is based on a knowledge base that clusters and classifies these policies and provides an organizational structure. The dataset includes annotated documents with policies, responsibilities, procedures, classification, purpose, scope, and applicability. The dataset also includes cluster and subcluster classification, type classification, and text entailment. The dataset is available for research and experimentation, and baseline performances using transformer language models have been provided. The limitations of the dataset include its focus on DoD cybersecurity policies, the English language, and the provided tasks. The dataset can serve as a benchmark and basis for future cybersecurity policy datasets and applications. Still, caution should be exercised regarding potential risks and biases associated with transformer language models.

Did the COVID-19 pandemic really affect cybersecurity? Short answer – Yes. Cybercrime is up 600% due to COVID-19.

Cyber Security Market size was valued at USD 197.4 billion in 2021, and is predicted to reach USD 657.02 billion by 2030, with a CAGR of 12.8% from 2022 to 2030.

For the fiscal year 2025, the government of the United States proposed nearly 13 billion U.S. dollar budget for cybersecurity, representing an increase from the previous fiscal year. These federal resources for cybersecurity are set to support a broad-based cybersecurity strategy for securing the government and enhancing the security of critical infrastructure and essential technologies.

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