Threat Intelligence & Response Data
Data Science and Analytics
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About
This dataset contains cybersecurity incident records, documenting various attack types, their severity, response actions, and potential data exfiltration. The dataset is useful for analyzing cybersecurity threats, response strategies, and risk assessment in digital environments. It provides insights into how different threats are detected, mitigated, and eradicated.
Dataset Features
- AM_ID: Unique identifier for each cybersecurity incident.
- Event ID: A unique identifier for tracking the event.
- Timestamp: Date and time when the security incident occurred.
- User Agent: The browser or system details used during the attack.
- Attack Type: The classification of the security threat (e.g., Ransomware, DDoS, Insider Threat).
- Attack Severity: The level of threat posed by the attack (e.g., critical, high, medium).
- Data Exfiltrated: Boolean indicating whether sensitive data was stolen (TRUE/FALSE).
- Threat Intelligence: Additional context or information about the attack.
- Response Action: The action taken to mitigate the threat (e.g., eradicated, contained, recovered).
Distribution
- Data Volume: 20000 rows and 9 columns.
- Format: Tabular dataset suitable for analysis in CSV, Excel, or database formats.
Usage
This dataset is ideal for a variety of applications:
- Threat Intelligence Analysis: Identifying attack trends and assessing security risks.
- Incident Response Optimization: Evaluating how organizations respond to cyber threats.
- Machine Learning for Cybersecurity: Training models to detect and predict security incidents.
- Risk Assessment: Understanding the impact of various cyber threats on IT infrastructure.
Coverage
- Geographic Coverage: Global (cybersecurity incidents are location-agnostic).
- Time Range: Covers events from 2020 to 2024.
- Demographics: Focuses on cybersecurity threats across different industries and sectors.
License
CC0
Who Can Use It
- Cybersecurity Analysts: To assess and improve security response strategies.
- Data Scientists: For building predictive models for threat detection.
- Researchers: To study trends in cyber threats and attack patterns.
- Businesses: To analyze and enhance their cybersecurity defenses.
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