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A Precise and Autonomous System for the Detection of Insect Emergence Patterns
Published on: January 9, 2019
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Dataset of intrusion detection alerts from a sharing platform.
Martin Husák1, Martin Žádník2, Václav Bartoš2
1Institute of Computer Science, Masaryk University, Czech Republic.
Data in Brief
|December 10, 2020
Summary
This cybersecurity dataset offers one week of intrusion detection alerts from multiple systems. It enables research into alert correlations, attack reconstruction, and threat analysis with anonymized network data.
Area of Science:
- Cybersecurity
- Network Security
- Intrusion Detection Systems
Background:
- Intrusion detection alerts are crucial for cybersecurity.
- Sharing alerts from heterogeneous systems aids research.
- Standardized formats like IDEA and taxonomies like eCSIRT.net improve data utility.
Purpose of the Study:
- To provide a valuable dataset for cybersecurity research.
- To facilitate the analysis of intrusion detection alerts.
- To support research in areas like temporal/spatial correlations and attack reconstruction.
Main Methods:
- Collected intrusion detection alerts over one week via the SABU platform.
- Utilized the Intrusion Detection Extensible Alert (IDEA) format for data storage.
- Employed the eCSIRT.net Incident Taxonomy for alert categorization.
- Anonymized network identifiers while retaining relevant features like blacklist presence and geolocation.
Main Results:
- A comprehensive dataset of heterogeneous intrusion detection alerts was generated.
- The dataset includes anonymized network identifiers and associated metadata.
- Alerts are categorized using a standardized incident taxonomy.
Conclusions:
- The dataset is suitable for various cybersecurity research applications.
- It supports analysis of intrusion detection alert patterns and characteristics.
- Facilitates advanced research in threat detection and response.
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