Are you looking for a straightforward way to detect anomalies in your Elasticsearch events? Look no further! The ee-outliers framework is here to simplify outlier detection with its user-friendly configuration and powerful underlying statistical models.
What is ee-outliers?
The ee-outliers framework is designed to detect statistical outliers in events stored within an Elasticsearch cluster. This powerful tool allows users to create easy-to-write configuration files that define how events should be analyzed for outliers. Initially developed to identify anomalies in security events, its versatility means it can be repurposed for outlier detection across various datasets. All you need is Docker and an Elasticsearch cluster to get started!
Why Use ee-outliers?
Although Elasticsearch is a fantastic search engine, it still lacks the capability to execute complex queries needed for advanced analysis and outlier detection. The ee-outliers framework bridges this gap by providing the ability to write simple and effective use cases that leverage statistical models. As an added bonus, machine learning models are currently under development, making this tool even more robust!
How It Works
The magic of the ee-outliers framework lies in its utilization of user-defined statistical models outlined in configuration files. When an outlier is detected, the relevant Elasticsearch events are enriched with additional outlier fields, which can then be visualized using powerful tools like Kibana or Grafana.
To illustrate how this framework operates, imagine you are a detective trying to find a criminal in a crowded city, with only a few clues to guide you. The ee-outliers framework acts as your magnifying glass, sharpening your view to uncover suspicious characters (outliers) hiding among the crowd (your Elasticsearch events). With the right configuration, you can identify anomalies like:
- Beaconing (DNS, TLS, HTTP, etc.)
- Geographically improbable activity
- Obfuscated command execution
- Fileless malware execution
- Malicious authentication events
- Processes with suspicious outbound connectivity
- Malicious persistence mechanisms (scheduled tasks, auto-runs, etc.)
The possibilities for anomaly detection are endless! Ready to dive in? Check out the page Getting Started to begin your journey with the ee-outliers framework!
Getting Started with ee-outliers
Preparing to utilize ee-outliers is simple:
- Ensure you have Docker installed and running.
- Set up an Elasticsearch cluster.
- Configure the framework using the user-defined configuration files.
- Run the detection model and start spotting outliers!
Troubleshooting Tips
If you encounter any challenges while using ee-outliers, consider the following troubleshooting suggestions:
- Verify your Elasticsearch connection settings are correct.
- Review your configuration files for any syntax errors.
- Ensure your Docker setup is functioning correctly and that you have sufficient permissions.
- Check the Github repository for any reported issues or updates.
If you’re still having issues, reach out for more help or insights! For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
Contact Information
The ee-outliers framework is developed and maintained by NVISO Labs. If you have questions or need assistance, feel free to create an issue on GitHub or send an email to research@nviso.be.
Licensing and Acknowledgements
The ee-outliers framework is released under the GNU GENERAL PUBLIC LICENSE v3 (GPL-3). We extend our gratitude to INNOVIRIS and the Brussels region for their support in funding our research and development activities. Thank you for choosing ee-outliers, and we look forward to your feedback!
