Blog Entries

19. 09. 2022 Davide Sbetti Log-SIEM, Machine Learning

Elasticsearch ML Models and Inference: Real-Time Classification

In a previous article, we explored the Machine Learning capabilities of Elasticsearch, which allowed us to apply anomaly detection techniques to our data, and helped us discover some really interesting facts as a result of our analysis. But can we take that idea even further? For instance, could we use data we’ve already collected to…

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29. 03. 2022 Davide Sbetti Machine Learning, NetEye

Data Exploration in Kibana: from a Simple Visualization to Anomaly Detection

These days we live in a data-driven world, where the collection and analysis of data empowers not only companies but also individuals to plan future actions based on the information that is extracted. NetEye enables both the collection and analysis of an enormous amount of data using various platforms, such as Kibana, for data written…

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22. 03. 2022 TobiasGoller Icinga Web 2, Machine Learning, NetEye

Performance Metrics Collection from NetEye to Elastic

Today my objective is to collect the performance metrics from various NetEye Icinga checks and write them out to Elasticsearch so I can apply Machine Learning (ML) algorithms to identify potential anomalies. This is a task that’s not possible with the open-source version of InfluxDB installed in NetEye. Moreover, this data in Elastic is used…

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19. 05. 2020 Nicola Degara Anomaly Detection, Log Management, Machine Learning, NetEye, Unified Monitoring, Webinar

Neteye SIEM: processes and tools for IT Services Security 🇮🇹

Talking about IT security is now clearly synonymous with resilience! We are continuously and inevitably under attack… every organization must implement defensive principles and practices that avoid the worst damage and the least impact on its survival and development. From the data selection, to its collection and normalization, for its representation and analysis with techniques…

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17. 10. 2019 TobiasGoller ITOA, Log-SIEM, Machine Learning, NetEye

Experiences with Netflow and Machine Learning in Elastic

Some time ago I was able to use the machine learning functionality in Elastic for the first time. I was astonished at how easy it is to use, and how fast it calculates historical data. In my particular case, I loaded Netflow data into the Elastic database. I wanted to use this data to evaluate…

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02. 08. 2017 Susanne Greiner Anomaly Detection, Machine Learning, NetEye, Real User Experience

Next Level Performance Monitoring – Part II: The Role of Machine Learning and Anomaly Detection

Machine learning and anomaly detection are being mentioned with increasing frequency in performance monitoring. But what are they and why is interest in them rising so quickly? From Statistics to Machine Learning There have been several attempts to explicitly differentiate between machine learning and statistics. It is not so easy to draw a line between…

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08. 06. 2015 Susanne Greiner Machine Learning, Real User Experience

Statistics and Machine Learning Techniques for RUE

In the age of the cloud the optimization of real end-user experience (RUE) is getting essential for success. On the one hand users expect applications to work faultlessly independent of the time, location, and device one is using them from. Application performance monitoring (APM) is therefore often based on RUE performance metrics. On the other…

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