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2025-02-20
As data exploration continues to grow in complexity, the tools used to query and visualize large datasets must evolve. Elastic’s release of version 8.17.0 introduced a powerful feature—the technical preview of MATCH and query string (QSTR) functions within ES|QL. This enhancement aims to make log searches in Kibana faster and more intuitive, a game-changer for those working with datasets such as DShield Honeypot logs.
In this article, we’ll explore how to leverage ES|QL in Kibana to efficiently query DShield Honeypot logs. By showing examples of queries based on user names and IP addresses, we’ll walk you through the process of building customized queries and visualizations in Kibana’s powerful platform.
Summary
Elastic’s new ES|QL functionality in Kibana introduces streamlined query options, significantly improving data exploration and analysis. Released with Kibana 8.17.0, it offers functions like MATCH and QSTR, which make querying log data much simpler and more user-friendly. This blog post focuses on demonstrating how ES|QL can be used to query DShield Honeypot logs, especially in terms of identifying top user names and actor IP addresses.
The first example query highlights how to count occurrences of a specific field (user.name) within the logs, sort them in descending order, and limit the result to the top 10 entries. This allows for a clear overview of the most active users in the logs.
The second example demonstrates querying for the top 10 actor IPs that were recorded over a 7-day period by the DShield Honeypot. By adjusting visualization types, axes, and colors, users can tailor the data presentation to suit their specific needs, further enhancing the analytical value.
In Kibana, after typing a query, ES|QL offers contextual suggestions, helping users explore the language’s potential without needing to memorize all available functions. These capabilities open up a wide range of possibilities for anyone working with DShield Honeypot logs or similar datasets.
What Undercode Say:
Elastic’s move to introduce ES|QL into Kibana with version 8.17.0 represents a significant step forward in the ease and power of querying Elasticsearch data. Historically, Kibana’s query language has been relatively technical and sometimes cumbersome for new users, especially when dealing with complex datasets. ES|QL addresses these challenges by adding simpler syntax and autocomplete features, which make it easier to write queries and experiment with different datasets.
For security analysts or anyone using DShield Honeypot data, this release is incredibly valuable. DShield collects a wealth of security-related data, including IP addresses and other metadata from honeypots designed to trap malicious activity. Having a streamlined query tool such as ES|QL makes it much easier to drill into this data, uncover patterns, and draw meaningful conclusions. No longer do analysts need to spend an inordinate amount of time crafting complex queries in the default query languages. With ES|QL, they can focus on results, not syntax.
One of the biggest advantages of ES|QL is its ability to instantly suggest field options as users type. This speeds up the process of writing queries, reduces errors, and allows users to explore data in a more fluid way. The query suggestions also help new users get comfortable with Elasticsearch’s structure without needing to be experts. This means that analysts can focus on the critical task of analysis, without being bogged down by query construction.
Let’s break down the two queries provided in the article to better understand how they work. The first query is designed to count the occurrences of a specific field (user.name) across the DShield Honeypot logs. This is particularly useful for identifying which users are most active, which could be a sign of either legitimate activity or, depending on the context, a security concern (such as an account being targeted or attacked).
“`sql
FROM cowrie
| KEEP user.name
| STATS COUNT(user.name) BY user.name
| SORT `COUNT(user.name)` DESC
“`
By using the KEEP keyword, the query filters the data to only include the user.name field. The STATS COUNT function then counts the number of occurrences of each unique user, while the SORT command orders these counts in descending order, allowing you to quickly identify the top users. Limiting the results to just the top 10 entries (LIMIT 10) can help focus on the most relevant data, keeping things manageable and efficient.
The second query looks at the top 10 actor IP addresses that have been logged over the last seven days. This is especially useful when investigating malicious activity, as unusual or unexpected IP addresses might indicate an attack. Here’s the query:
“`sql
FROM cowrie
| KEEP related.ip
| STATS COUNT(related.ip) BY related.ip
| SORT `COUNT(related.ip)` DESC
| LIMIT 10
“`
The related.ip field is the focus here, which stores the IP addresses of the actors interacting with the honeypot. Again, the STATS COUNT function counts the number of occurrences of each unique IP address, and the query sorts them in descending order. This is helpful in identifying patterns in attack traffic or the volume of interaction from specific IPs. Visualizing this data could reveal interesting trends, such as a spike in malicious activity from certain geographical regions.
Analytical Insights:
ES|QL’s addition to Kibana not only simplifies querying but also enhances the overall experience for analysts working with security datasets like DShield Honeypot logs. DShield’s data provides key insights into internet security by collecting logs from multiple honeypots, and with the rise in sophisticated cyberattacks, such data is increasingly valuable for threat intelligence teams.
With the of ES|QL, Kibana users can now interact with this data more efficiently. Analysts are no longer required to spend hours manually crafting complex queries or dealing with cumbersome syntax errors. ES|QL’s user-friendly features allow even those new to data exploration to build powerful queries quickly. This lower barrier to entry makes it easier for teams to stay on top of emerging threats and quickly pivot when something suspicious appears in their data.
Moreover, ES|QL is incredibly powerful because it integrates seamlessly into Kibana’s visualization tools. By querying DShield Honeypot logs through ES|QL, analysts can not only view the raw data but also visualize it in a more meaningful way, such as through graphs or pie charts. The customization of these visualizations adds an additional layer of flexibility, allowing analysts to present the data in a way that best supports their findings and reports.
However, as with any new tool or feature, there are limitations to consider. While ES|QL is intuitive for simpler queries, it might not be as flexible for highly complex analytics where more granular control over Elasticsearch queries is necessary. Advanced users might still prefer writing raw queries in Elasticsearch’s native query language for certain tasks that require intricate filtering or advanced joins.
Overall, the of ES|QL in Kibana is a highly welcomed development for anyone working with Elasticsearch data, especially security analysts using DShield Honeypot logs. With its simplicity, speed, and integration with Kibana’s powerful visualization capabilities, ES|QL offers a promising way to make data analysis faster, more efficient, and more accessible to a wider range of users. As organizations continue to grapple with increasingly sophisticated cyber threats, tools like ES|QL will be essential in providing actionable insights from vast amounts of security-related data.
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