'Wazuh Quickstart Erroring on wazuh-indexer install
I am just finding wazuh and wanted to test it out. I have tried the step by step and the scripted. Cannot get it to work. So I am consolidating my deployment to just 1 VM and am trying to run the quickstart to get going. When I try to install I get the below error. I cannot find anything related to wazuh-indexer 4.3.1-1. Thoughts?
10/05/2022 12:52:37 INFO: Created wazuh-install-files.tar. It contains the Wazuh cluster key, certificates, and passwords necessary for installation. 10/05/2022 12:52:37 INFO: --- Wazuh indexer --- 10/05/2022 12:52:37 INFO: Starting Wazuh indexer installation. Updating Subscription Management repositories. EL-8 - Wazuh 75 kB/s | 3.4 kB 00:00 No match for argument: wazuh-indexer-4.3.1-1 Error: Unable to find a match: wazuh-indexer-4.3.1-1 10/05/2022 12:52:48 ERROR: Wazuh indexer installation failed. 10/05/2022 12:52:48 INFO: --- Removing existing Wazuh installation --- 10/05/2022 12:52:48 INFO: Installation cleaned. Check the /var/log/wazuh-install.log file to learn more about the issue.
Solution 1:[1]
The root of that error is that the script is trying to download
wazuh-indexer-4.3.1-1 instead of the current wazuh-indexer-4.3.0-1.
It seems that the production script included this bug for a short period of time. Please download the script and install it again.
Sorry for the inconvenience.
Solution 2:[2]
You are correct. After waiting a few days, the bug was fixed. But am now hitting a new bug. When trying to run filebeat setup --dashboards. I get an error: Exiting: Kibana API is not available in Kibana version 1.2.0
I have the setting:
compatibility.override_main_response_version: true
on my opensearch.yml but still no avail.
Solution 3:[3]
If you are using the wazuh-install script, it is not required to perform any further configuration.
In order to troubleshoot this issue, could you please provide us with the following information:
- Architecture: Single host or multi-node
- Installation method: Step by step installation
- Documentation page you follow to install your environment
- OS of your nodes
I recommend you to join Wazuh slack channel (https://wazuh.com/community/) and move this issue there, or into the Wazuh google group in order to be able to correctly monitor this problem
Solution 4:[4]
Yes you can use k-means to predict clusters. Once you have clustered your training data, you will receive cluster centers for the chosen number of clusters. E.g., if you have chosen k=3, your dataset will be divided into 3 clusters and hence you will receive 3 cluster centers.
Therefore, now you can take your test data and for each test data point you can find the euclidean distance among the the three cluster centers. The one for which the distance is minimum will be the predicted cluster for you.
If you are using scikit-learn there is also a predict method with K-Means, which should do the above basically.
Solution 5:[5]
The KMeans Cluster is unsupervised ML model. That means there won't be any labelled data for training and prediction also. It takes training data and based on model tuning it tries cluster the training data and assign cluster labels for each cluster.
And on this trained model you can pass values so that it predicts the optimal cluster label for given input. Here is example python code snippet.
import numpy as np
import matplotlib.pyplot as pyplot
from sklearn.cluster import KMeans
from sklearn.preprocessing import scale
model = KMeans(n_clusters=2)
model = model.fit(scale(data)) # data is your training data
print(model.labels_) # prints labels for clusters. you can map to meaningful labels
model.predict(scale(test)) # test is your data to predict the cluster
Sources
This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.
Source: Stack Overflow
| Solution | Source |
|---|---|
| Solution 1 | Rebits |
| Solution 2 | Tyler2P |
| Solution 3 | Rebits |
| Solution 4 | der Fotik |
| Solution 5 | Murali D |