loglizer
loghub
| loglizer | loghub | |
|---|---|---|
| 2 | 5 | |
| 1,228 | 1,530 | |
| 1.5% | 3.0% | |
| 0.0 | 5.3 | |
| 13 days ago | 4 days ago | |
| Jupyter Notebook | ||
| MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
loglizer
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SOC with machine learning
https://github.com/logpai/loglizer has an MIT license. Seems like they've done some of the heavy lifting already. If you're just looking for logs, check out https://github.com/logpai/loghub.
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how to never ever lose connection to raspberry pi
If you want to really get paranoid, then you can write a monitoring app that uses machine learning to do the log analysis and detect anomalies in your system. There are some open source tools available, like this for example. Also you can train the network for your specific use case and then just have the service running the inference on your logs and a pre-trainer model that is running on system logs. Then you really get in paranoid mode.
loghub
- Security+ 601 - Where to learn READING LOGS?
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SOC with machine learning
https://github.com/logpai/loglizer has an MIT license. Seems like they've done some of the heavy lifting already. If you're just looking for logs, check out https://github.com/logpai/loghub.
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PSA: PLEASE learn data structures and algorithms. The amount of people in DevOps who can't code is too damn high!
Here is an example apache log fie: If you want to try and prove me wrong write a script to find and return line numbers which contain the string "Directory index forbidden" or some other substring which is better then O(N).
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Any dataset that could be interesting to analyze using text network analysis?
Any text? How about Loghub? Maybe you could visualize links in error messages for failure analysis, for example does a seemingly harmless warning seem to be linked to a much worse problem that occurs later?
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Mining metrics from unstructured logs
Here is a summary for a log from the logpai/loghub dataset (kudos to the Logpai team for sharing this dataset):
What are some alternatives?
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
coroot-aws-agent - A prometheus exporter that gathers metrics from AWS services.
Fog - An open source computer cloning & management system
HELK - The Hunting ELK
wazuh-ruleset - Wazuh - Ruleset
scrapydweb - Web app for Scrapyd cluster management, Scrapy log analysis & visualization, Auto packaging, Timer tasks, Monitor & Alert, and Mobile UI. DEMO :point_right:
anomaly-detection-resources - Anomaly detection related books, papers, videos, and toolboxes
logparser - A machine learning toolkit for log parsing [ICSE'19, DSN'16]
luminol - Anomaly Detection and Correlation library
coroot-node-agent - A Prometheus exporter based on eBPF that gathers comprehensive container metrics
logparser - A library and a CLI tool for clustering unstructured logs.