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Analyzer to detect peaks when analyzing multi-attribute telemetry feeds

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Telemetry Peak Analyzer


Telemetry Peak Analyzer is a framework to analyze and detect peaks on telemetry data with multiple dimensions, indexes, and attributes. The analyzer detects meaningful peaks based on statistical measurements computed over a short local window and a longer global window of telemetry data:

  • Local window - a short time data window in which we want to detect peaks of a given attribute or dimension, e.g., file type. During the detection process, the analyzer generates a local statistics table (LST) with all the necessary statistical measurements.

  • Global window - a historical long time data window which serves as a global benchmark to determine if a detected peak within the local window is meaningful. During the detection process, it will generate (or update) a global statistics table (GST) with all the necessary statistical measurements.

Telemetry data is dynamic, therefore the global benchmark as reflected by GST needs to be updated over time. To make the global benchmark adaptive, we use a sliding window mechanism which allows us to quickly update the new GST using previous GST and LST.

Note: this implementation is a generalization of a research tool that was tailored to detect waves of malicious files sharing the same file type; to fully generalize terms and components, the source code relies on the following terms to describe different parts of the telemetry feed:

  • index: a tuple of attributes used to uniquely identify a telemetry data record.
  • dimensions: the attributes used to decompose a time-series into independent and orthogonal time-series.

Such generalization is not perfect (for example, the current implementation does not support more than two dimensions) and some backends have obvious limitations; things will improve as the analyzer supports more types of telemetry data.

Try it out

Build & Run

This package can be installed via pip, just run pip install telemetry-peak-analyzer or pip install -e ..

If you want to install the dependencies required by the tina backend (a custom backend based on Elasticsearch used internally) you should append the [tina] extra option; you might need to use double quotes when doing a dev install, i.e., pip install -e ".[tina]"; note that a valid configuration file might be required. See data/config.ini.template for an example.

Extra backends might require private dependencies; if that is the case, remember to select the internal index server using the -i option; if you require access, contact one of the maintainers.


This package includes a console script ready to be used. Examples:

  • python -m telemetry_peak_analyzer -b telemetry_peak_analyzer.backends.JsonBackend -n "./data/telemetry_example_*" -t 10: in this example the peak analyzer reads from some local files using the JSON backend (note the double quotes) and sets the threshold to 10; note that when -t is specified, it will overwrite any suggested global threshold defined in GST.
  • python -m telemetry_peak_analyzer -c config.ini -b telemetry_peak_analyzer.backends.tina.TinaBackend -n tina_nlemea -d 2: in this example the peak analyzer reads from Tina from the last 2 days of data, using the configuration file config.ini, and the section tina_nlemea to know how to connect to the backend.


There are a number of JSON files in the data directory for test using the JSON backend. Note that all the test files have been completely anonymized, to the point that even file hashes do not refer to actual files anymore.

As mentioned above, the analyzer detects peaks based on statistical measurements of both a local window and a global window. In the detailed example, the process comprises two steps.

  1. python -m telemetry_peak_analyzer -n ./data/telemetry_example_3.json -s 2020-11-01 –e 2020-11-04

This step generates an initial GST table as global benchmark from the defined initial global window, as specified by -s and -e options in the command. This step is only required the first time the analyzer is executed. Subsequent runs will update the GST using previously computed GST and LST tables.

Expected output:

test@localhost telemetry-peak-analyzer % python -m telemetry_peak_analyzer -n ./data/telemetry_example_3.json -s 2020-11-01 -e 2020-11-04
INFO -> [2021-09-15 12:00:11] [0010mb] Loading Peak Analyzer from 2020-11-01 00:00:00 to 2020-11-04 00:00:00 with t=None
INFO -> [2021-09-15 12:00:11] [0010mb] Loading backend 'JsonBackend'
INFO -> [2021-09-15 12:00:11] [0010mb] Loaded files:
INFO -> [2021-09-15 12:00:11] [0010mb]  /Users/test/telemetry-peak-analyzer/data/telemetry_example_3.json
INFO -> [2021-09-15 12:00:11] [0010mb] Loading analyzer 'FileTypePeakAnalyzer' with backend 'JsonBackend'
INFO -> [2021-09-15 12:00:11] [0010mb] Loading global tables from file '/Users/test/telemetry-peak-analyzer/global_table.json'
INFO -> [2021-09-15 12:00:11] [0010mb]  Failed: [Errno 2] No such file or directory: '/Users/test/telemetry-peak-analyzer/global_table.json'
INFO -> [2021-09-15 12:00:11] [0010mb] Loading global tables from the backend
INFO -> [2021-09-15 12:00:12] [0012mb] Loading local tables
INFO -> [2021-09-15 12:00:12] [0013mb] Getting peaks
INFO -> [2021-09-15 12:00:12] [0013mb] Refreshing global tables
INFO -> [2021-09-15 12:00:12] [0013mb] Saving global tables to '/Users/test/telemetry-peak-analyzer/global_table.json'

As the output shows, the process creates a JSON file global_table.json which is the initial GST table containing the global statistics.

  1. python -m telemetry_peak_analyzer -n ./data/telemetry_example_3.json -s 2020-11-04 –e 2020-11-05

This step will finally detect peaks from a local window (as specified by -s and -e options) by leveraging the statistics in the GST and LST tables. This run will also update the GST (ideally, in production, you want to execute this second command on a daily basis to minimize the data to be processed).

Expected output:

test@localhost telemetry-peak-analyzer % python -m telemetry_peak_analyzer -n ./data/telemetry_example_3.json -s 2020-11-04 -e 2020-11-05
INFO -> [2021-09-15 12:00:46] [0010mb] Loading Peak Analyzer from 2020-11-04 00:00:00 to 2020-11-05 00:00:00 with t=None
INFO -> [2021-09-15 12:00:46] [0010mb] Loading backend 'JsonBackend'
INFO -> [2021-09-15 12:00:46] [0010mb] Loaded files:
INFO -> [2021-09-15 12:00:46] [0010mb]  /Users/test/telemetry-peak-analyzer/data/telemetry_example_3.json
INFO -> [2021-09-15 12:00:46] [0010mb] Loading analyzer 'FileTypePeakAnalyzer' with backend 'JsonBackend'
INFO -> [2021-09-15 12:00:46] [0010mb] Loading global tables from file '/Users/test/telemetry-peak-analyzer/global_table.json'
INFO -> [2021-09-15 12:00:46] [0010mb] Loading local tables
INFO -> [2021-09-15 12:00:46] [0015mb] Getting peaks
INFO -> [2021-09-15 12:00:46] [0015mb] TelemetryPeak(malicious, ZipArchiveFile)
INFO -> [2021-09-15 12:00:46] [0015mb]  sub_count: 11083
INFO -> [2021-09-15 12:00:46] [0015mb]  samp_count: 3028
INFO -> [2021-09-15 12:00:46] [0015mb]  samp_sub_count_max: 426
INFO -> [2021-09-15 12:00:46] [0015mb]  samp_sub_count_mean: 3.66
INFO -> [2021-09-15 12:00:46] [0015mb]  samp_sub_count_std: 11.54
INFO -> [2021-09-15 12:00:46] [0015mb]  samp_sub_ratio: 0.04
INFO -> [2021-09-15 12:00:46] [0015mb]  global_samp_sub_count_max: 2
INFO -> [2021-09-15 12:00:46] [0015mb]  global_threshold_suggested: 629
INFO -> [2021-09-15 12:00:46] [0015mb] Refreshing global tables
INFO -> [2021-09-15 12:00:46] [0015mb] Saving global tables to '/Users/test/telemetry-peak-analyzer/global_table.json'

As the output shows, it loads the GST generated from the 1st step, and successfully detects a ZipArchiveFile-based peak within the local window, and prints out some key statistical measurements generated during the detection process.

At the end of the process, the GST table gets updated.


The telemetry-peak-analyzer project team welcomes contributions from the community. Before you start working with telemetry-peak-analyzer, please read our Developer Certificate of Origin. All contributions to this repository must be signed as described on that page. Your signature certifies that you wrote the patch or have the right to pass it on as an open-source patch. For more detailed information, refer to


Create the virtual env:

python3 -m venv venv

Activate the virtual env:

source ./venv/bin/activate

Install tox:

pip install tox

Run tests:


Due to a bug in tox if you update the dependencies in setup.cfg the environments will not be re-created, leading to errors when running the tests (see As workaround, pass the --recreate flag after updating the dependencies.

Before committing, install the package in dev mode (needed by pylint) following the instructions detailed in the Build & Run section.

Then install pylint and pre-commit:

pip install pylint pre-commit

Install the hook:

pre-commit install

If you want to run pre-commit on all files use the following command:

pre-commit run --all-files


BSD 2-Clause


The peak analyzer can be used to detect network peaks too. In order to use it for this purpose, while calling the peak analyzer, the -a option should be used to specify that the NetworkTypePeakAnalyzer is to be used. The -b option should specify the backend to be used, i.e. the NetworkTinaBackend.

Due to the much larger volume of network peaks in a given time range as compared to file peaks, it is recommended to use the -e and -d parameters to specify the date and time range.

Test command: python -m telemetry_peak_analyzer -c ./data/config.ini -a telemetry_peak_analyzer.analyzers.NetworkTypePeakAnalyzer -b telemetry_peak_analyzer.backends.tina.NetworkTinaBackend -n tina_westus -e 2022-04-01 -d 1 -o ./output.json

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