# Call Detail Record (CDR) Interpreter **URL:** \ **Category:** Discussion & Solutions\ **Tags:** solved\ **Created:** [September 11, 2026, 9:00am UTC](https://onehack.st/t/call-detail-record-cdr-interpreter/325476 "2026-09-11T09:00:35Z")\ **Posts on this page:** 5\ **Page:** 1
**Author:** ![Wantok](https://onehack.st/user_avatar/onehack.st/wantok/32/24960_2.png) [@Wantok](https://onehack.st/u/Wantok)\ **Post date:** [September 11, 2026, 9:00am UTC](https://onehack.st/t/call-detail-record-cdr-interpreter/325476/1 "2026-09-11T09:00:35Z")
Since there are many free tools available, may I kindly ask if any team member knows a CDR tool that are available for free to interpret MSISDN call detail records? ---
**Author:** ![Ze380y](https://onehack.st/user_avatar/onehack.st/ze380y/32/48760_2.png) [@Ze380y](https://onehack.st/u/Ze380y)\ **Post date:** [September 16, 2026, 3:54pm UTC](https://onehack.st/t/call-detail-record-cdr-interpreter/325476/2 "2026-09-16T15:54:42Z")
**Free tools that read and explain call detail records** πŸ“ž **Drop the records in, get answers out β€” free programs and methods for MSISDN call detail records, zero setup first.** **πŸ‘ Instant view (nothing to install)** - **Call Log Explorer** β€” browser tab, drop the file, charts appear. πŸ”— [github.com/soumendrak/call-log-explorer](https://github.com/soumendrak/call-log-explorer) - **ChatGPT** β€” upload the file, ask in plain words, tables back. πŸ”— [help.openai.com](https://help.openai.com/en/articles/8437071-advanced-data-analysis) - **Claude** β€” upload 30 MB files, ask plain questions. πŸ”— [support.claude.com](https://support.claude.com/en/articles/8241126-upload-files-to-claude) - **Gemini** β€” upload 100 MB files, summaries and charts. πŸ”— [support.google.com](https://support.google.com/gemini/answer/14903178) - **[AnalyzeData.io](http://AnalyzeData.io)** β€” no signup, plain English report. πŸ”— [analyzedata.io](https://analyzedata.io/) **🧹 Messy files first** - **CleverCSV** β€” fixes broken delimiters. πŸ”— [github.com/alan-turing-institute/CleverCSV](https://github.com/alan-turing-institute/CleverCSV) - **CSV-Cleaner** β€” one timestamp format, in bulk. πŸ”— [github.com/dfirvault/CSV-Cleaner](https://github.com/dfirvault/CSV-Cleaner) **πŸ“¦ The dedicated free tools** - **CCAT v2** β€” stats, top contacts, tower coords, KML path. πŸ”— [github.com/Crimealysis/CDR-Analyzer](https://github.com/Crimealysis/CDR-Analyzer) - **CDR-Stats** β€” search, group, dashboards with maps. πŸ”— [github.com/cdr-stats/cdr-stats](https://github.com/cdr-stats/cdr-stats) - **CDRViewer 3** β€” portable, 45+ formats. πŸ”— [blue-tail.com/cdrviewer-3](https://blue-tail.com/cdrviewer-3/) - **cellyzer** β€” Python plus no-code GUI, routes and maps. πŸ”— [github.com/anjuchamantha/cellyzerβ€”CDR-data-analyzer](https://github.com/anjuchamantha/cellyzer---CDR-data-analyzer) - **gigaTrace** β€” self-hosted suite, contact graph, SQL. πŸ”— [github.com/SU1199/gigaTrace](https://github.com/SU1199/gigaTrace) - **TATVA** β€” multi-source platform, timelines, dossier. πŸ”— [github.com/rio-ARC/TATVA-Forensic-Investigation](https://github.com/rio-ARC/TATVA-Forensic-Investigation) - **RLEAPP** β€” carrier returns into timeline reports. πŸ”— [github.com/abrignoni/RLEAPP](https://github.com/abrignoni/RLEAPP) - **cdr-mapper** β€” tower sites into KML. πŸ”— [github.com/danzek/cdr-mapper](https://github.com/danzek/cdr-mapper) - **Mobipack** β€” clean and map trajectories. πŸ”— [github.com/SpatialDataCommons/CDR-analysis-tools-std](https://github.com/SpatialDataCommons/CDR-analysis-tools-std) - **bandicoot** β€” 1442 phone indicators plus visuals. πŸ”— [github.com/computationalprivacy/bandicoot](https://github.com/computationalprivacy/bandicoot) - **CDR\_Analyzer** β€” Python CLI, frequent callers, locations. πŸ”— [github.com/Vega-4n6/CDR\_Analyzer](https://github.com/Vega-4n6/CDR_Analyzer) - **Cell-Data-Viewer** β€” tower CSVs on a map, fully local. πŸ”— [github.com/NerdTech-one/Cell-Data-Viewer](https://github.com/NerdTech-one/Cell-Data-Viewer) **🧭 Twin and shape tools** - **chroniq** β€” offline CSV timeline in the browser. πŸ”— [github.com/thisislola/chroniq](https://github.com/thisislola/chroniq) - **4n6time-go** β€” desktop timeline viewer. πŸ”— [github.com/CdtDelta/4n6time-go](https://github.com/CdtDelta/4n6time-go) - **pink-lemonade** β€” data grid, pivots, SQL. πŸ”— [github.com/forynsics/pink-lemonade](https://github.com/forynsics/pink-lemonade) - **Timesketch** β€” collaborative timeline analysis. πŸ”— [github.com/google/timesketch](https://github.com/google/timesketch) - **Gephi** β€” caller-callee columns become the map. πŸ”— [gephi.org/quickstart](https://gephi.org/quickstart/) - **Pivot manual** β€” analyst method in one PDF. πŸ”— [policetechnical.com](https://policetechnical.com/wp-content/uploads/2012/04/Pivot-Table-Manual.pdf) **πŸ“– Read the meaning** - **pi-labs method** β€” frequency, towers, IMEI links. πŸ”— [pi-labs.ai](https://www.pi-labs.ai/cdr-analysis-investigators-call-records/) - **Royal Palm playbook** β€” 10-step reading method. πŸ”— [royalpalminvestigations.com](https://royalpalminvestigations.com/how-to-read-a-call-detail-record-report-a-pis-guide-for-attorneys/) - **Bellingcat case** β€” worked phone-metadata example. πŸ”— [bellingcat.com](https://www.bellingcat.com/resources/2020/12/14/navalny-fsb-methodology/) - **Bellingcat toolkit** β€” free OSINT directory and community. πŸ”— [bellingcat.gitbook.io](https://bellingcat.gitbook.io/) - **i3Forum voice fraud** β€” practitioners sharing guidance. πŸ”— [i3forum.org](https://i3forum.org/workgroups/voice-fraud/) **πŸ”“ Binary and odd formats** - **MARBEN decoder** β€” upload raw binary, get readable XML. πŸ”— [marben-products.com](https://www.marben-products.com/decoder-asn1-cdr/) - **[asn1.io](http://asn1.io) inspector** β€” in-browser decoder, schema ready. πŸ”— [asn1.io/CDR-inspector](https://asn1.io/CDR-inspector/) - **tap3edit** β€” roaming TAP3 to XML and back. πŸ”— [tap3edit.github.io](https://tap3edit.github.io/) - **tap3xml** β€” C++ CLI for the same job. πŸ”— [github.com/tap3edit/tap3xml](https://github.com/tap3edit/tap3xml) - **3GPP 32.298** β€” every field’s exact meaning. πŸ”— [etsi.org 32.298](https://etsi.org/deliver/etsi_ts/132200_132299/132298/17.08.00_60/ts_132298v170800p.pdf) - **3GPP 32.297** β€” transfer rules companion. πŸ”— [etsi.org 32.297](https://www.etsi.org/deliver/etsi_TS/132200_132299/132297/19.00.00_60/ts_132297v190000p.pdf) - **GSMA TAP3 TD.57** β€” roaming dictionary. πŸ”— [pdfcoffee.com TD.57](https://pdfcoffee.com/td57-v365-tap-format-specs-pdf-free.html) **πŸ—Ί Towers become maps** - **OpenCellID** β€” tower IDs to coordinates. πŸ”— [opencellid.org](https://www.opencellid.org/downloads) - **CellMapper** β€” crowd coverage map. πŸ”— [cellmapper.net/map](https://www.cellmapper.net/map) - **uMap** β€” drop CSV/KML, see the path. πŸ”— [umap-project.org](https://umap-project.org/) - **Google Earth** β€” satellite view of the same path. πŸ”— [google.com/earth](https://www.google.com/earth/versions/) **πŸ“± Your own records** - **TelstraCallToCSV** β€” carrier API into clean CSV. πŸ”— [github.com/Capta1nT0ad/TelstraCallToCSV](https://github.com/Capta1nT0ad/TelstraCallToCSV) - **Logger app** β€” device log exported on-device. πŸ”— [f-droid.org logger](https://f-droid.org/packages/com.logger.app) **πŸŽ“ Practice first** - **CRAWDAD sample** β€” 142 days of real calls. πŸ”— [ieee-dataport.org](https://ieee-dataport.org/open-access/crawdad-ctupersonal) - **MPD lab** β€” hands-on course with real dataset. πŸ”— [github.com/leoferres/mpdtutorial](https://github.com/leoferres/mpdtutorial) - **krzdur/cdr** β€” worked course code. πŸ”— [github.com/krzdur/cdr](https://github.com/krzdur/cdr) - **NIST CFReDS** β€” documented training corpora. πŸ”— [cfreds.nist.gov](https://cfreds.nist.gov/) - **DFRWS challenges** β€” skill tests on real data. πŸ”— [dfrws.org](https://dfrws.org/) **⚑ Start:** drop the file into Call Log Explorer tonight. ---
**Author:** ![Indianapolis](https://onehack.st/user_avatar/onehack.st/indianapolis/32/150586_2.png) [@Indianapolis](https://onehack.st/u/Indianapolis)\ **Post date:** [October 4, 2026, 12:30pm UTC](https://onehack.st/t/call-detail-record-cdr-interpreter/325476/3 "2026-10-04T12:30:06Z")
Yes β€” for mobile-operator CDRs containing MSISDN, start with a local/offline tool rather than uploading subscriber data to a random website. **Free options** **Python CDR Analyzer β€” best starting point** β†’ Open-source GitHub project β†’ Imports CSV files β†’ Searches frequent callers and contacts β†’ Supports IMEI, dates, coordinates, cell locations, call type, and inbound/outbound analysis β†’ Expected fields include `PartyA`, `PartyB`, `IMSI`, `IMEI`, `LogDate`, `LAT`, `LNG`, `ADDR`, and call type β†’ Runs locally with Python, so it does not require a cloud upload. ```bash git clone https://github.com/Vega-4n6/CDR_Analyzer.gitcd CDR_Analyzerpip install -r requirements.txtpython CDRAnalyzer.py ``` **No-code option β€” Excel or LibreOffice** For a small or medium CSV: 1. Import the file. 2. Keep MSISDN columns as **text** , not numbers. 3. Convert date/time into one proper timestamp column. 4. Create pivot tables for: - Most frequent contacts - Total calls by day/hour - Incoming vs outgoing calls - Total duration by MSISDN - Calls by cell ID or location - IMEI changes over time Excel can filter structured CDR files and use pivot tables/charts for call-pattern analysis; LibreOffice Calc can handle the same basic workflow for free. **Larger data sets β€” CDR-Stats** CDR-Stats is open-source and provides browser-based reports, mediation, rating, alarms, fraud-pattern analysis, and multi-source processing. It is primarily designed for **Asterisk, FreeSWITCH, Kamailio, SIP, and other VoIP CDRs** , so it may require field mapping before it works with a mobile-network MSISDN export. **Important distinction** ```plaintext Mobile CDR β†’ MSISDN, IMSI, IMEI, cell ID, tower, SMS, locationVoIP CDR β†’ SIP user, caller ID, destination, codec, MOS, packet loss ``` CDR-Stats is strong for VoIP. For raw mobile-network records, the Python analyzer or a custom pandas dashboard is usually the better fit. **Minimum useful column mapping** | **CDR meaning** | **Common column names** | | --- | --- | | Main subscriber | `MSISDN`, `PartyA`, `Calling_Number` | | Other party | `PartyB`, `Called_Number`, `B_Number` | | Date/time | `Call_Start`, `LogDate`, `Call_Originating_Time` | | Duration | `Duration`, `Duration_Seconds` | | Direction | `IN`, `OUT`, `Inbound`, `Outbound` | | Device | `IMEI` | | Subscriber identity | `IMSI` | | Tower/site | `Cell_ID`, `CGI`, `LAC`, `Site_ID` | | Location | `LAT`, `LNG`, `ADDR` | | Event type | `CALL_TYPE`, `SMS`, `DATA` | **My pick** - **Under 100,000 rows:** Excel or LibreOffice - **100,000–5 million rows:** Python + pandas - **Large recurring telecom imports:** CDR-Stats or a custom PostgreSQL dashboard - **Need maps and investigation timelines:** a local CDR analyzer with cell-site coordinates Only process CDRs you are authorized to handle, and keep real MSISDN, IMSI, IMEI, and location data offline. Use dummy numbers when testing or asking for technical help. ---
**Author:** ![Grace2077](https://onehack.st/user_avatar/onehack.st/grace2077/32/179796_2.png) [@Grace2077](https://onehack.st/u/Grace2077)\ **Post date:** [October 23, 2026, 2:50am UTC](https://onehack.st/t/call-detail-record-cdr-interpreter/325476/4 "2026-10-23T02:50:44Z")
Ze380y’s list covers the dedicated tools really well β€” I’ll add a workflow note from the AI side, since that’s where I do my first pass. When I get a carrier CSV export, I do three things before asking any model anything: (1) normalize the timestamps to one format β€” mixed timestamp formats are the number one reason model summaries come back wrong; (2) split files over the upload limit into chunks by date range (Claude takes around 30 MB, Gemini around 100 MB) so nothing gets silently truncated mid-file; (3) strip or hash the actual phone numbers if the data is real and sensitive β€” cloud models are great for analysis, but I don’t upload raw PII to them. Then I ask for structured outputs, not prose: top 10 contacts by call count and total duration, a time-of-day histogram, and a list of numbers that only ever receive calls but never make them. Those three questions surface most of the interesting patterns on the first pass. And I always spot-check the model’s numbers against a quick manual filter in the spreadsheet β€” models occasionally miscount when the CSV has quoted commas or multi-line fields. For repeat work, I save the prompt that worked as a template and reuse it. The dedicated tools are better for maps and timelines, but for β€œwhat’s the story in this file” the chatbot pass saves me the most time. ---
**Author:** ![Ze380y](https://onehack.st/user_avatar/onehack.st/ze380y/32/48760_2.png) [@Ze380y](https://onehack.st/u/Ze380y)\ **Post date:** [October 23, 2026, 3:27am UTC](https://onehack.st/t/call-detail-record-cdr-interpreter/325476/5 "2026-10-23T03:27:30Z")
# 🎧 Your call log is a piece of music, a constellation, and a diary @Wantok β€” @Grace2077’s three questions are the right ones, and @Indianapolis already handed you the local parser. This is the fun half: five ways to look at a CDR that no dashboard on the internet has, and every one of them is free. 🎯 ## 🎧 Hear the pattern instead of reading it Timestamps are time, and time is rhythm. Map the hour to a note and the duration to its length, and a call log becomes music: - **[TwoTone](https://twotone.io/)** β€” built with the Google News Initiative: load a CSV, choose which column becomes pitch, hit play. You hear the day - **[DataSonifyer](https://studio.datasonifyer.de/en)** β€” the same idea with a different scale, no account - **[Sonic Pi’s sonification cookbook](https://github.com/Decibels-Sonification/sonification-resources/blob/main/SonicPi-cookbook.md)** β€” when you want to compose it yourself, with the normalise function ready to copy A number that calls every nine minutes sounds wrong instantly. On a table of 4,000 rows, you would never see it. ## 🌌 Draw it without writing a line of code - **[RAWGraphs](https://www.rawgraphs.io/)** β€” 30 chart models, drag your columns onto the axes, export SVG. **Your data never leaves the browser** , which matters when every row is a phone number - **[OpenRefine](https://openrefine.org)** β€” the quiet hero: cluster `+92 300…`, `0300…` and `+92300…` into one person before you analyse anything ## πŸ•³ The one nobody thinks of: the silence The gaps between calls are the interesting part. A phone that goes quiet for nine hours was off, asleep, or somewhere else. Plot the gap length and you get a **diary** : ```sql SELECT timestamp, timestamp - LAG(timestamp) OVER (PARTITION BY msisdn ORDER BY timestamp) AS silent_for FROM cdr ORDER BY silent_for DESC LIMIT 20; ``` The longest silences are the nights. The pattern of them is a sleep schedule, a shift pattern, or a different time zone. ## πŸ€– The bot radar Calls with **zero duration in both directions** are a machine. Count them per number, then look at who they dialled β€” robocallers, IVR systems and auto-diallers all have the same signature, and it takes one query to separate them from people. ## πŸ—Ί And the map that started this Cell IDs become coordinates through **[OpenCelliD](https://opencellid.org)** or **[beaconDB](https://beacondb.net)**, then **[kepler.gl](https://kepler.gl)** animates the trail. One month of records becomes a movement video. πŸ—Ί ## πŸ§ͺ The join worth keeping ```sql SELECT a.msisdn, b.msisdn, a.cell_id, a.timestamp FROM cdr a JOIN cdr b ON a.cell_id = b.cell_id AND a.msisdn < b.msisdn AND ABS(strftime('%s', a.timestamp) - strftime('%s', b.timestamp)) < 300; ``` Same cell, five minutes apart β€” the query behind β€œwere these two people actually together”. > **The two habits that make any of this trustworthy** > > Normalise every timestamp into one column, one format, one time zone. Keep MSISDN and IMSI as **text** so the leading zeros survive the import. > > Then hash the numbers before a file leaves your machine, and work only on records you are authorised to hold. Same analysis, and the rest of the file stays useful.