Commit 780d09c1 by Jun Matsushita

Moving sources to its own repo

parent 95647c07
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# CVE
https://cve.circl.lu/
https://github.com/MISP/MISP
http://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-3942
https://nodesecurity.io/advisories/97
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# GSMMap
## Overview
https://gsmmap.org/
![](../best-practice-protection-measures.png)
## Scores
- Protection Dimension
+ Intercept
+ Impersonation
+ Tracking
- IMSI Catcher Score
## Metrics
- IMSI Catcher Metric
+ The IMSI catcher heuristic calculates an overall score (sum) out of a number of sub-scores. If this overall score exceeds a specified maximum value, an alarm is raised in the app.
- 2G Over-the-air protection
+ Encryption algorithm
* A5/1
* A5/3
+ Padding randomization
+ SI randomization
+ Require IMEI in CMC
+ Hopping entropy
+ Authenticate calls (MO)
+ Authenticate SMS (MO)
+ Authenticate paging (MT)
+ Authenticate LURs
+ Encrypt LURs
+ Update TMSI
- 3G Over-the-air protection
+ Encryption
+ Update TMSI
- HLR/VLR configuration
+ Mask MSC
+ Mask IMSI
## Measurements
- [IMSI Catcher Measurements](https://opensource.srlabs.de/projects/snoopsnitch/wiki/IMSI_Catcher_Score)
+ [A1 - Different LAC/CID for the same ARFCN (Removed)](https://opensource.srlabs.de/projects/snoopsnitch/wiki/IMSI_Catcher_Score#A1-Different-LACCID-for-the-same-ARFCN-Removed)
+ [A2 - Inconsistent LAC](https://opensource.srlabs.de/projects/snoopsnitch/wiki/IMSI_Catcher_Score#A2-Inconsistent-LAC)
+ [A4 - Same LAC/CID on different ARFCNs](https://opensource.srlabs.de/projects/snoopsnitch/wiki/IMSI_Catcher_Score#A4-Same-LACCID-on-different-ARFCNs)
+ [A5 - Lonesome location area](https://opensource.srlabs.de/projects/snoopsnitch/wiki/IMSI_Catcher_Score#A5-Lonesome-location-area)
+ [K1 - No neighboring cells](https://opensource.srlabs.de/projects/snoopsnitch/wiki/IMSI_Catcher_Score#K1-No-neighboring-cells)
+ [K2 - High cell reselect offset](https://opensource.srlabs.de/projects/snoopsnitch/wiki/IMSI_Catcher_Score#K2-High-cell-reselect-offset)
+ [C1 - Encryption Downgrade](https://opensource.srlabs.de/projects/snoopsnitch/wiki/IMSI_Catcher_Score#C1-Encryption-Downgrade)
+ [C2 - Delayed CIPHER MODE COMPLETE ack.](https://opensource.srlabs.de/projects/snoopsnitch/wiki/IMSI_Catcher_Score#C2-Delayed-CIPHER-MODE-COMPLETE-ack)
+ [C3 - CIPHER MODE CMD msg. without IMEISV (removed)](https://opensource.srlabs.de/projects/snoopsnitch/wiki/IMSI_Catcher_Score#C3-CIPHER-MODE-CMD-msg-without-IMEISV-removed)
+ [C4 - ID requests during location update](https://opensource.srlabs.de/projects/snoopsnitch/wiki/IMSI_Catcher_Score#C4-ID-requests-during-location-update)
+ [C5 - Cipher setting out of average](https://opensource.srlabs.de/projects/snoopsnitch/wiki/IMSI_Catcher_Score#C5-Cipher-setting-out-of-average)
+ [T1 - Low registration timer](https://opensource.srlabs.de/projects/snoopsnitch/wiki/IMSI_Catcher_Score#T1-Low-registration-timer)
+ [T3 - Paging without transaction](https://opensource.srlabs.de/projects/snoopsnitch/wiki/IMSI_Catcher_Score#T3-Paging-without-transaction)
+ [T4 - Orphaned traffic channel](https://opensource.srlabs.de/projects/snoopsnitch/wiki/IMSI_Catcher_Score#T4-Orphaned-traffic-channel)
+ [R1 - Inconsistent neighbor list](https://opensource.srlabs.de/projects/snoopsnitch/wiki/IMSI_Catcher_Score#R1-Inconsistent-neighbor-list)
+ [R2 - High number of paging groups](https://opensource.srlabs.de/projects/snoopsnitch/wiki/IMSI_Catcher_Score#R2-High-number-of-paging-groups)
+ [F1 - Few paging requests (removed)](https://opensource.srlabs.de/projects/snoopsnitch/wiki/IMSI_Catcher_Score#F1-Few-paging-requests-removed)
- Protection
## Data
- [data.json](data.json)
## Probe
- https://opensource.srlabs.de/projects/snoopsnitch
## Sources
- https://lists.srlabs.de/pipermail/gsmmap/
- https://opensource.srlabs.de/projects/snoopsnitch
---
layout: index
---
# Sources
> Potential measurement partners
- https://www.eff.org/deeplinks/2012/02/https-everywhere-decentralized-ssl-observatory
> Others
- WebXRay
- HSTS detection
- CVE Search http://cve-search.github.io/cve-search/
- Node Security Project https://github.com/nodesecurity/nsp
- Docker Registry (using CoreOS Clair?) https://hub.docker.com/r/library/node/tags/wheezy/
- [Android Observatory](https://androidobservatory.org)
- PrivacyFix
- privacychoice.org
- privacyscore.com
# Methodology
> Discussion which documents are binding?
> Discussion about the granularity of what is collected and for what purpose.
> Discussion: What does acceptable remedy looks like?
# Data
Evidence data about elements could be reused by OII. Steps for this could be:
- In the *Company*Outcome sheets
- Under each colored group, for each question *Element #*
+ In the sources field, there is a *Element #:* prefix followed by a list of numbers, for each *Source #*
* In the *Company*Sources sheet
- the *Source #* column has the reference, and there is:
+ Document title
+ URL
+ Date of document (often empty)
+ Date accessed
+ In the comment field, there is a *Element #:* with information that explains the score.
Converting to OII:
- *Element #* would be a **Claim** it is prefixed by a yes/no with some details. The original question is not in the spreadsheet but on the website for instance (https://rankingdigitalrights.org/index2015/indicators/c1/) and as Markdown (https://github.com/rankingdigitalrights/index2015/blob/stage/app/_indicators/c1.md)
-
# Potential collaboration items
- Restructure the raw data to make it easier to access evidence/source info.
- Help publish this on the site
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# TOSBack
https://tosback.org/
# Trackography
Could be a data source!
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# WebXRay
Source: https://github.com/timlib/webxray
Viewing Reports
Use the interactive mode to guide you to generating an analysis. When it is completed it will be output to the '/reports' directory. This will contain a number of csv files; they are:
- db_summary: a basic report of how many pages loaded, how many errors, basic stats
- summary_by_tld: gives more stats on how many domains are contacted, cookies, javascript, etc.
- domains-by-tld: the most frequently contacted domains, by tld
- elements-by-tld: most frequent elements, any type
- elements-by-tld-image: most frequent elements, images
- elements-by-tld-javascript: most frequent elements, javascript
- orgs-by-tld: this is the most interesting bit, shows all the top companies who own the domains which are being contacted - relies on the data in webxray/resources/org_domains/org_domains.json which was compiled manually and should be expanded.
- network: pairings between page domains and tracker domains, you can import this info to data viz software to do cool stuff - this is something worth heavy tweaking if it's of particular interest to you!
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