| Commit message (Collapse) | Author | Age |
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Fixed errors for bigendian platforms in ndpiReader.
All address and port comparisons and hash calculations are done with
endian in mind.
The get_ndpi_flow_info() function searched for an existing flow for the
forward and reverse direction of the packet.
The ndpi_workflow_node_cmp() function looked for a flow regardless of
the packet's direction. This is what led to an error in determining the
direction of transmission of the packet.
Fixed error in "synscan" test: the number of packets in the forward and
reverse direction is incorrectly defined (verified via tcpdump).
Fixed bug with icmp protocol checksum check for big endian platforms.
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Differentiate between Google its own apps/services and Google Cloud.
We already do something similar for Amazon vs AWS and Microsoft vs Azure.
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leading cybersecurity companies and CDNs, useful to make destinations that should be marked as trusted in firewalls and security gateways
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Removed attic directory now obsolete
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* Sync utest results
* Fix read-heap-buffer-overflow error reported by CI
See: https://github.com/ntop/nDPI/runs/5055876515?check_suite_focus=true
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Add detection of AccuWeather site/app and Google Classroom.
Improve detection of Azure, Zattoo, Whatsapp, MQTT and LDAP.
Fix some RX false positives.
Fix some "Uncommon TLS ALPN"-risk false positives.
Fix "confidence" value for some Zoom/Torrent classifications.
Minor fix in Lua script for Wireshark extcap.
Update .gitignore file.
Let GitHub correctly detect the language type of *.inc files.
Zattoo example has been provided by @subhajit-cdot in #1148.
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Detected by oss-fuzz
See: https://oss-fuzz.com/testcase-detail/6730505580576768
Fix a function prototype
Update a unit test results
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As a general rule, the higher the confidence value, the higher the
"reliability/precision" of the classification.
In other words, this new field provides an hint about "how" the flow
classification has been obtained.
For example, the application may want to ignore classification "by-port"
(they are not real DPI classifications, after all) or give a second
glance at flows classified via LRU caches (because of false positives).
Setting only one value for the confidence field is a bit tricky: more
work is probably needed in the next future to tweak/fix/improve the logic.
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See:
https://www.apple.com/privacy/docs/iCloud_Private_Relay_Overview_Dec2021.PDF
TODO: an up-to-date list of egress IP ranges is publicly available. Can
we use it somehow?
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Improve Microsoft, GMail, Likee, Whatsapp, DisneyPlus and Tiktok
detection.
Add Vimeo, Fuze, Alibaba and Firebase Crashlytics detection.
Try to differentiate between Messenger/Signal standard flows (i.e chat)
and their VOIP (video)calls (like we already do for Whatsapp and
Snapchat).
Add a partial list of some ADS/Tracking stuff.
Fix Cassandra, Radius and GTP false positives.
Fix DNS, Syslog and SIP false negatives.
Improve GTP (sub)classification: differentiate among GTP-U, GTP_C and
GTP_PRIME.
Fix 3 LGTM warnings.
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Co-authored-by: Lucas Santos <lucas.santos@zerum.com>
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Signed-off-by: Toni Uhlig <matzeton@googlemail.com>
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The goal is to have a (roughly) idea about how many packets nDPI needs
to properly classify a flow.
Log this information (and guessed flows number too) during unit tests,
to keep track of improvements/regressions across commits.
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Add support for Likee app (https://likee.video/) and GitLab
Improve detection of Google domains ("gtv1.com" added in 6dd42d19f was
likely a typo)
Improve Snapchat and Tiktok detection
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Signed-off-by: Toni Uhlig <matzeton@googlemail.com>
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