  Transport protocols use congestion control to avoid overloading a network.
Nowadays, different congestion control variants exist that influence
performance. Studying their use is thus relevant, but it is hard to identify
which variant is used. While passive identification approaches exist, these
require detailed domain knowledge and often also rely on outdated assumptions
about how congestion control operates and what data is accessible. We present
DeePCCI, a passive, deep learning-based congestion control identification
approach which does not need any domain knowledge other than training traffic
of a congestion control variant. By only using packet arrival data, it is also
directly applicable to encrypted (transport header) traffic. DeePCCI is
therefore more easily extendable and can also be used with QUIC.
