[anti-censorship-team] Next steps for unclassifiable protocols

Roger Dingledine arma at torproject.org
Fri May 31 05:02:35 UTC 2019


Hi folks,

Here are some brainstorming thoughts to get us started improving our
"unclassifiable protocols" idea.

----------------------------------------------------------------------

For context, right now we have essentially one deployed design in this
space, which is obfs4:
https://gitweb.torproject.org/pluggable-transports/obfs4.git/tree/doc/obfs4-spec.txt
This protocol does a short handshake (one client message, one server
message) to establish session keys, then it just sends bytes back and
forth with an (additional) layer of encryption so no payload bytes
assist an attacker with doing content-based protocol classification.

----------------------------------------------------------------------

One of the more thorough analyses of obfs4 comes from the FTE folks:
https://www.freehaven.net/anonbib/#ccs2015obfsdetection
where they show a classifier that assesses entropy of the first
server-side packet, plus looking at the length of that initial message,
and they can consistently pick out obfs4 flows with only a 0.25% false
positive rate compared to background traffic they recorded at their
university.

That false positive rate is where it gets interesting: it sounds good
until we realize that it means it flags 1 in every 400 flows as being
obfs4, i.e. it falsely tags about 36000 flows in the 14M flow dataset.
And the false positives aren't randomly distributed either: 1.22% of
their SSL flows are falsely tagged as obfs4, and it's the ones where
the SNI in the SSL handshake has a lot of entropy (because everything
else in the SSL handshake is already random). Also, entire protocols
like RTMP get caught up and tagged as obfs4, because their handshake
starts out with a block of random bytes.

A key takeaway is that if they ran this classifier, with its high true
positive rate and low false positive rate, on a large country's network
traffic, approximately none of the flows that it would tag as obfs4 would
actually be obfs4. Their Section 7 in that paper has a good discussion
of the base rate issues and how they apply here.

Are there other papers that design or analyze classifiers for obfs4?

----------------------------------------------------------------------

The good news: It would seem that obfs4 is most effective in the "long
tail of varied traffic" scenario. That is, considering the spectrum
between "small corporate network" and "China backbone", obfs4 needs that
more broad background traffic in order to make its false positives too
painful to block.

The bad news: I still worry about an attack that puts together many
building blocks, each of which individually is like the "1% false
positive rate" classifier in the above paper, but that together drive
their false positive rate low enough that blocking is safe to do. One
observation there is that the more complexity there is to a protocol,
the harder it is to "really" look like it, i.e. to match it in all
dimensions at once.

Consider this lovingly handcrafted graph, where the X axis is how
thoroughly we try to look like some expected protocol, and the Y axis
is how close we can get to making the adversary unwilling to block us:

1_|                                                     |_1
  |                                                     |
  |\                                                   /|
  | \                                                 / |
  |  \                                               /  |
  |   \                                             /   |
0_|    \___________________________________________/    |_0
  |                                                     |

There's a huge valley in the middle, where we're trying to look like
something, but we're not doing it well at all, so there is little damage
from blocking us.

The ramp on the right is the game that FTE tries to play, where in theory
if they're not perfectly implementing their target protocol then the
adversary can deploy a distinguisher and separate their traffic from
the "real" protocol, but in practice there are enough broken and weird
implementations of the protocol in the wild that even being "close enough"
might cause the censor to hesitate.

And the ramp on the left is our unclassifiable traffic game, where we
avoid looking like any particular expected protocol, and instead rely
on the long tail of legitimate traffic to include something that we
blend with.

Observation: *both* of these ramps are in the "broken in theory, but works
in practice" situation. I started out thinking it was just the obfs4 one,
and that the FTE one was somehow better grounded in theory. But neither
side is actually trying to build the ideal perfect protocol, whatever
that even is. The game in both cases is about false positives, which
come from messy (i.e. hard to predict) real-world background traffic.

One research question I wrestled with while making the graph: which ramp
is steeper? That is, which of these approaches is more forgiving? Does one
of them have a narrower margin for error than the other, where you really
have to be right up against the asymptote or it doesn't work so well?

For both approaches, their success depends on the variety of expected
background traffic.

The steepness of the right-hand (look-like-something) ramp also varies
greatly based on the specific protocol it's trying to look like. At first
glance we might think that the more complex the protocol, the better
you're going to need to be at imitating it in all dimensions. That is,
the more aspects of the protocol you need to get right, the more likely
you'll slip up on one of them. But competing in the other direction is:
the more complex the protocol, the more broken weird implementations
there could be in practice.

I raise the protocol complexity question here because I think it
has something subtle and critical to do with the look-like-nothing
strategy. Each dimension of the protocol represents another opportunity
to deploy a classifier building block, where each classifier by itself
is too risky to rely on, but the composition of these blocks produces
the killer distinguisher. One of the features of the unclassifiable
protocol that we need to maintain, as we explore variations of it, is
the simplicity: it needs to be the case that the adversary can't string
together enough building-block classifiers to reach high confidence. We
need to force them into building classifiers for *every other protocol*,
rather than letting them build a classifier for our protocol.

(I'll also notice that I'm mushing together the notion of protocol
complexity with other notions like implementation popularity and
diversity: a complex proprietary protocol with only one implementation
will be no fun to imitate, but the same level of complexity where every
vendor implements their own version will be much more workable.)

----------------------------------------------------------------------

I've heard two main proposed ways in which we could improve obfs in
theory -- and hopefully thus in practice too:

(A) Aaron's idea of using the latest adversarial machine learning
approaches to evolve a traffic transform that resists classifying. That
is, play off the classifiers with our transform, in many different
background traffic scenarios, such that we end up with a transform
that resists classifying (low true positive and/or high false positive)
in many of the scenarios.

(B) Philipp's idea from scramblesuit of having the transform be
parameterized, and for each bridge we choose and stick with a given
set of parameters. That way we're not generating *flows* that each
aim to blend in, but rather we're generating bridges that each aim to
blend differently. This diversity should adapt well to many different
background traffic scenarios because in every scenario some bridges
might be identified but some bridges will stay under the radar.

At first glance these two approaches look orthogonal, i.e. we can do both
of them at once. For example, Aaron's approach tells us the universe of
acceptable parameters, and Philipp's approach gives us diversity within
that universe.

Aaron: How do we choose the parameter space for our transforms to
explore? How much of that can be automated, and how much needs to be
handcrafted by subject-matter-experts? I see how deep learning can produce
a magic black-box classifier, but I don't yet see how that approach can
present us with a magic black-box transform.

And as a last note, it would sure be nice to produce transforms that
are robust relative to background traffic, i.e. to not be brittle or
overfit to a particular scenario. Otherwise we're giving up one of our few
advantages in the arms race, which is that right now we force the censor
to characterize the traffic -- including expected future traffic! --
and assess whether it's safe to block us.

There. Hopefully some of these ideas will cause you to have better
ideas. :)

--Roger



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