July 2019 - anti-censorship-team - lists.torproject.org

Next steps for unclassifiable protocols
by Roger Dingledine 04 Oct '20

04 Oct '20

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… 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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Best (simple) obfs4 deployment url?
by Roger Dingledine 07 Aug '19

07 Aug '19

Hi Philipp! I am finishing up my Defcon slides, and I realized I don't know where to point people for setting up obfs4 bridges. Google sends me to https://support.torproject.org/operators/operators-6/ but I bet that's not up-to-date (but maybe it should become up-to-date). On the tor-relays list I see https://trac.torproject.org/projects/tor/wiki/doc/PluggableTransports/obfs4… which is (a) a really long url, and (b) an intimidatingly long page. And I hear irl is working a 'tor-bridge' meta-deb: https://bugs.torproject.org/31153 So: assuming I want to tell a bunch of people to do something in a week and a half, using a url they can hastily scribble down from my slide, what should I tell them? :) It is ok if the url doesn't say the right thing today but we're confident it will say the right thing on the morning of Aug 8. Thanks, --Roger

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The MASQUE circumvention protocol
by Philipp Winter 01 Aug '19

01 Aug '19

I found a more extensive description of the MASQUE protocol: <https://davidschinazi.github.io/masque-drafts/draft-schinazi-masque.html> Here are my key take-aways after reading the draft: * MASQUE enables circumvention by hiding a circumvention proxy behind a web server, similar to Sergey's httpsproxy. Clients "unlock" the web server's circumvention feature by using the newly-proposed HTTP Transport Authentication Standard: <https://tools.ietf.org/html/draft-schinazi-httpbis-transport-auth-00> In a nutshell, clients need to send a CONNECT request with a transport authentication header to .well-known/masque/initial. Crucially, MASQUE defends against active probing by responding with "405 Method Not Allowed" to failed authentication attempts -- the same response one would get for an unexpected CONNECT request. This prevents censors from learning if a web server supports MASQUE. * Once a client "unlocked" a MASQUE server, one can tunnel several types of traffic over the MASQUE server. One can use it as an HTTP proxy, send DNS requests, and do both UDP and TCP proxying. * MASQUE supports QUIC over HTTP/3 and TLS 1.3 over HTTP/2. There is a fallback mechanism from HTTP/3 to HTTP/2 to provide a disincentive for censors to block QUIC or HTTP/3. * MASQUE only provides obfuscation and does not provide anonymity. The document suggests onion routing in Section 2.4 to work around this shortcoming. * MASQUE does not defend against traffic analysis but QUIC supports padding, so there's a mechanism to mitigate this problem. Traffic analysis defence is left for future work in Section 7.2. * QUIC has a "connection migration" feature that allows clients to seamlessly switch end-to-end connections from one MASQUE server to another. * Similar to onion services, MASQUE makes it possible to make available a server behind NAT by using a MASQUE server as a rendez-vous mechanism. There's an IETF mailing list for the project: <https://mailarchive.ietf.org/arch/browse/masque/> MASQUE may make a good pluggable transport and we should engage early to make this happen. I intend to suggest this idea on their mailing list soon. Cheers, Philipp

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Resources related to user testing and screen recording
by David Fifield 14 Jul '19

14 Jul '19

These are in response to a conversation with phw and cohosh about observing how people use Tor Browser. Here are links relating to the 2015 UX Sprint, at which we did one-on-one observations of a few users. https://blog.torproject.org/ux-sprint-2015-wrapup https://trac.torproject.org/projects/tor/wiki/org/meetings/2015UXsprint The videos of the sessions are here: https://people.torproject.org/~dcf/uxsprint2015/ The source code for our screen recording setup is here. I'm attaching the README. https://www.bamsoftware.com/git/repo.eecs.berkeley.edu/tor-ux.git Here's a rough description of the whole process. We ran user experiments over two days. We were hampered by late recruiting (Berkeley only approved the experiment literally the day before) and we only had five participants, three on Saturday and two on Sunday. But that number turned out to be plenty because of how labor-intensive the process was. We had all the devs in a room downstairs with a projector. The experiments were run upstairs in a smaller room on a laptop. What we did was screencast the laptop screen to the projector downstairs (using VLC streaming through an SSH tunnel). Developers could watch user interactions live, and we also recorded the whole thing. We audio recorded the subjects' voices on handheld audio recorders (we encouraged them to think out loud as they were using the software). The idea was to transcribe the audio and add it to the screen recordings as subtitles. That was a huge amount of work, but the videos are really interesting and enlightening. We gave participants the choice of a Windows or Mac laptop. It happens that all of the chose Mac, though we had Windows ready to go. Windows was a big pain to set up with VLC and SSH; I might do that differently a second time. We originally planned to run up to two experiments simultaneously. That would have been way too hectic. It's a lot of work greeting people, doing paperwork, doing the experiment, making sure you reset the computer in between experiments--we really needed an extra person downstairs all that time. With more researchers (i.e., people allowed to interact with participants) it would be possible. We budgeted one hour of one-on-one time with each participant. It ended up taking between 15 and 30 minutes of actual experiment for each (that's how long the resultant videos are), but that doesn't count all the paperwork and setup time. 60 minutes of actual experiment would have been too long. It could be that long if it were not one on one. But also, there was high variance in how long things took to complete. Some users took longer to install than others. Another took a long time trying to find a specific UI element. The repo for our PETS 2017 paper is here: https://github.com/lindanlee/PETS2017-paper The screen capture videos from a pilot session of recording are here: https://github.com/lindanlee/PETS2017-paper/tree/master/sessions/pre/videos Scripts relating to setting up the simulated censorship firewall are here: https://github.com/lindanlee/PETS2017-paper/tree/master/experiment

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"Dissecting Tor Bridges" NDSS'17 paper
by Philipp Winter 10 Jul '19

10 Jul '19

I skimmed Matic et al.'s NDSS'17 paper "Dissecting Tor Bridges: a Security Evaluation of Their Private and Public Infrastructures": <https://censorbib.nymity.ch/pdf/Matic2017a.pdf> Below are the points that stood out the most to me. Note however that the study is from 2017 and some numbers may no longer be valid. * With the exception of China and the U.S., default bridges served by far the most users. The popularity of default bridges makes them a single point of failure that is easy for censors to block. We have been losing default bridges over the last few months and should be recruiting new operators: <https://trac.torproject.org/projects/tor/wiki/doc/TorBrowser/DefaultBridges> We have a list of criteria for new operators at the bottom of the page. If you are interested in running a default bridge, please get in touch. * Four OR ports (443, 8443, 444, and 9001) were used much more often than others. Today, 19% of bridges use port 9001 and 17% use port 443. Port 9001 is problematic because it is an attractive target for Internet-wide scans. So is port 443 but it has some merit for users who seek to circumvent corporate firewalls. Is there any reason at all to have bridges listen on port 9001? If not, we should ask these operators to pick a new port. * If a censor discovers a bridge and the bridge runs an SSH server, the censor can fetch its fingerprint and use Shodan to find other SSH servers with the same fingerprint. These servers may also be running bridges. Bridge-specific SSH keys would fix this problem. We may want to create a "bridge opsec guide" for subtle issues like these. * Shodan and Censys are search engines for Internet-wide scans. The authors were successful in using these datasets to find 35% of "public" bridges, i.e., bridges that publish their server descriptor to the bridge authority. The idea is to look for certificates that resemble a Tor bridge and then actively probe the port to confirm this suspicion. This is a tricky balancing act: most bridges should probably avoid the ports that Shodan and Censys scan but we need a few of them for users whose firewalls whitelist these ports. * Many bridges run transports that are both resistant *and* vulnerable to the GFW's active probing attacks. The vulnerable protocols are a liability to the resistant protocols. We fixed this issue in BridgeDB (#28655) but it remains a problem for Internet-wide scans: if a censor discovers a bridge via a port 9001 scan, obfs4's probing-resistance doesn't help. The need to haven an open OR port (#7349) remains a painful issue. Also, wouldn't it be useful to have a mechanism to instruct bridges to stop serving a transport? At this point, there is no reason to still serve obfs2, obfs3, or ScrambleSuit. Cheers, Philipp

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