Hello and good morning and welcome to this third edition of Exchange for Media's Streaming Media Summit. After two years of pandemic, we are back on ground with this initiative and as many of you know, the Streaming Media Summit is part of a full day of events that also includes the Exchange for Media Play Awards, which start at 6 o'clock in the evening today. I have with me the country head of Amazon Prime Video today, Gaurav Gandhi, who has been in this hot seat for a good part of the last four, five years and Gaurav is here today to talk to me about what's happening in the OTT space, in the OTT ecosystem in India, how it has grown in the last few years, especially during the pandemic, how the OTT industry has changed the shape of the content business, and what's in store as far as Amazon Prime Video in India is concerned. So with that, thank you Gaurav for joining us. Thank you, Gaurav. It's always a pleasure talking to you. So Gaurav, let me get straight into this and ask you, tell us how has the Streaming landscape evolved in India in the last five years that Prime Video has been around? I think it's been pretty interesting overall for the industry. They have been tailwind in the industry for streaming for a long time. The fact that we have a young demographic, you know, overall, who's akin to watching more VOD content. We are largely a single TV household market, which basically meant that the ubiquitous smartphone became the TV at home for everyone, cheapest data in the world. And then of course, fascinating story around content that we've seen across languages. So with such a robust content industry and the enablers is no doubt that we will sort of get to the point we have gotten to as streaming in India and we're just getting started as an industry. I think for us at Prime Video, it's been great five years as well. Our journey has really been about basically super serving our delightfully diverse audience base in India. The fact that India is diverse is known to everybody. But the fact is from day one, thinking about programming across ten languages, how we build a combination of original content and licensed content around that. We've been super serving with obviously our total originals, our local originals, our movies across ten languages, and then even localized products. For example, Prime Video Mobilization, which is an India specific you know, launch that we did now it rolled out globally, a marketplace model that we've taken out with channel partners to basically give more selection to our customers. All of these have been enablers in the super serving. The interesting part, what you see in our list that with all of that, the industry on subscription side has grown significantly. Prime Video today is watched in 99% of countries pin codes. We have seen our content travel not just locally but globally. Today customers can enjoy that in over 200 countries and territories. Two very interesting things I must state here is that as the business has grown and our metrics have grown as well and we've done really well, the impact we've had on the overall creative economy, we're very humbled and happy with that. First, we've actually played a role in expanding the linguistic parrot of customers in the country. Today 50% of our customers are watching content in four languages. You know, if you talked about pandemic, your introduction and you talked about that saying one big change that's happened really is the fact that customers have been far more open now to watching content beyond the native language. So with that, it expands the selection of content that the customers can watch but also expands the reach for all creators. The second big change is about the fact that this industry overall, and we have played a role in that as well, is about giving fresh and new talent an opportunity. And I think today with, you know, 50% of talent of our originals to date and going forward 70%, if you're looking at his new talent behind the camera in front of the camera. So I think these combinations, along with the fact that, you know, we've grown significantly, make us very happy about our performance the last five years and overall how are the industries going? But like we said, Amazon, it's always day one. So getting started. Yeah, it's fantastic what you've managed to achieve in these five years and, you know, we are all, we've all experienced firsthand the linguistic sort of expansion of content viewership that has taken place especially in the last two years. I, for one, took on to watching a lot of Malayalam content on Prime Video. We've seen how Telgu cinema has really gone mass and, you know, mainline in India. And the reverse is also becoming true. You know, yesterday Akshay Kumar's movie Prithviraj was announced where they announced that they'll be releasing the movie in four languages. So it is really cross-polished, pollination and expansion of creative talent happening across India. The plate sort of becoming bigger, the canvas becoming wider can only sort of board really well for the creative industry, the platforms and ultimately for the consumer. Let me now come to, you know, what Prime Video is up to in India. Tell us what is Prime Video's vision for India and where does India specifically fit into the larger scheme of things for the company globally? Yeah. I think Prime Video's vision in India and around the world is to be the most loved entertainment hub for customers. And when I say entertainment hub, it means that we will obviously program and acquire and create lots of originals and movies and licensed films and shows for, you know, our customers or Prime members. But we also look at how we can add to that selection by building the entertainment marketplace. So as we brought to the channels program that we launched last year and having many of our partners come on board to offer their content through our channels program, we have recently launched our movie rental service, you know, which is part of our marketplace program again. So between all of this, you know, between what we do for our Prime members and exclusive original programming and then our marketplace program, we want to be the entertainment hub for customers. Beyond that, we also want to be the home for talent. And we believe that, you know, giving new talent and established talent a voice and opportunity to showcase what they have. And also the fact that we play a role of being an enabler in getting all that they produce and create to the customers is a very important and responsible role that we feel ourselves, you know, accountable for, for both the talent and to the customers. I think in all of that, as I was mentioning in the previous question you asked, that we also play a role of, you know, enabling the creative economy. And we think that all of us in the streaming business, you know, we've all sort of contributed to it. But in the foundational years of this industry, we have really played the role of giving unprecedented reach to content. And so as Prime Video, when we look at this, we also look at the fact that how can we get that content that a creator is making to as many customers as possible? You know, so we, we take some of these things as our core responsibility. And that's how we think about it. Your second part of the question about where India stands, I think India is a very important market and locale for Prime Video and for Amazon overall. And we are deeply invested in creating, you know, great personal content and offering a selection to our customers. The India business is doing well. You know, we see that India is one of the highest portions of customers who Prime members were for watching video every month. We also see India as a market where, you know, you know, people are joining Prime Video, Prime Program for Prime Video. We've seen last year, you know, the most customers who actually joined and watched Prime Video coming from India. So we are overall very excited about our journey here. And like I said, it's getting started. Content, as you sort of have mentioned plays the, you know, most pivotal part in the life of an OTT platform. And you've just announced a slate of 40 new releases over the next two years across various languages. As I mentioned earlier, you've done significant sort of play in the content space, language space in the last few years, Malayalam, Telugu, not just looking at Hindi and English, but, you know, expanding the other parts of the content pieces as well. How are you looking to build the kind of the language content slate for, you know, your library? And the other part of the question is, are you also looking at, for example, non-Hindi language content traveling across models?  So when we program for, you know, for India, we think about, hey, we started our journey with 10 languages and we're saying like, how do we make sure that the customer of a particular language gets the right choice of content for them? And we therefore look at, you know, do we have, you know, local movies that we can get for the customer from that market? Then we say we, how do you build the original program for it? But we then go beyond and say, can we make sure that we enhance the reach of content of one language to the other as well? And the interesting part I can share with you is, you know, the local language films have seen enormous reach outside the home state. In fact, 50% of their viewing has come from outside the home state.  So when you look at a selection for say a customer, you know, whose preferred language is Hindi, there is the Hindi local language originals we'll create, we'll create, we'll acquire Hindi local language films, we will look at how other languages can be made available to that customer to access either through subtitles or dubs. We look at international content that can be made available through localization or otherwise available with subtitles again. And then we look at, you know, how that selection is sort of suited to your taste. Because this business is about segmentation and about the fact that I want to create, you know, right content for the right cohort of customers. It's not that, you know, you can offer, it's not about like, like I keep saying, it's not about a broadcast to a billion, it's about unicast, a billion unicasts, right? In that context, what your taste is and what your mood is, your preferences, you know, could change and could be different than mine. And therefore, your selection strategy for our originals has been like that, we will create a Bandish Bandit and we'll also create a Made in Heaven. That's right, right? So we'll create a, you know, a modern love and a family man. And that width of content selection, similarly on the film side, you know, you will see the plethora of films that we, you know, we license and we are creating now. So all of that is to make sure the diverse taste of our customers in each language is catered to. And because of the geographical and linguistic barriers going away, the choice for customers is expanded. So now when you're creating a local language content, are you creating, you know, about a slate, when we're creating a show, like sort of, we are thinking about, you know, how can that show be appealing to not just customers who like to watch Tamil content, but actually to customers across India and around the world, just the same way as we think about when you create family man, like how can be available to and watch around the world. And internationally, particularly we already see 20% of our customers who watch our Indian regionals outside India. So I think that's really what's been keeping us busy and we excited about the opportunity of how this content can really cross all kinds of linguistic and geographical borders. Yeah, very interesting, you know, bits of angles in that answer, especially with regards to how sort of content has traversed borders. And, you know, you have examples today where, you know, brands always used to follow the strategy of hiring different brand ambassadors for different regions. And now you have, you know, many cases where Southern movie star is now a national brand ambassador, a sort of, you know, a star who's predominantly worked in the, you know, Hindi belt in the North Indian market is also now showcased very well across the southern markets, where, you know, the primary language is different, you have cases like Samanta Prabhu, a big star in the south who's done family man, and you know, has been part of a Hindi predominantly Hindi series and, you know, appeal to audiences in the Hindi belt. Let me come to the, you know, other part of the content piece which is movies and, you know, should we all know prime video primarily started as a, you know, licensed movie service and then it grew significantly to where it is. And, you know, offering direct to movie premier during pandemic grew.  Yeah, we live in a country which is, which is blessed to have the most amazing creators. And, you know, the fact of the matter is we create between all languages about 2000 films a year. We also live in a country which there are people who really love their movies and crazy about films and what overall the experience of watching movies. Between this, there has been, you know, the, the gap of access. And the gap of access was the fact that just 9000 screens for over a billion people to watch films. And it makes up screen density at eight per million compared to say 40 China 120 or 28 for us so on. That is also the fact that most folks can actually go and watch a film in theaters or can't watch many films in theater than movies, you know, don't get adequate window and so on. And so our journey on prime video has been about really how try to be a cinema at home, a theater at home for customers to be able to watch these movies started with early access film during four to eight weeks across languages. We then, during the pandemic, realized that there was, you know, films available to release. And there were customers wanted to watch content and we then played the role of actually getting these films to customers. And it actually was, I think, a defining moment. But at that moment was refining for the industry at large and not just for prime video. Because what I did was it gave us, it gave so much confidence to create us that the film can reach even a bigger scale and an audience base that they could imagine. And, you know, when you could, you know, watch and films like Sher Shah, the films like, sorry, films like Sher Shah, films like Surai Po through Trisham do and many, many others have demonstrated that. And what's amazing about that is that they've gone into not just the, you know, deepest parts of the country, cross language barriers, but borders, but actually have been big hits internationally. Indian movies were never getting released in so many countries and territories around the world. But you get to watch these movies in 200 countries and territories in the first couple of weeks of launch. Now that gave a lot of confidence to people, saying that there's no longer a world of OTT movies or streaming movies and world of theatrical movies, that world blended together beautifully. So we could give that reach and confidence and we played a role in that part and others played a role too. So that was a big moment. And as we go forward, you know, our journey in investing behind, you know, these great films is about how can we get more such films and get out to customers and we actually believe theaters and streaming coexist in that role. It's not either or it's and and in that end, there will be films that go to theaters first and come to Prime Video, there will be films that come directly to us and just on the supply side as well. You know, we have, we've looked at licensing films, we are now doing co-productions, we are going into original movies now. So it's really the fact that we want to make sure we get as much films and output as possible and then we make sure we give customers choice there. And my last point on that film is that our latest movie rental service that we've launched is one more way for customers to access films. So it's really enabling at both ends. On demand side, you're giving customers choice how they want to take it and the supply side investing behind the film in various models. That's how we're thinking about the film business. Yeah, interesting you say that because you know movies really as opposed to four, five years back when OTT really started growing in India and there was a tussle with producers and theater owners, I think all parties, especially the sort of production outside of the business has realized that you know working together has significantly more benefits and it's not a sort of you know adversary sort of stance one has to take and movies being on OTT has significantly helped the movie business and when the movie business gets sort of benefited, the entire value chain including the theatrical business benefits from that. Absolutely. In fact, in fact, two important examples is the fact that so many films were available to be released but couldn't release, capital was blocked, people had invested years of you know blood and sweat into creating those films to get them to customers to make sure people can enjoy it to commercially add value to the chain allows producers and creators to make more films which can then release on theatrical or streaming or both. Similarly, as we go forward in co productions, we are actively we are absolutely enabling the fact that our partners can actually go make you know make the films in religion theaters. So we actually believe it's a completely synchronous business which runs you know helping each other overall and it's not either or at all and look at the rebound you know once movies are back in theaters people are sort of headed back to watch it on the big screen and I think I will not be wrong in saying that that OTT's have played a very big part in this rebound because you know they've kept the content sort of pipeline going, they made sure that enough investment kept going into the sort of movie business, they made sure that you know producers had incentive to make good quality content despite the fact that you know earnings were very restricted from the box office the sort of theatrical release and now that you know we the pandemic is behind us, theaters have reopened, we have seen significantly more footfalls you know one small example is people tell me PVR share price just kept going up irrespective of what's happening in the you know broader economy. I give you another example of how this manifests of the role of OTT. If you look at this year amongst the three big films theatrically Pushpa, RRR and KGF are films that have become national hits and they are originally from a local language and they have gone well beyond that language into appeal across the country and I feel OTT's and streaming series have a role in acceptance of movies across languages and have been enablers of course these movies are great content I'm just saying them we put a role in changing customer habits and being more accepting of watching content outside your native language preferred language so I think it's really helps it's symbiotic because these great films that come on streaming Pushpa was a great example when came on streaming and became as big a hit for us as well so I think this really works well. Yeah I mean we all saw KGF one because we were at home and it was available on OTT having said that Bahubali happened you know before the pandemic and it was a very big hit but the fact remains that so much exposure to you know non-hindi language content happened because we were at home and that really you know was the precursor for people going into theatres and watching you know the next sort of lot of that content. Let me ask you a little about the marketplace model Gaurav and tell us a little more about what the what is the objective it is supposed to serve and what is the marketplace model about. I think the marketplace model for us is solving for two things one it is about giving our customers access to content that they will love beyond what we are able to create or able to offer them from the Prime program and we do a lot we invest a lot in that but we also understand customers want to watch more shows and more movies so we want to give them more wider selection particularly with channels program like I mentioned to you so with all the channel partners we get them on board we also solve for the channel partners to say we are able to give them reach of you know a prime program and able to make sure that they have here's a customer base which is a paying customer base who loves their content it gives ubiquitous device coverage to all our partners easy payment options and therefore and makes the life of our customers better and our partners better so it's really bring those pieces together the on the our movie rental program that we just launched it's solving for you know the point I mentioned to you earlier of the fact that there is supply of content and there is demand for it but there always been a challenge in terms of the infra around it how do you get there so we played like I said we played a role of getting the movies to theaters to sorry to streaming after theaters here is one more way for customers to choose their film get early access to films you know after theatrical and and sometimes before they come on on the subscription services and or they give access to films way beyond subscription programs for example our movie rental service is available to even non-prime members people who are not prime yet so the idea is about like how can we get great content to customers and give multiple choices and options for them to choose so it's really about that let me ask you a broader question now about what's happening in the you know larger industry where do you think the industry is headed over the next five years though you know the there is no sort of two ways about the future growth potential of the OTT business but give us a slightly more nuanced view of you know uh how is this growth going to happen and what is this growth what are the trends that this growth will sort of you know create which will be long lasting I think um you know when people ask me about the industry and say you know tell me how's the five years been and you know where are we headed like I say that we should write report cards for business that's so nascent and so yeah you know in its early stages it's generally the industry is off to a great start I think that all the sectoral trends that we see the tailwinds are only getting stronger for the for the industry um two or three key things from our point of view that we look at one that business models will continue to evolve and iterate uh you know we see in India the avod model the s-word model the hybrid model now we'll we have a transactional model on top so we'll have all of those models working people are approaching video from different lenses you have you know telecom companies having video plays you have um broadcasters having their own streaming play you can have a dth service saying I want to have online and of course you have pro play pure play streaming services irrespective of the models video is the center of it and that and center of that really is the heart of this content business so if you look at content ends what you really see is what we talked about a little bit the fact is the language barrier if it exists it's not diminished already will completely go away if you look at next few years you will just have limitless choice and you will just want to be like if I I need to connect with content so that that is disappearing and disappear more with that geographical borders itself are also of for content boundaries for content um you know uh move away because you we can also almost see we are on the cusp of you know a point where you can have a Indian Indian series hit which is global truly global you're already seeing massive appeal for some of our shows and movies uh so we are seeing that third is you know you talked briefly about brand ambassadors earlier about you will have a different brand ambassador we are seeing truly national stars you see truly national creators whatever to create content for without thing I'm actually from this language of this day you actually see that and you will see them international as well um so on the content side and that is what's we're seeing we're also seeing the acceptance of our customers to try different kinds of content so we are as you would have seen in our slate we have docu series coming we have programming coming from across genres and we are creating from horror to supernatural to all of that genres which were earlier not being done at all um and because it's segmented because it's very focused to a customer segment and then can appeal beyond you know you can go and actually super serve with more of what you would would would allow so I think the experimentation across genres uh linguistic barriers going away uh truly national stars Indian show become shows and movies becoming truly global and global hits are few trends that we see going from a business economic model point of view I think all the models will work through it's not going to be that you know a model is going to win over model b it will all coexist um and and and the last point I would say is that customers are last three or four years we've seen a very encouraging trend and we always knew this but it's very encouraging Indian customers pay for content it's a misnorma they don't pay for content they value conscious but when they see great value they pay for content they're paying for experiences they pay for when you have distinguished you know differentiated offerings so we actually see that uh business really becoming uh really big from the point of view the and people ask what what that could get to well the pay TV industry size is an example of paying customers right so that's really the the you know three to five year view of how we see this at a broader level you know in five years time there'll be as many people streaming video as they watch TV today so yeah fantastic one last question what are the challenges you think that could come in the way of the sort of business that it grows what would be as you said at the start it's still a nascent business yeah I mean we are past the sort of stage of having feeding troubles yeah but what are the some challenges that the OTT industry is likely to see at as it goes into the next decade I think the one of the big ones was to have a constant supply of high quality content coming from from here because the demand for content is so much yeah so for example when we started our journey on originals the reality is India had not produced before that high quality cinematic value limited series you know we had you know daily soaps we had others we had content we made for TV but we didn't have experience of creating this but we had amazing creators in India who made great films right so we didn't have a pure premium pay TV model in the country which which enabled this right so that journey over the last five years working with creators you know who made great shows for us and others as well and learn we learned with them and they learned with us has you know is now and now going to other languages that's been a journey and we're very happy with how it's turned out for our customers and and lots more to be done but you know the industry has to do that at scale and build and learn and do more and keep offering you know the the the funny thing on this is that you drop a season of a show and people binge overnight and next morning and saying where season do coming over season three coming you have to keep you know upping the games or to say you create a benchmark for for the next one so constant supply and how we turn that around and and with production facilities infra high quality that's one the second is that we should never forget that this is a very diverse country with very different taste and preferences and you're not just programming for language programming for the household that's right right so if you're really programmed with the household how can I program for for person a person b the house the children in the house and so on and it's not one content that's going to really appeal to them is different things appeal so do I have enough to appeal to the house and how do we bring that forward and I think the third one is is really about the enablers which are we getting sorted but you have to as you go forward you have to cater to local products and offerings and packs and solutions which allow customers to access this content so like for example uh with as we offer a plans a monthly a quarterly annual we also offer the mobile option for customers who want to only access on a mobile and so on so you have to make sure that you create products and offerings for customers to be able to get to this content as you supply that I don't see these are challenges I see this as opportunities but I see us us and others focus on these in the next few years to really unlock the potential of of what we can get to and like I said it's still day one absolutely you know one successful piece of content creates you know multi-fold expectations and you know people just want more and more of that and has always been said in this country movies and cricket are sort of two pillars that have bound this country together and there I say you know movies should sort of movies now called content and OTTs have played a very big part in in sort of content being a becoming a very big unifier in this country especially in the last few years as we have seen uh non-Hindi movies doing really well in the Hindi markets and vice versa so really it is the power of OTT that has driven this change that change and uh you know put it on the same sort of level as cricket is in this country for many many years thank you Gaurav Gandhi for joining us for sharing your thoughts with us I'm sure those of you watching here today will take back some valuable insights about what's happening in the OTT space and what's happening with Amazon in India with that it's a wrap from me and thank you so much for listening to this thank you thank you thank you thank you so much Gaurav


Hello, I am Mati van Hoef and together with Tom van Goetheem, we will be presenting Timeless Timing Attacks. Tom van Goetheem is a researcher at the Disturnet Group in KU Leuven in Belgium. He's a phonetic web and network security enthusiast and he likes to exploit side-channel leaks in browsers and more general in web platforms as well. I am Mati van Hoef, I'm a postdoc at NYU Abu Dhabi and I'll later this year be starting as a professor at KU Leuven in Belgium. I'm interested in wireless and network security and software security and also a bit in Applied Crypto. And previously, I discovered Crack Attack against WPA2 on the RC4 Nomore attack. And in this presentation, we'll be talking about timing attacks on timing leaks. So let me start with some examples of timing leaks. The top left corner we can see is straightforward timing leak where the pending on some secret condition extra computation is performed and here the execution time, so also the response time of for instance the web application, will leak whether the condition was true or false. The top right, we have a for loop that iterates through all the elements in an array. On the for loop is terminated once an element is found with a certain secret condition. On other words, the number of iterations that are being executed depend on some kind of secret information. And this again also means that the execution time of this for loop leaks sensitive information to an adversary. Finally at the bottom, we have an if test that checks whether an array is empty or not, and if it is not empty, extra computation is performed. And here you can imagine a web app where you can search through secret documents using a keyword and then this code would leak whether this keyword is present in a secret document or not. Now how are these timing leaks exploited in practice? Well the attacker would first need to connect to the target server. The attacker then sends a possibly large amount of requests to the server and for each request the server will measure how long it takes to receive a response. And then the attacker will compare two sets of timing measurements namely the baseline on the target. And what do I mean with baseline here? Well with baseline, I for instance mean the first example here where then the secret condition evaluates to false, on the adversary knows that the secret condition evaluates to false, on the adversary will use that as a baseline, on the target request is then a request where the adversary doesn't know what the secret condition evaluates to. But based on the response time, the adversary can then derive whether the secret condition and the target request is also false or whether it is in fact true. And to check whether there is a difference between the baseline on the target request, we will be using statistical tests to determine whether there really is a significant difference or not. Now there are several factors that influence the success rate of a timing attack. The first one is the network connection between the attacker and the victim server. And in particular, the higher the jitter of this connection, the worse the performance of the timing attack. Now the attacker can mitigate this to some extent by for instance moving closer to the target. So in practice that could be renting a server in the same cloud provider where the victim is also located. And I also want to remark here that the jitter is present both on the upstream path, so in the requests, on the downstream path, so in the responses. Another important factor is the size of the timing leak. So it's much easier to exploit a large timing leak say of 50 milliseconds than it is to exploit a small timing leak of say 5 microseconds. Finally, the last important factor is the number of measurements that an adversary can make. In particular, the more timing measurements that an adversary can make, the better the performance of the timing attack will be. And Tom will now show a graphical illustration of a timing attack. Thank you for that introduction Mati. So now let's see how such a remote timing attack works with a visual representation. So we have our attacker on the left side sending the request to the server. And this request goes over the internet, then is processed by the server. Responses generated and sent back to the attacker. The attacker will then measure how long it took between sending and the request and receiving the response. And in this case, this was a bit over three seconds. Then the attacker stores this timing value and then he will probably need to make multiple requests. So he starts sending the next request. And here on the third hop, there was a small delay, also causing a delay in the arrival of the response. So this means that because of this jitter, the attacker will need to make multiple measurements and then apply some statistical analysis in order to reveal the true processing time of the server. So we did some measurements to see how many requests the attacker would actually require. So we varied the number or the difference in timing of the processing time between five microseconds and 50 microseconds. And then we determined how many requests an attacker would need to achieve a 95% accuracy. So our attacker was located in our university network, located in Belgium, and then we set up servers across the world. And then we performed the attack to see how many requests were required. So to detect a timing difference of 50 microseconds with a server in the EU, the attacker also located in the EU, a total of 333 requests were required. Now when the server was further away, so for instance when it was located in the US, more requests were required. So in this case, for 50 microseconds, well over 4,000 requests were required to detect this difference. And also see that as the timing difference became smaller, also the attacker required more measurements. So for 20 microseconds it was close to 3,000 requests that were needed for the server in the EU, growing to 23,000 for 10 microseconds. And for a timing difference of 5 microseconds, we found that it was not possible to do this with a maximum set to 100,000 requests. So we were thinking like how can we improve this remote timing index? And that's how we came to the Timeless Timing Index. So we know that if we rely on absolute response timing, this will not be very reliable because there will be always this jitter that's included in every request because the request needs to traverse over the internet and so does the response. So then we thought okay then maybe let's get rid of the notion of time and that's why we call this attack Timeless. So instead of relying on this sequential timing measurements, we can try to exploit concurrency and only consider the order of the responses. So we don't use any absolute timing measurements anymore in these timing measurements, we just look at the response order. And this means as a very important side effect is that these Timeless Timing Attacks are completely unaffected by network jitter. So let's see how this works in practice. So again we have our attacker and the server, but now the attacker is not just sending one request, but he will be sending two requests that are contained in the same packet. So these two requests arrive simultaneously at the server and are processed concurrently and because the light pink request took less time to process, the light blue response will be generated first and sent first to the attacker, allowing the attacker to detect that this request required less processing time. So if in this case we have some jitter on the way to the server, it will not affect anything because both requests will still arrive exactly at the same time at the server because they're contained in a single packet. And then they can be processed simultaneously at the server side. And again we see the same result. Thank you Tom. So what are the requirements of a Timeless Timing Attack? Well first and for all, the requests need to arrive at the same time at the server. The server needs to process these requests in parallel, so concurrently. And the order of the responses needs to reflect the difference in the execution time on the server. So let's explore these three requirements in more detail. So the first requirement that both requests arrive simultaneously at the server can be fulfilled in two manners, namely we can either rely on multiplexing or on encapsulation. Along with multiplexing, our example is the HTTP2 protocol and this is because with the HTTP2 protocol, two requests can be sent simultaneously over the same connection. In fact, a single TCP packet can carry multiple HTTP2 requests that will be processed concurrently once they arrive at the server. For encapsulation, there our example is the HTTP1 protocol because the HTTP1 protocol on its own does not allow you to send two requests in parallel. However, when we exploit HTTP1 over Tor or over VPN, then we can send two HTTP requests at the same time over Tor or over the VPN network. So let me illustrate these two cases graphically. So for multiplexing, we have the HTTP2 example where here on top you can see the two HTTP2 requests in gray. And the adversary will assure that these two HTTP2 requests are sent in a single TCP packet. This assures that these two requests will arrive simultaneously at the server. For the encapsulation case, we have the HTTP1 example where here again at the bottom, we have two HTTP1 requests shown in gray. These two HTTP1 requests are sent in different TCP connections, meaning they are also encapsulated in two different TCP packets shown in red here, and each TCP packet is then encapsulated in a different Tor cell. And what the adversary now does is the adversary assures that these two Tor cells will be aggregated into a single TCP packet. And once this TCP packet arrives at the Tor onion servers that we are targeting, then the onion servers will process these two requests effectively in parallel. For the second requirement, the second requirement is that these two requests then have to be handled in parallel by the server, and whether this is the case is application dependent. We have found that in most cases applications can handle requests in parallel. One possible exception is when you target cryptographic operations, because those often have to be handled in a certain sequential order. The third requirement then is that the order of the responses will reflect which request finished processing first. On practice, that means that the server must immediately send a response after it finished processing a request. Additionally, the adversary must be able to reliably recover the response order. In other words, when the responses are sent back from the victim server to the adversary, the network should not be reordering these responses. Now in our experiments, we notice that these two responses generally follow the same network path, meaning the order is maintained. But even if for some reason the order of the responses is switched along the network path, then the adversary can still rely on TCP fields such as the TCP sequence numbers or the TCP timestamps to recover the order in which the server sent both responses. So what is now the performance of a timeless timing attack? Well, let's first and for all recall the performance of a traditional timing attack, where Tom explained the first part of this table here. On this slide, we also added the performance of a traditional timing attack over the land on the local host. For instance, we can see that if we want to exploit a timing leak of five microseconds over the local host, this would require a bit more than 40 requests in a traditional timing attack. In contrast, if you look at our new timeless timing attacks, we can, for example, exploit a timing leak of five microseconds using roughly 50 request pairs. What important here is that the timing leak can then be exploited no matter where the adversary is located on the internet. This is in contrast to traditional timing attacks because with traditional timing attacks, we were not able to exploit the timing leak of five microseconds over the internet while for our timeless timing attacks we can. On top of that, I want to highlight that with the timeless timing attacks, we were able to exploit the timing leak of just 100 nanoseconds, while with the traditional timing attacks, the best we could exploit was a timing leak of 150 nanoseconds when the adversary was in the same land or local host. And in fact, over the internet, a traditional timing attack at best could exploit a timing leak of 50 microseconds. This really shows that timeless timing attacks are in order of a magnitude more powerful than traditional timing attacks. So that covers how direct timing attacks work. It's also possible to perform timeless timing attacks in a cross-site setting on to perform timeless timing attacks against Wi-Fi authentication. Anton will now elaborate on the cross-site timing attack scenario. So these cross-site timing attacks, they are a bit different from the direct timing attacks that we were talking about before because in this case it will not be the attacker who's directly connecting to the targeted server, but it will be the victim sending requests. But still, these requests are triggered by the attacker by launching some malicious JavaScript code. And this could happen when the victim lands on a malicious website. For instance, by clicking on a link or if there's a malicious advertisement being shown or if this victim has a really urgent need to look at some cute animal videos and then ends up on the website of the attacker. So these requests that are triggered by the attacker with the JavaScript, they will include the cookies. So this means that the server will process the request using the victim's authentication. And once the request has been processed, the attacker can still observe the order of the response. For instance, when he's using the fetch API, he can look at which promise results first. And this might leak sensitive information that the victim has shared with the website. So we actually applied this timing attack. This cross-site timing attack to the search functionality in Hacker 1, where we were able to leak information about private undisclosed reports. So one of the key differences is that with direct timing attacks is that here the attacker does not have direct control over the network. This is because the browser can choose how to send a request to the kernel and the kernel will then send it to the server. So we will need another technique to make sure that the two requests are joined into a single packet. And in order to do so, we can leverage TCP congestion control. So this mechanism will prevent the client from sending a very large number of packets at once. And instead, the client will need to wait for an acknowledgement from the server before more packets can be sent. So once as soon as the client is waiting for acknowledgement from the server, any other requests that are made, their data will be stored in the queue and they will be merged together into a single packet. So the attack looks fairly simple. So first we make a post request with a sufficiently long body. And then immediately after, we make the two target requests. So the one will be for the baseline and then the other one is the target URL for which we want to see if it matches the same timing as the baseline or whether it takes longer or shorter. Okay, so let's see how this works in practice then. So here we show the victim's packet queue, the TCP packets that are being queued to be sent to the server. So first the attacker makes this post request, which already sends off a number of TCP packets. But there will be also some packets that cannot be sent yet because we need to wait for the acknowledgement from the server and they'll be added to the queue. So here every square represents a single TCP packet. So then the second line of codes is where the attacker will make a request for the baseline URL. And as you can see here it's represented with this pink light pink. And then the third line of code will be the attacker sending the request for the target URL represented as the target pink. So as you can see both requests will be added to the same TCP packet. And then later on when the client has received enough or sufficient acknowledgement from the server it's possible to send all the remaining packets that are in the queue. And as you can see the light and dark pink requests will be both in a single TCP packet which is exactly what we need to perform our timeless time index. So in this presentation we already discussed the direct time index, where the attacker connects directly to the server. Then what I was just talking about are the cross-site time index. And then now Mati will be talking about a third attack scenario where we use timeless time index to break Wi-Fi authentication. So it is also possible to perform timeless timing index against Wi-Fi. And in particular we're going to exploit the EAP PWD protocol that can be used in WPA2 or even WPA3 enterprise networks. So most enterprise networks they use certificates to authenticate the server and sometimes also to authenticate the user. However this can be quite annoying to configure so in practice a small but significant amount of networks rely on the EAP PWD protocol instead to authenticate a user using a username on password. And when using these enterprise authentication protocols the authentication happens between the client on the authentication server. And here the authentication server is commonly a free-radius server on the access point will simply forward messages between the client on the authentication server. Because the authentication server can be located anywhere on the internet the communication between the access point on the authentication server is typically protected using a TLS tunnel on this TLS tunnel is called a Rotsack connection. Now the EAP PWD protocol that we will be targeting uses the so-called hash to curve algorithm to verify a password. Unfortunately a timing leak was discovered in this algorithm this was called a dragon blood attack. However against EAP PWD this timing leak was fairly small and it did not appear possible to combine multiple timing measurements in a more powerful attack so in other words it seemed almost impossible to exploit this timing leak at least against EAP PWD. However by using the timeless timing attack we are able to attack the EAP PWD protocol and how does this work? Well the adversary will spoof three clients shown here on the right and first the first two clients will associate to the access point the access point will request the identity of the client. The client will reply with their identity and send the identity to the free-radius server and then the free-radius server will tell these clients okay I recognize you on both of you now have to perform the EAP PWD authentication algorithm. Now up to this point nothing special happened instead the attack only starts now and in the first part of the attack the third client will send a special authentication frame to the access point. On the access point will forward this authentication frame over the TLS tunnel so over the RADSEC connection to the free-radius server. On this will cause the buffer of the access point to slowly fill up with RADSEC frames and as a result when client 1 and 2 now send their EAP PWD authentication response then because the access point already has some frames in its buffer these two frames will be combined into one TLS record. Before I explain that in more detail I want to highlight that the server here will send both these EAP PWD authentication responses in a single physical Wi-Fi frame. On the single physical Wi-Fi frame is called an AMPDU frame. This means that these two authentication responses will arrive at exactly the same time at the access point on because the buffer at the access point is slowly starting to get full the access point will combine both authentication requests in a single TLS record that is sent to the free-radius server. The free-radius server will then process both authentication requests simultaneously and parallel and it will reply using whichever request finished first and then it will reply using the other request that finished processing second. So here the request of the first client finished first at the server so the server also sends its reply first and then the reply of the second client is sent. And we can see that this order is also reflected at the adversary side namely the adversary will first receive the reply of the client one and then it will receive the reply for client two and the order of these replies allows an adversary to brute force the password of the victim and in this case the probability of then successfully brute forcing the password of the client is higher than 99% or to put that differently the response order correctly reflects the execution time at the free-radius servers in 99% of the time. So only in less than 1% of the cases does the response order incorrectly reflect the execution time at the server and we can use this information to then perform an offline dictionary attack against the password. So for instance if we take the rock you password dump which contains about 140 million passwords then we need to perform 40 measurements and with those 40 measurements we have 86% success probability of correctly recovering the password at least if the password is inside this password done. On this brute force search costs about $1 on for instance on Amazon cloud server. So as a quick recap we now explained direct timing attacks, we explained cross-site timing attacks and we explained timeless timing attacks over Wi-Fi authentication and with that I hand the word back over to Tom. Alright and that brings us to the demonstration. So for this demonstration I created an application called the vault which is basically a place where people can securely share their text documents with military grade protection. So users can enter the title of the document and then select the required security level of the other users that is required to access the document. So it ranges from one which is for public documents up until five which is for really top-secret documents. And the user can enter the content and finally post the document. Another functionality provided by the vault is the search functionality where we can search for strings like high and then get a bunch of results. We can also look for other strings like for instance Vegas and then we can see that there is this one single result. Or we can try some more secretive things like the string defcon but unfortunately we don't find any documents containing that string. Well I did create this application so I know that there is at least one document with the string defcon. But the reason why we're not shown this document is because we don't have the right security level. So in this demonstration what we will try to do is leak the password that is in the same document as the one that contains the string defcon. And in order to do so we will be using these timeless timing attacks. But before I go into the details there how the attack works I'll first explain how the search functionality works. So basically what we do is we do a textual search on the contents of all the documents using the query provided by the user. And then if there's at least one document we need to be able to tell where the current user, well their security level is high enough to view one of the documents. So getting the security level is done by launching a simple SQL query on the database that's located on the same server as the web server. So the timing or the latency there is very minimal. But still this is where the timing leak is. Because if there are no documents which means that there's no match for the text well we don't have to launch this SQL query. So this means that if there's at least one document the timing will take slightly like a few microseconds longer. And this is what we leverage in our attack. So we use Python here and we're using the H2 time library which is the one that we also used in our research. And it's made public and I'll be sharing the link later on in the presentation. So what we do is we define the URL prefix. So we know that the actual password is prefixed by defcon underscore password equals. And then we guess the current character one by one. So if the guess is incorrect we don't get shown any, well there will be no documents matching the text search. So this SQL query will not be executed. And then we compare this to the baseline request where we use a character that's not part of the character set. So we know that it will never have any matching documents. And then we run this with the H2 time library and we get the response order for 15 request pairs. So basically we determine how many negative results there are. Negative means that the order in which the response arrived was reversed. So if we get for the response of R2 before the response of R1 the order is reversed because R1 is sent before R2 typically. Or the request is sent before but then the response is received in a different order. And then we determine the percentage of how many responses were received in reverse order and then see if this is above or below a certain threshold. And in this case the threshold was set to 80%. Okay so I think now we are ready to actually run the attack which is done using just Python. And we will be guessing the passwords character by character. So on the topmost line you can see all the characters that have been found so far. So we are at T and 1. Now we are also guessing or correctly guessed the letter M. So everything here is being shown in real time. Of course to make the demo a bit shorter not the entire character set is used but we just use some random guesses but as you can see so far it's going pretty well. So on the bottom line we show for each character that's being guessed the percentage of the responses that arrived in the reverse order. And for the characters that were guessed incorrectly the percentage is somewhat close to 50% because well there's a 50-50% chance for every request that's being sent that it will arrive before or after the other request. So whether the response order is the same or different the chance is around 50%. Of course there's still a bit of randomness so that's why it's not always 50 but it's sometimes 40, sometimes 60 and I believe that we're already getting quite close to extracting the password and yeah there it is. So we managed to in well just under 2 minutes find that the password is timeless timing. Of course in a real world scenario it would take a bit longer because we would have to guess all the characters one by one but still I think it shows that the deck works very well. Alright and that brings us to the conclusion. So we find that these timeless timing tags are not affected by network jitter at all neither on the upstream nor the downstream. So this allows the attacker to perform these remote timing tags with an accuracy that is similar to as if the attack was launched against the local system. In order to do so we need to ensure that both requests are well they arrive at the same time at the server so in order to do so the attacker can leverage multiplexing or if that's not available it might still be available to leverage a transport protocol that enables encapsulation of the requests. And we also find that all the protocols that meet these criteria may be susceptible to timeless time index and we already in our research we created practical attacks against HP2 and EAPWD for Wi-Fi authentication. Okay so with that I would like to thank you for listening to the presentation. If you have any additional questions or remarks or ideas don't hesitate to reach out to us on Twitter and then finally you can find this issue time library that we used in our research but also in the demo in the following link and then on the right hand side you can find the sources or the source code of the demonstration so you can play around with it as well. Okay thank you.


Okay so today we'll do a class to try to improve your freestyle. Tell me about yourself, a bit of background freestyle. Do you know how to swim already?