Improving Security and Performance in Low Latency Anonymous Networks

Conventional wisdom dictates that the level of anonymity offered by low latency anonymity networks increases as the user base grows. However, the most significant obstacle to increased adoption of such systems is that their security and performance properties are perceived to be weak. In an effort to help foster adoption, this dissertation aims to better understand and improve security, anonymity, and performance in low latency anonymous communication systems. To better understand the security and performance properties of a popular low latency anonymity network, we characterize Tor, focusing on its application protocol distribution, geopolitical client and router distributions, and performance. For instance, we observe that peer-to-peer file sharing protocols use an unfair portion of the network’s scarce bandwidth. To reduce the congestion produced by bulk downloaders in networks such as Tor, we design, implement, and analyze an anonymizing network tailored specifically for the BitTorrent peer-to-peer file sharing protocol. We next analyze Tor’s security and anonymity properties and empirically show that Tor is vulnerable to practical end-to-end traffic correlation attacks launched by relatively weak adversaries that inflate their bandwidth claims to attract traffic and thereby compromise key positions on clients’ paths. We also explore the security and performance trade-offs that revolve around path length design decisions and we show that shorter paths offer performance benefits and provide increased resilience to certain attacks. Finally, we discover a source of performance degradation in Tor that results from poor congestion and flow control. To improve Tor’s performance and grow its user base, we offer a fresh approach to congestion and flow control inspired by techniques from IP and ATM networks

Traffic Monitoring and analysis for source identification

Ph.DDOCTOR OF PHILOSOPH

Multipath Routing on Anonymous Communication Systems: Enhancing Privacy and Performance

We live in an era where mass surveillance and online tracking against civilians and organizations have reached alarming levels. This has resulted in more and more users relying on anonymous communications tools for their daily online activities. Nowadays, Tor is the most popular and widely deployed anonymization network, serving millions of daily users in the entire world. Tor promises to hide the identity of users (i.e., IP addresses) and prevents that external agents disclose relationships between the communicating parties. However, the benefit of privacy protection comes at the cost of severe performance loss. This performance loss degrades the user experience to such an extent that many users do not use anonymization networks and forgo the privacy protection offered. On the other hand, the popularity of Tor has captured the attention of attackers wishing to deanonymize their users. As a response, this dissertation presents a set of multipath routing techniques, both at transport and circuit level, to improve the privacy and performance offered to Tor users. To this end, we first present a comprehensive taxonomy to identify the implications of integrating multipath on each design aspect of Tor. Then, we present a novel transport design to address the existing performance unfairness of the Tor traffic.In Tor, traffic from multiple users is multiplexed in a single TCP connection between two relays. While this has positive effects on privacy, it negatively influences performance and is characterized by unfairness as TCP congestion control gives all the multiplexed Tor traffic as little of the available bandwidth as it gives to every single TCP connection that competes for the same resource. To counter this, we propose to use multipath TCP (MPTCP) to allow for better resource utilization, which, in turn, increases throughput of the Tor traffic to a fairer extend. Our evaluation in real-world settings shows that using out-of-the-box MPTCP leads to 15% performance gain. We analyze the privacy implications of MPTCP in Tor settings and discuss potential threats and mitigation strategies. Regarding privacy, in Tor, a malicious entry node can mount website fingerprinting (WFP) attacks to disclose the identities of Tor users by only observing patterns of data flows.In response to this, we propose splitting traffic over multiple entry nodes to limit the observable patterns that an adversary has access to. We demonstrate that our sophisticated splitting strategy reduces the accuracy from more than 98% to less than 16% for all state-of-the-art WFP attacks without adding any artificial delays or dummy traffic. Additionally, we show that this defense, initially designed against WFP, can also be used to mitigate end-to-end correlation attacks. The contributions presented in this thesis are orthogonal to each other and their synergy comprises a boosted system in terms of both privacy and performance. This results in a more attractive anonymization network for new and existing users, which, in turn, increases the security of all users as a result of enlarging the anonymity set

TOWARDS RELIABLE CIRCUMVENTION OF INTERNET CENSORSHIP

The Internet plays a crucial role in today\u27s social and political movements by facilitating the free circulation of speech, information, and ideas; democracy and human rights throughout the world critically depend on preserving and bolstering the Internet\u27s openness. Consequently, repressive regimes, totalitarian governments, and corrupt corporations regulate, monitor, and restrict the access to the Internet, which is broadly known as Internet \emph{censorship}. Most countries are improving the internet infrastructures, as a result they can implement more advanced censoring techniques. Also with the advancements in the application of machine learning techniques for network traffic analysis have enabled the more sophisticated Internet censorship. In this thesis, We take a close look at the main pillars of internet censorship, we will introduce new defense and attacks in the internet censorship literature. Internet censorship techniques investigate users’ communications and they can decide to interrupt a connection to prevent a user from communicating with a specific entity. Traffic analysis is one of the main techniques used to infer information from internet communications. One of the major challenges to traffic analysis mechanisms is scaling the techniques to today\u27s exploding volumes of network traffic, i.e., they impose high storage, communications, and computation overheads. We aim at addressing this scalability issue by introducing a new direction for traffic analysis, which we call \emph{compressive traffic analysis}. Moreover, we show that, unfortunately, traffic analysis attacks can be conducted on Anonymity systems with drastically higher accuracies than before by leveraging emerging learning mechanisms. We particularly design a system, called \deepcorr, that outperforms the state-of-the-art by significant margins in correlating network connections. \deepcorr leverages an advanced deep learning architecture to \emph{learn} a flow correlation function tailored to complex networks. Also to be able to analyze the weakness of such approaches we show that an adversary can defeat deep neural network based traffic analysis techniques by applying statistically undetectable \emph{adversarial perturbations} on the patterns of live network traffic. We also design techniques to circumvent internet censorship. Decoy routing is an emerging approach for censorship circumvention in which circumvention is implemented with help from a number of volunteer Internet autonomous systems, called decoy ASes. We propose a new architecture for decoy routing that, by design, is significantly stronger to rerouting attacks compared to \emph{all} previous designs. Unlike previous designs, our new architecture operates decoy routers only on the downstream traffic of the censored users; therefore we call it \emph{downstream-only} decoy routing. As we demonstrate through Internet-scale BGP simulations, downstream-only decoy routing offers significantly stronger resistance to rerouting attacks, which is intuitively because a (censoring) ISP has much less control on the downstream BGP routes of its traffic. Then, we propose to use game theoretic approaches to model the arms races between the censors and the censorship circumvention tools. This will allow us to analyze the effect of different parameters or censoring behaviors on the performance of censorship circumvention tools. We apply our methods on two fundamental problems in internet censorship. Finally, to bring our ideas to practice, we designed a new censorship circumvention tool called \name. \name aims at increasing the collateral damage of censorship by employing a ``mass\u27\u27 of normal Internet users, from both censored and uncensored areas, to serve as circumvention proxies

Tradeoffs between Anonymity and Quality of Services in Data Networking and Signaling Games

Timing analysis has long been used to compromise users\u27 anonymity in networks. Even when data is encrypted, an adversary can track flows from sources to the corresponding destinations by merely using the correlation between the inter-packet timing on incoming and outgoing streams at intermediate routers. Anonymous network systems, where users communicate without revealing their identities, rely on the idea of Chaum mixing to hide `networking information\u27. Chaum mixes are routers or proxy servers that randomly reorder the outgoing packets to prevent an eavesdropper from tracking the flow of packets. The effectiveness of such mixing strategies is, however, diminished under constraints on network Quality of Services (QoS)s such as memory, bandwidth, and fairness. In this work, two models for studying anonymity, packet based anonymity and flow based anonymity, are proposed to address these issues quantitatively and a trade-off between network constraints and achieved anonymity is studied. Packet based anonymity model is proposed to study the short burst traffic arrival models of users such as in web browsing. For packet based anonymity, an information theoretic investigation of mixes under memory constraint and fairness constraint is established. Specifically, for memory constrained mixes, the first single letter characterization of the maximum achievable anonymity for a mix serving two users with equal arrival rates is provided. Further, for two users with unequal arrival rates the anonymity is expressed as a solution to a series of finite recursive equations. In addition, for more than two users and arbitrary arrival rates, a lower bound on the convergence rate of anonymity is derived as buffer size increases and it is shown that under certain arrival configurations the lower bound is tight. The adverse effects of requirement of fairness in data networking on anonymous networking is also studied using the packet based anonymity model and a novel temporal fairness index is proposed to compare the tradeoff between fairness and achieved anonymity of three diverse and popular fairness paradigms: First Come First Serve, Fair Queuing and Proportional Method. It is shown that FCFS and Fair Queuing algorithms have little inherent anonymity. A significant improvement in anonymity is therefore achieved by relaxing the fairness paradigms. The analysis of the relaxed FCFS criterion, in particular, is accomplished by modeling the problem as a Markov Decision Process (MDP). The proportional method of scheduling, while avoided in networks today, is shown to significantly outperform the other fair scheduling algorithms in anonymity, and is proven to be asymptotically optimal as the buffer size of the scheduler is increased. Flow based anonymity model is proposed to study long streams traffic models of users such as in media streaming. A detection theoretic measure of anonymity is proposed to study the optimization of mixing strategies under network constraints for this flow based anonymity model. Specifically, using the detection time of the adversary as a metric, the effectiveness of mixing strategies is maximized under constraints on memory and throughput. A general game theoretic model is proposed to study the mixing strategies when an adversary is capable of capturing a fraction of incoming packets. For the proposed multistage game, existence of a Nash equilibrium is proven, and the optimal strategies for the mix and adversary were derived at the equilibrium condition.It is noted in this work that major literature on anonymity in Internet is focused on achieving anonymity using third parties like mixes or onion routers, while the contributions of users\u27 individual actions such as accessing multiple websites to hide the targeted websites, using multiple proxy servers to hide the traffic routes are overlooked. In this thesis, signaling game model is proposed to study specifically these kind of problems. Fundamentally, signaling games consist of two players: senders and receivers and each sender belongs to one of multiple types. The users who seek to achieve anonymity are modeled as the sender of a signaling game and their types are identified by their personal information that they want to hide. The eavesdroppers are modeled as the receiver of the signaling game. Senders transmit their messages to receivers. The transmission of these messages can be seen as inevitable actions that a user have to take in his/her daily life, like the newspapers he/she subscribes on the Internet, online shopping that he/she does, but these messages are susceptible to reveal the user identity such as his/her political affiliation or his/her affluence level. The receiver (eavesdropper) uses these messages to interpret the senders\u27 type and take optimal actions according to his belief of senders\u27 type. Senders choose their messages to increase their reward given that they know the optimal policies of the receivers for choosing the action based on the transmitted message. However, sending the messages that increases senders\u27 reward may reveal their type to receivers thus violating their privacy and can be used by eavesdropper in future to harm the senders. In this work, the payoff of a signalling game is adjusted to incorporate the information revealed to an eavesdropper such that this information leakage is minimized from the users\u27 perspective. The existence of Bayesian-Nash equilibrium is proven in this work for the signaling games even after the incorporation of users\u27 anonymity. It is also proven that the equilibrium point is unique if the desired anonymity is below a certain threshold

Private and censorship-resistant communication over public networks

Society’s increasing reliance on digital communication networks is creating unprecedented opportunities for wholesale surveillance and censorship. This thesis investigates the use of public networks such as the Internet to build robust, private communication systems that can resist monitoring and attacks by powerful adversaries such as national governments. We sketch the design of a censorship-resistant communication system based on peer-to-peer Internet overlays in which the participants only communicate directly with people they know and trust. This ‘friend-to-friend’ approach protects the participants’ privacy, but it also presents two significant challenges. The first is that, as with any peer-to-peer overlay, the users of the system must collectively provide the resources necessary for its operation; some users might prefer to use the system without contributing resources equal to those they consume, and if many users do so, the system may not be able to survive. To address this challenge we present a new game theoretic model of the problem of encouraging cooperation between selfish actors under conditions of scarcity, and develop a strategy for the game that provides rational incentives for cooperation under a wide range of conditions. The second challenge is that the structure of a friend-to-friend overlay may reveal the users’ social relationships to an adversary monitoring the underlying network. To conceal their sensitive relationships from the adversary, the users must be able to communicate indirectly across the overlay in a way that resists monitoring and attacks by other participants. We address this second challenge by developing two new routing protocols that robustly deliver messages across networks with unknown topologies, without revealing the identities of the communication endpoints to intermediate nodes or vice versa. The protocols make use of a novel unforgeable acknowledgement mechanism that proves that a message has been delivered without identifying the source or destination of the message or the path by which it was delivered. One of the routing protocols is shown to be robust to attacks by malicious participants, while the other provides rational incentives for selfish participants to cooperate in forwarding messages

Design and Implementation of Algorithms for Traffic Classification

Traffic analysis is the practice of using inherent characteristics of a network flow such as timings, sizes, and orderings of the packets to derive sensitive information about it. Traffic analysis techniques are used because of the extensive adoption of encryption and content-obfuscation mechanisms, making it impossible to infer any information about the flows by analyzing their content. In this thesis, we use traffic analysis to infer sensitive information for different objectives and different applications. Specifically, we investigate various applications: p2p cryptocurrencies, flow correlation, and messaging applications. Our goal is to tailor specific traffic analysis algorithms that best capture network traffic’s intrinsic characteristics in those applications for each of these applications. Also, the objective of traffic analysis is different for each of these applications. Specifically, in Bitcoin, our goal is to evaluate Bitcoin traffic’s resilience to blocking by powerful entities such as governments and ISPs. Bitcoin and similar cryptocurrencies play an important role in electronic commerce and other trust-based distributed systems because of their significant advantage over traditional currencies, including open access to global e-commerce. Therefore, it is essential to the consumers and the industry to have reliable access to their Bitcoin assets. We also examine stepping stone attacks for flow correlation. A stepping stone is a host that an attacker uses to relay her traffic to hide her identity. We introduce two fingerprinting systems, TagIt and FINN. TagIt embeds a secret fingerprint into the flows by moving the packets to specific time intervals. However, FINN utilizes DNNs to embed the fingerprint by changing the inter-packet delays (IPDs) in the flow. In messaging applications, we analyze the WhatsApp messaging service to determine if traffic leaks any sensitive information such as members’ identity in a particular conversation to the adversaries who watch their encrypted traffic. These messaging applications’ privacy is essential because these services provide an environment to dis- cuss politically sensitive subjects, making them a target to government surveillance and censorship in totalitarian countries. We take two technical approaches to design our traffic analysis techniques. The increasing use of DNN-based classifiers inspires our first direction: we train DNN classifiers to perform some specific traffic analysis task. Our second approach is to inspect and model the shape of traffic in the target application and design a statistical classifier for the expected shape of traffic. DNN- based methods are useful when the network is complex, and the traffic’s underlying noise is not linear. Also, these models do not need a meticulous analysis to extract the features. However, deep learning techniques need a vast amount of training data to work well. Therefore, they are not beneficial when there is insufficient data avail- able to train a generalized model. On the other hand, statistical methods have the advantage that they do not have training overhead

New statistical disclosure attacks on anonymous communications networks

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Ingeniería del Software e Inteligencia Artificial, leída el 5-02-2016.El anonimato es una dimensi on de la privacidad en la que una persona se reserva su identidad en las relaciones sociales que mantiene. Desde el punto de vista del area de las comunicaciones electr onicas, el anonimato posibilita mantener oculta la informaci on que pueda conducir a la identi caci on de las partes involucradas en una transacci on. Actualmente, conservar el anonimato en las transacciones de informaci on en red representa uno de los aspectos m as importantes. Con este n se han desarrollado diversas tecnolog as, com unmente denominadas tecnolog as para la mejora de la privacidad. Una de las formas m as populares y sencillas de proteger el anonimato en las comunicaciones entre usuarios son los sistemas de comunicaci on an onima de baja latencia basados en redes de mezcladores. Estos sistemas est an expuestos a una serie de ataques basados en an alisis de tr a co que comprometen la privacidad de las relaciones entre los usuarios participantes en la comunicaci on, esto es, que determinan, en mayor o menor medida, las identidades de emisores y receptores. Entre los diferentes tipos de ataques destacan los basados en la inundaci on de la red con informaci on falsa para obtener patrones en la red de mezcladores, los basados en el control del tiempo, los basados en el contenido de los mensajes, y los conocidos como ataques de intersecci on, que pretenden inferir, a trav es de razonamientos probabil sticos o de optimizaci on, patrones de relaciones entre usuarios a partir de la informaci on recabada en lotes o durante un per odo de tiempo por parte del atacante. Este ultimo tipo de ataque es el objeto de la presente tesis...Anonymity is a privacy dimension related to people's interest in preserving their identity in social relationships. In network communications, anonymity makes it possible to hide information that could compromise the identity of parties involved in transactions. Nowadays, anonymity preservation in network information transactions represents a crucial research eld. In order to address this issue, a number of Privacy Enhancing Technologies have been developed. Low latency communications systems based on networks of mixes are very popular and simple measures to protect anonymity in users communications. These systems are exposed to a series of attacks based on tra c analysis that compromise the privacy of relationships between user participating in communications, leading to determine the identity of sender and receiver in a particular information transaction. Some of the leading attacks types are attacks based on sending dummy tra c to the network, attacks based on time control, attacks that take into account the textual information within the messages, and intersections attacks, that pretend to derive patterns of communications between users using probabilistic reasoning or optimization algorithms. This last type of attack is the subject of the present work. Intersection attacks lead to derive statistical estimations of the communications patterns (mean number of sent messages between a pair of users, probability of relationship between users, etc). These models were named Statistical Disclosure Attacks, and were soon considered able to compromise seriously the anonymity of networks based on mixes. Nevertheless, the hypotheses assumed in the rst publications for the concrete development of the attacks were excessively demanding and unreal. It was common to suppose that messages were sent with uniform probability to the receivers, to assume the knowledge of the number of friends an user has or the knowledge a priori of some network parameters, supposing similar behavior between users, etc...Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu