Traffic Analysis Attacks and Defenses in Low Latency Anonymous Communication

The recent public disclosure of mass surveillance of electronic communication, involving powerful government authorities, has drawn the public's attention to issues regarding Internet privacy. For almost a decade now, there have been several research efforts towards designing and deploying open source, trustworthy and reliable systems that ensure users' anonymity and privacy. These systems operate by hiding the true network identity of communicating parties against eavesdropping adversaries. Tor, acronym for The Onion Router, is an example of such a system. Such systems relay the traffic of their users through an overlay of nodes that are called Onion Routers and are operated by volunteers distributed across the globe. Such systems have served well as anti-censorship and anti-surveillance tools. However, recent publications have disclosed that powerful government organizations are seeking means to de-anonymize such systems and have deployed distributed monitoring infrastructure to aid their efforts. Attacks against anonymous communication systems, like Tor, often involve trac analysis. In such attacks, an adversary, capable of observing network traffic statistics in several different networks, correlates the trac patterns in these networks, and associates otherwise seemingly unrelated network connections. The process can lead an adversary to the source of an anonymous connection. However, due to their design, consisting of globally distributed relays, the users of anonymity networks like Tor, can route their traffic virtually via any network; hiding their tracks and true identities from their communication peers and eavesdropping adversaries. De-anonymization of a random anonymous connection is hard, as the adversary is required to correlate traffic patterns in one network link to those in virtually all other networks. Past research mostly involved reducing the complexity of this process by rst reducing the set of relays or network routers to monitor, and then identifying the actual source of anonymous traffic among network connections that are routed via this reduced set of relays or network routers to monitor. A study of various research efforts in this field reveals that there have been many more efforts to reduce the set of relays or routers to be searched than to explore methods for actually identifying an anonymous user amidst the network connections using these routers and relays. Few have tried to comprehensively study a complete attack, that involves reducing the set of relays and routers to monitor and identifying the source of an anonymous connection. Although it is believed that systems like Tor are trivially vulnerable to traffic analysis, there are various technical challenges and issues that can become obstacles to accurately identifying the source of anonymous connection. It is hard to adjudge the vulnerability of anonymous communication systems without adequately exploring the issues involved in identifying the source of anonymous traffic. We take steps to ll this gap by exploring two novel active trac analysis attacks, that solely rely on measurements of network statistics. In these attacks, the adversary tries to identify the source of an anonymous connection arriving to a server from an exit node. This generally involves correlating traffic entering and leaving the Tor network, linking otherwise unrelated connections. To increase the accuracy of identifying the victim connection among several connections, the adversary injects a traffic perturbation pattern into a connection arriving to the server from a Tor node, that the adversary wants to de-anonymize. One way to achieve this is by colluding with the server and injecting a traffic perturbation pattern using common traffic shaping tools. Our first attack involves a novel remote bandwidth estimation technique to conrm the identity of Tor relays and network routers along the path connecting a Tor client and a server by observing network bandwidth fluctuations deliberately injected by the server. The second attack involves correlating network statistics, for connections entering and leaving the Tor network, available from existing network infrastructure, such as Cisco's NetFlow, for identifying the source of an anonymous connection. Additionally, we explored a novel technique to defend against the latter attack. Most research towards defending against traffic analysis attacks, involving transmission of dummy traffic, have not been implemented due to fears of potential performance degradation. Our novel technique involves transmission of dummy traffic, consisting of packets with IP headers having small Time-to-Live (TTL) values. Such packets are discarded by the routers before they reach their destination. They distort NetFlow statistics, without degrading the client's performance. Finally, we present a strategy that employs transmission of unique plain-text decoy traffic, that appears sensitive, such as fake user credentials, through Tor nodes to decoy servers under our control. Periodic tallying of client and server logs to determine unsolicited connection attempts at the server is used to identify the eavesdropping nodes. Such malicious Tor node operators, eavesdropping on users' traffic, could be potential traffic analysis attackers