No NAT'd User left Behind: Fingerprinting Users behind NAT from NetFlow Records alone

It is generally recognized that the traffic generated by an individual connected to a network acts as his biometric signature. Several tools exploit this fact to fingerprint and monitor users. Often, though, these tools assume to access the entire traffic, including IP addresses and payloads. This is not feasible on the grounds that both performance and privacy would be negatively affected. In reality, most ISPs convert user traffic into NetFlow records for a concise representation that does not include, for instance, any payloads. More importantly, large and distributed networks are usually NAT'd, thus a few IP addresses may be associated to thousands of users. We devised a new fingerprinting framework that overcomes these hurdles. Our system is able to analyze a huge amount of network traffic represented as NetFlows, with the intent to track people. It does so by accurately inferring when users are connected to the network and which IP addresses they are using, even though thousands of users are hidden behind NAT. Our prototype implementation was deployed and tested within an existing large metropolitan WiFi network serving about 200,000 users, with an average load of more than 1,000 users simultaneously connected behind 2 NAT'd IP addresses only. Our solution turned out to be very effective, with an accuracy greater than 90%. We also devised new tools and refined existing ones that may be applied to other contexts related to NetFlow analysis

A Covert Data Transport Protocol

Both enterprise and national firewalls filter network connections. For data forensics and botnet removal applications, it is important to establish the information source. In this paper, we describe a data transport layer which allows a client to transfer encrypted data that provides no discernible information regarding the data source. We use a domain generation algorithm (DGA) to encode AES encrypted data into domain names that current tools are unable to reliably differentiate from valid domain names. The domain names are registered using (free) dynamic DNS services. The data transmission format is not vulnerable to Deep Packet Inspection (DPI).Comment: 8 pages, 10 figures, conferenc

Systemization of Pluggable Transports for Censorship Resistance

An increasing number of countries implement Internet censorship at different scales and for a variety of reasons. In particular, the link between the censored client and entry point to the uncensored network is a frequent target of censorship due to the ease with which a nation-state censor can control it. A number of censorship resistance systems have been developed thus far to help circumvent blocking on this link, which we refer to as link circumvention systems (LCs). The variety and profusion of attack vectors available to a censor has led to an arms race, leading to a dramatic speed of evolution of LCs. Despite their inherent complexity and the breadth of work in this area, there is no systematic way to evaluate link circumvention systems and compare them against each other. In this paper, we (i) sketch an attack model to comprehensively explore a censor's capabilities, (ii) present an abstract model of a LC, a system that helps a censored client communicate with a server over the Internet while resisting censorship, (iii) describe an evaluation stack that underscores a layered approach to evaluate LCs, and (iv) systemize and evaluate existing censorship resistance systems that provide link circumvention. We highlight open challenges in the evaluation and development of LCs and discuss possible mitigations.Comment: Content from this paper was published in Proceedings on Privacy Enhancing Technologies (PoPETS), Volume 2016, Issue 4 (July 2016) as "SoK: Making Sense of Censorship Resistance Systems" by Sheharbano Khattak, Tariq Elahi, Laurent Simon, Colleen M. Swanson, Steven J. Murdoch and Ian Goldberg (DOI 10.1515/popets-2016-0028

Your WiFi is leaking: what do your mobile apps gossip about you?

This paper describes how mobile device apps can inadvertently broadcast personal information through their use of wireless networks despite the correct use of encryption. Using a selection of personas we illustrate how app usage can be tied to personal information. Users would likely assume the confidentiality of personal information (including age, religion, sexuality and gender) when using an encrypted network. However, we demonstrate how encrypted traffic pattern analysis can allow a remote observer to infer potentially sensitive data passively and undetectably without any network credentials. Without the ability to read encrypted WiFi traffic directly, we process the limited side-channel data available (timings and frame sizes) to enable remote app detection. These side-channel data measurements are represented as histograms and used to construct a Random Forest classifier capable of accurately identifying mobile apps from the encrypted traffic they cause. The Random Forest algorithm was able to correctly identify apps with a mean accuracy of ∼99% within the training set. The classifier was then adapted to form the core of a detection program that could monitor multiple devices in real-time. Tests in a closed-world scenario showed 84% accuracy and demonstrated the ability to overcome the data limitations imposed by WiFi encryption. Although accuracy suffers greatly (67%) when moving to an open-world scenario, a high recall rate of 86% demonstrates that apps can unwittingly broadcast personal information openly despite using encrypted WiFi. The open-world false positive rate (38% overall, or 72% for unseen activity alone) leaves much room for improvement but the experiment demonstrates a plausible threat nevertheless. Finally, avenues for improvement and the limitations of this approach are identified. We discuss potential applications, strategies to prevent these leaks, and consider the effort required for an observer to present a practical privacy threat to the everyday WiFi user. This paper presents and demonstrates a nuanced and difficult to solve privacy vulnerability that cannot not be mitigated without considerable changes to current- and next-generation wireless communication protocols