HORNET: High-speed Onion Routing at the Network Layer

We present HORNET, a system that enables high-speed end-to-end anonymous channels by leveraging next generation network architectures. HORNET is designed as a low-latency onion routing system that operates at the network layer thus enabling a wide range of applications. Our system uses only symmetric cryptography for data forwarding yet requires no per-flow state on intermediate nodes. This design enables HORNET nodes to process anonymous traffic at over 93 Gb/s. HORNET can also scale as required, adding minimal processing overhead per additional anonymous channel. We discuss design and implementation details, as well as a performance and security evaluation.Comment: 14 pages, 5 figure

One Bad Apple Spoils the Bunch: Exploiting P2P Applications to Trace and Profile Tor Users

Tor is a popular low-latency anonymity network. However, Tor does not protect against the exploitation of an insecure application to reveal the IP address of, or trace, a TCP stream. In addition, because of the linkability of Tor streams sent together over a single circuit, tracing one stream sent over a circuit traces them all. Surprisingly, it is unknown whether this linkability allows in practice to trace a significant number of streams originating from secure (i.e., proxied) applications. In this paper, we show that linkability allows us to trace 193% of additional streams, including 27% of HTTP streams possibly originating from "secure" browsers. In particular, we traced 9% of Tor streams carried by our instrumented exit nodes. Using BitTorrent as the insecure application, we design two attacks tracing BitTorrent users on Tor. We run these attacks in the wild for 23 days and reveal 10,000 IP addresses of Tor users. Using these IP addresses, we then profile not only the BitTorrent downloads but also the websites visited per country of origin of Tor users. We show that BitTorrent users on Tor are over-represented in some countries as compared to BitTorrent users outside of Tor. By analyzing the type of content downloaded, we then explain the observed behaviors by the higher concentration of pornographic content downloaded at the scale of a country. Finally, we present results suggesting the existence of an underground BitTorrent ecosystem on Tor

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

Seeking Anonymity in an Internet Panopticon

Obtaining and maintaining anonymity on the Internet is challenging. The state of the art in deployed tools, such as Tor, uses onion routing (OR) to relay encrypted connections on a detour passing through randomly chosen relays scattered around the Internet. Unfortunately, OR is known to be vulnerable at least in principle to several classes of attacks for which no solution is known or believed to be forthcoming soon. Current approaches to anonymity also appear unable to offer accurate, principled measurement of the level or quality of anonymity a user might obtain. Toward this end, we offer a high-level view of the Dissent project, the first systematic effort to build a practical anonymity system based purely on foundations that offer measurable and formally provable anonymity properties. Dissent builds on two key pre-existing primitives - verifiable shuffles and dining cryptographers - but for the first time shows how to scale such techniques to offer measurable anonymity guarantees to thousands of participants. Further, Dissent represents the first anonymity system designed from the ground up to incorporate some systematic countermeasure for each of the major classes of known vulnerabilities in existing approaches, including global traffic analysis, active attacks, and intersection attacks. Finally, because no anonymity protocol alone can address risks such as software exploits or accidental self-identification, we introduce WiNon, an experimental operating system architecture to harden the uses of anonymity tools such as Tor and Dissent against such attacks.Comment: 8 pages, 10 figure

TARANET: Traffic-Analysis Resistant Anonymity at the NETwork layer

Modern low-latency anonymity systems, no matter whether constructed as an overlay or implemented at the network layer, offer limited security guarantees against traffic analysis. On the other hand, high-latency anonymity systems offer strong security guarantees at the cost of computational overhead and long delays, which are excessive for interactive applications. We propose TARANET, an anonymity system that implements protection against traffic analysis at the network layer, and limits the incurred latency and overhead. In TARANET's setup phase, traffic analysis is thwarted by mixing. In the data transmission phase, end hosts and ASes coordinate to shape traffic into constant-rate transmission using packet splitting. Our prototype implementation shows that TARANET can forward anonymous traffic at over 50~Gbps using commodity hardware

How Unique is Your .onion? An Analysis of the Fingerprintability of Tor Onion Services

Recent studies have shown that Tor onion (hidden) service websites are particularly vulnerable to website fingerprinting attacks due to their limited number and sensitive nature. In this work we present a multi-level feature analysis of onion site fingerprintability, considering three state-of-the-art website fingerprinting methods and 482 Tor onion services, making this the largest analysis of this kind completed on onion services to date. Prior studies typically report average performance results for a given website fingerprinting method or countermeasure. We investigate which sites are more or less vulnerable to fingerprinting and which features make them so. We find that there is a high variability in the rate at which sites are classified (and misclassified) by these attacks, implying that average performance figures may not be informative of the risks that website fingerprinting attacks pose to particular sites. We analyze the features exploited by the different website fingerprinting methods and discuss what makes onion service sites more or less easily identifiable, both in terms of their traffic traces as well as their webpage design. We study misclassifications to understand how onion service sites can be redesigned to be less vulnerable to website fingerprinting attacks. Our results also inform the design of website fingerprinting countermeasures and their evaluation considering disparate impact across sites.Comment: Accepted by ACM CCS 201

Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection

In this paper, we introduce and evaluate PROPEDEUTICA, a novel methodology and framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning (ML) and deep learning (DL) algorithms. In PROPEDEUTICA, all software processes in the system start execution subjected to a conventional ML detector for fast classification. If a piece of software receives a borderline classification, it is subjected to further analysis via more performance expensive and more accurate DL methods, via our newly proposed DL algorithm DEEPMALWARE. Further, we introduce delays to the execution of software subjected to deep learning analysis as a way to "buy time" for DL analysis and to rate-limit the impact of possible malware in the system. We evaluated PROPEDEUTICA with a set of 9,115 malware samples and 877 commonly used benign software samples from various categories for the Windows OS. Our results show that the false positive rate for conventional ML methods can reach 20%, and for modern DL methods it is usually below 6%. However, the classification time for DL can be 100X longer than conventional ML methods. PROPEDEUTICA improved the detection F1-score from 77.54% (conventional ML method) to 90.25%, and reduced the detection time by 54.86%. Further, the percentage of software subjected to DL analysis was approximately 40% on average. Further, the application of delays in software subjected to ML reduced the detection time by approximately 10%. Finally, we found and discussed a discrepancy between the detection accuracy offline (analysis after all traces are collected) and on-the-fly (analysis in tandem with trace collection). Our insights show that conventional ML and modern DL-based malware detectors in isolation cannot meet the needs of efficient and effective malware detection: high accuracy, low false positive rate, and short classification time.Comment: 17 pages, 7 figure

SafeExit: Exit Node Protection for TOR

TOR is one of the most important networks for providing anonymity over the internet. However, in some cases its exit node operators open themselves up to various legal challenges, a fact which discourages participation in the network. In this paper, we propose a mechanism for allowing some users to be voluntarily verified by trusted third parties, providing a means by which an exit node can verify that they are not the true source of traffic. This is done by extending TOR’s anonymity model to include another class of user, and using a web of trust mechanism to create chains of trust