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Register a new userAn Online Matching Model for Self-Adjusting ToR-to-ToR Networks
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Added by
Chen Avin
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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This is a short note that formally presents the matching model for the theoretical study of self-adjusting networks as initially proposed in [1].
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Some BitTorrent users are running BitTorrent on top of Tor to preserve their privacy. In this extended abstract, we discuss three different attacks to reveal the IP address of BitTorrent users on top of Tor. In addition, we exploit the multiplexing of streams from different applications into the same circuit to link non-BitTorrent applications to revealed IP addresses.
Anonymity networks are becoming increasingly popular in todays online world as more users attempt to safeguard their online privacy. Tor is currently the most popular anonymity network in use and provides anonymity to both users and services (hidden services). However, the anonymity provided by Tor is also being misused in various ways. Hosting illegal sites for selling drugs, hosting command and control servers for botnets, and distributing censored content are but a few such examples. As a result, various parties, including governments and law enforcement agencies, are interested in attacks that assist in de-anonymising the Tor network, disrupting its operations, and bypassing its censorship circumvention mechanisms. In this paper, we survey known Tor attacks and identify currently available techniques that lead to improved de-anonymisation of users and hidden services.
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.
The Tor anonymity system provides online privacy for millions of users, but it is slower than typical web browsing. To improve Tor performance, we propose PredicTor, a path selection technique that uses a Random Forest classifier trained on recent measurements of Tor to predict the performance of a proposed path. If the path is predicted to be fast, then the client builds a circuit using those relays. We implemented PredicTor in the Tor source code and show through live Tor experiments and Shadow simulations that PredicTor improves Tor network performance by 11% to 23% compared to Vanilla Tor and by 7% to 13% compared to the previous state-of-the-art scheme. Our experiments show that PredicTor is the first path selection algorithm to dynamically avoid highly congested nodes during times of high congestion and avoid long-distance paths during times of low congestion. We evaluate the anonymity of PredicTor using standard entropy-based and time-to-first-compromise metrics, but these cannot capture the possibility of leakage due to the use of location in path selection. To better address this, we propose a new anonymity metric called CLASI: Client Autonomous System Inference. CLASI is the first anonymity metric in Tor that measures an adversarys ability to infer client Autonomous Systems (ASes) by fingerprinting circuits at the network, country, and relay level. We find that CLASI shows anonymity loss for location-aware path selection algorithms, where entropy-based metrics show little to no loss of anonymity. Additionally, CLASI indicates that PredicTor has similar sender AS leakage compared to the current Tor path selection algorithm due to PredicTor building circuits that are independent of client location.
Let $frak a$ be an ideal of a commutative noetherian ring $R$ with unity and $M$ an $R$-module supported at $V(fa)$. Let $n$ be the supermum of the integers $i$ for which $H^{fa}_i(M) eq 0$. We show that $M$ is $fa$-cofinite if and only if the $R$-module $Tor^R_i(R/fa,M)$ is finitely generated for every $0leq ileq n$. This provides a hands-on and computable finitely-many-steps criterion to examine $mathfrak{a}$-confiniteness. Our approach relies heavily on the theory of local homology which demonstrates the effectiveness and indispensability of this tool.
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