53,507 research outputs found
Fast Consensus under Eventually Stabilizing Message Adversaries
This paper is devoted to deterministic consensus in synchronous dynamic
networks with unidirectional links, which are under the control of an
omniscient message adversary. Motivated by unpredictable node/system
initialization times and long-lasting periods of massive transient faults, we
consider message adversaries that guarantee periods of less erratic message
loss only eventually: We present a tight bound of for the termination
time of consensus under a message adversary that eventually guarantees a single
vertex-stable root component with dynamic network diameter , as well as a
simple algorithm that matches this bound. It effectively halves the termination
time achieved by an existing consensus algorithm, which also works under
our message adversary. We also introduce a generalized, considerably stronger
variant of our message adversary, and show that our new algorithm, unlike the
existing one, still works correctly under it.Comment: 13 pages, 5 figures, updated reference
Measuring and mitigating AS-level adversaries against Tor
The popularity of Tor as an anonymity system has made it a popular target for
a variety of attacks. We focus on traffic correlation attacks, which are no
longer solely in the realm of academic research with recent revelations about
the NSA and GCHQ actively working to implement them in practice.
Our first contribution is an empirical study that allows us to gain a high
fidelity snapshot of the threat of traffic correlation attacks in the wild. We
find that up to 40% of all circuits created by Tor are vulnerable to attacks by
traffic correlation from Autonomous System (AS)-level adversaries, 42% from
colluding AS-level adversaries, and 85% from state-level adversaries. In
addition, we find that in some regions (notably, China and Iran) there exist
many cases where over 95% of all possible circuits are vulnerable to
correlation attacks, emphasizing the need for AS-aware relay-selection.
To mitigate the threat of such attacks, we build Astoria--an AS-aware Tor
client. Astoria leverages recent developments in network measurement to perform
path-prediction and intelligent relay selection. Astoria reduces the number of
vulnerable circuits to 2% against AS-level adversaries, under 5% against
colluding AS-level adversaries, and 25% against state-level adversaries. In
addition, Astoria load balances across the Tor network so as to not overload
any set of relays.Comment: Appearing at NDSS 201
Practical Attacks Against Graph-based Clustering
Graph modeling allows numerous security problems to be tackled in a general
way, however, little work has been done to understand their ability to
withstand adversarial attacks. We design and evaluate two novel graph attacks
against a state-of-the-art network-level, graph-based detection system. Our
work highlights areas in adversarial machine learning that have not yet been
addressed, specifically: graph-based clustering techniques, and a global
feature space where realistic attackers without perfect knowledge must be
accounted for (by the defenders) in order to be practical. Even though less
informed attackers can evade graph clustering with low cost, we show that some
practical defenses are possible.Comment: ACM CCS 201
- …