26,694 research outputs found
Passive network tomography for erroneous networks: A network coding approach
Passive network tomography uses end-to-end observations of network
communication to characterize the network, for instance to estimate the network
topology and to localize random or adversarial glitches. Under the setting of
linear network coding this work provides a comprehensive study of passive
network tomography in the presence of network (random or adversarial) glitches.
To be concrete, this work is developed along two directions: 1. Tomographic
upper and lower bounds (i.e., the most adverse conditions in each problem
setting under which network tomography is possible, and corresponding schemes
(computationally efficient, if possible) that achieve this performance) are
presented for random linear network coding (RLNC). We consider RLNC designed
with common randomness, i.e., the receiver knows the random code-books all
nodes. (To justify this, we show an upper bound for the problem of topology
estimation in networks using RLNC without common randomness.) In this setting
we present the first set of algorithms that characterize the network topology
exactly. Our algorithm for topology estimation with random network errors has
time complexity that is polynomial in network parameters. For the problem of
network error localization given the topology information, we present the first
computationally tractable algorithm to localize random errors, and prove it is
computationally intractable to localize adversarial errors. 2. New network
coding schemes are designed that improve the tomographic performance of RLNC
while maintaining the desirable low-complexity, throughput-optimal, distributed
linear network coding properties of RLNC. In particular, we design network
codes based on Reed-Solomon codes so that a maximal number of adversarial
errors can be localized in a computationally efficient manner even without the
information of network topology.Comment: 40 pages, under submission for IEEE Trans. on Information Theor
Byzantine Modification Detection in Multicast Networks With Random Network Coding
An information-theoretic approach for detecting Byzantine or adversarial modifications in networks employing random linear network coding is described. Each exogenous source packet is augmented with a flexible number of hash symbols that are obtained as a polynomial function of the data symbols. This approach depends only on the adversary not knowing the random coding coefficients of all other packets received by the sink nodes when designing its adversarial packets. We show how the detection probability varies with the overhead (ratio of hash to data symbols), coding field size, and the amount of information unknown to the adversary about the random code
Network error correction with unequal link capacities
This paper studies the capacity of single-source single-sink noiseless
networks under adversarial or arbitrary errors on no more than z edges. Unlike
prior papers, which assume equal capacities on all links, arbitrary link
capacities are considered. Results include new upper bounds, network error
correction coding strategies, and examples of network families where our bounds
are tight. An example is provided of a network where the capacity is 50%
greater than the best rate that can be achieved with linear coding. While
coding at the source and sink suffices in networks with equal link capacities,
in networks with unequal link capacities, it is shown that intermediate nodes
may have to do coding, nonlinear error detection, or error correction in order
to achieve the network error correction capacity
Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems
We show that end-to-end learning of communication systems through deep neural
network (DNN) autoencoders can be extremely vulnerable to physical adversarial
attacks. Specifically, we elaborate how an attacker can craft effective
physical black-box adversarial attacks. Due to the openness (broadcast nature)
of the wireless channel, an adversary transmitter can increase the
block-error-rate of a communication system by orders of magnitude by
transmitting a well-designed perturbation signal over the channel. We reveal
that the adversarial attacks are more destructive than jamming attacks. We also
show that classical coding schemes are more robust than autoencoders against
both adversarial and jamming attacks. The codes are available at [1].Comment: to appear at IEEE Communications Letter
Routing for Security in Networks with Adversarial Nodes
We consider the problem of secure unicast transmission between two nodes in a
directed graph, where an adversary eavesdrops/jams a subset of nodes. This
adversarial setting is in contrast to traditional ones where the adversary
controls a subset of links. In particular, we study, in the main, the class of
routing-only schemes (as opposed to those allowing coding inside the network).
Routing-only schemes usually have low implementation complexity, yet a
characterization of the rates achievable by such schemes was open prior to this
work. We first propose an LP based solution for secure communication against
eavesdropping, and show that it is information-theoretically rate-optimal among
all routing-only schemes. The idea behind our design is to balance information
flow in the network so that no subset of nodes observe "too much" information.
Interestingly, we show that the rates achieved by our routing-only scheme are
always at least as good as, and sometimes better, than those achieved by
"na\"ive" network coding schemes (i.e. the rate-optimal scheme designed for the
traditional scenario where the adversary controls links in a network rather
than nodes.) We also demonstrate non-trivial network coding schemes that
achieve rates at least as high as (and again sometimes better than) those
achieved by our routing schemes, but leave open the question of characterizing
the optimal rate-region of the problem under all possible coding schemes. We
then extend these routing-only schemes to the adversarial node-jamming
scenarios and show similar results. During the journey of our investigation, we
also develop a new technique that has the potential to derive non-trivial
bounds for general secure-communication schemes
Multishot Adversarial Network Decoding
We investigate adversarial network coding and decoding focusing on the
multishot regime. Errors can occur on a proper subset of the network edges and
are modeled via an adversarial channel. The paper contains both bounds and
capacity-achieving schemes for the Diamond Network and the Mirrored Diamond
Network. We also initiate the study of the generalizations of these networks
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