88,092 research outputs found
Location-Verification and Network Planning via Machine Learning Approaches
In-region location verification (IRLV) in wireless networks is the problem of
deciding if user equipment (UE) is transmitting from inside or outside a
specific physical region (e.g., a safe room). The decision process exploits the
features of the channel between the UE and a set of network access points
(APs). We propose a solution based on machine learning (ML) implemented by a
neural network (NN) trained with the channel features (in particular, noisy
attenuation values) collected by the APs for various positions both inside and
outside the specific region. The output is a decision on the UE position
(inside or outside the region). By seeing IRLV as an hypothesis testing
problem, we address the optimal positioning of the APs for minimizing either
the area under the curve (AUC) of the receiver operating characteristic (ROC)
or the cross entropy (CE) between the NN output and ground truth (available
during the training). In order to solve the minimization problem we propose a
twostage particle swarm optimization (PSO) algorithm. We show that for a long
training and a NN with enough neurons the proposed solution achieves the
performance of the Neyman-Pearson (N-P) lemma.Comment: Accepted for Workshop on Machine Learning for Communications, June 07
2019, Avignon, Franc
Steganography: a class of secure and robust algorithms
This research work presents a new class of non-blind information hiding
algorithms that are stego-secure and robust. They are based on some finite
domains iterations having the Devaney's topological chaos property. Thanks to a
complete formalization of the approach we prove security against watermark-only
attacks of a large class of steganographic algorithms. Finally a complete study
of robustness is given in frequency DWT and DCT domains.Comment: Published in The Computer Journal special issue about steganograph
Resilient Distributed Energy Management for Systems of Interconnected Microgrids
In this paper, distributed energy management of interconnected microgrids,
which is stated as a dynamic economic dispatch problem, is studied. Since the
distributed approach requires cooperation of all local controllers, when some
of them do not comply with the distributed algorithm that is applied to the
system, the performance of the system might be compromised. Specifically, it is
considered that adversarial agents (microgrids with their controllers) might
implement control inputs that are different than the ones obtained from the
distributed algorithm. By performing such behavior, these agents might have
better performance at the expense of deteriorating the performance of the
regular agents. This paper proposes a methodology to deal with this type of
adversarial agents such that we can still guarantee that the regular agents can
still obtain feasible, though suboptimal, control inputs in the presence of
adversarial behaviors. The methodology consists of two steps: (i) the
robustification of the underlying optimization problem and (ii) the
identification of adversarial agents, which uses hypothesis testing with
Bayesian inference and requires to solve a local mixed-integer optimization
problem. Furthermore, the proposed methodology also prevents the regular agents
to be affected by the adversaries once the adversarial agents are identified.
In addition, we also provide a sub-optimality certificate of the proposed
methodology.Comment: 8 pages, Conference on Decision and Control (CDC) 201
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