4,693 research outputs found
Machine Learning For In-Region Location Verification In Wireless Networks
In-region location verification (IRLV) aims at verifying whether a user is
inside a region of interest (ROI). In wireless networks, IRLV can exploit the
features of the channel between the user and a set of trusted access points. In
practice, the channel feature statistics is not available and we resort to
machine learning (ML) solutions for IRLV. We first show that solutions based on
either neural networks (NNs) or support vector machines (SVMs) and typical loss
functions are Neyman-Pearson (N-P)-optimal at learning convergence for
sufficiently complex learning machines and large training datasets . Indeed,
for finite training, ML solutions are more accurate than the N-P test based on
estimated channel statistics. Then, as estimating channel features outside the
ROI may be difficult, we consider one-class classifiers, namely auto-encoders
NNs and one-class SVMs, which however are not equivalent to the generalized
likelihood ratio test (GLRT), typically replacing the N-P test in the one-class
problem. Numerical results support the results in realistic wireless networks,
with channel models including path-loss, shadowing, and fading
Attack Detection in Sensor Network Target Localization Systems with Quantized Data
We consider a sensor network focused on target localization, where sensors
measure the signal strength emitted from the target. Each measurement is
quantized to one bit and sent to the fusion center. A general attack is
considered at some sensors that attempts to cause the fusion center to produce
an inaccurate estimation of the target location with a large mean-square-error.
The attack is a combination of man-in-the-middle, hacking, and spoofing attacks
that can effectively change both signals going into and coming out of the
sensor nodes in a realistic manner. We show that the essential effect of
attacks is to alter the estimated distance between the target and each attacked
sensor to a different extent, giving rise to a geometric inconsistency among
the attacked and unattacked sensors. Hence, with the help of two secure
sensors, a class of detectors are proposed to detect the attacked sensors by
scrutinizing the existence of the geometric inconsistency. We show that the
false alarm and miss probabilities of the proposed detectors decrease
exponentially as the number of measurement samples increases, which implies
that for sufficiently large number of samples, the proposed detectors can
identify the attacked and unattacked sensors with any required accuracy
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
Exploiting Lack of Hardware Reciprocity for Sender-Node Authentication at the PHY Layer
This paper proposes to exploit the so-called reciprocity
parameters (modelling non-reciprocal communication
hardware) to use them as decision metric for binary hypothesis
testing based authentication framework at a receiver node Bob.
Specifically, Bob first learns the reciprocity parameters of the
legitimate sender Alice via initial training. Then, during the test
phase, Bob first obtains a measurement of reciprocity parameters
of channel occupier (Alice, or, the intruder Eve). Then, with
ground truth and current measurement both in hand, Bob
carries out the hypothesis testing to automatically accept (reject)
the packets sent by Alice (Eve). For the proposed scheme, we
provide its success rate (the detection probability of Eve), and
its performance comparison with other schemes
Byzantine Attack and Defense in Cognitive Radio Networks: A Survey
The Byzantine attack in cooperative spectrum sensing (CSS), also known as the
spectrum sensing data falsification (SSDF) attack in the literature, is one of
the key adversaries to the success of cognitive radio networks (CRNs). In the
past couple of years, the research on the Byzantine attack and defense
strategies has gained worldwide increasing attention. In this paper, we provide
a comprehensive survey and tutorial on the recent advances in the Byzantine
attack and defense for CSS in CRNs. Specifically, we first briefly present the
preliminaries of CSS for general readers, including signal detection
techniques, hypothesis testing, and data fusion. Second, we analyze the spear
and shield relation between Byzantine attack and defense from three aspects:
the vulnerability of CSS to attack, the obstacles in CSS to defense, and the
games between attack and defense. Then, we propose a taxonomy of the existing
Byzantine attack behaviors and elaborate on the corresponding attack
parameters, which determine where, who, how, and when to launch attacks. Next,
from the perspectives of homogeneous or heterogeneous scenarios, we classify
the existing defense algorithms, and provide an in-depth tutorial on the
state-of-the-art Byzantine defense schemes, commonly known as robust or secure
CSS in the literature. Furthermore, we highlight the unsolved research
challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral
Secure neighbor discovery in wireless sensor networks using range-free localization techniques
Si una red inalámbrica de sensores se implementa en un entorno hostil, las limitaciones intrínsecas a los nodos conllevan muchos problemas de seguridad. En este artículo se aborda un ataque particular a los protocolos de localización y descubrimiento de vecinos, llevada a cabo por dos nodos que actúan en connivencia y establecen un "agujero de gusano" para tratar de engañar a un nodo aislado, haciéndole creer que se encuentra en la vecindad de un conjunto de nodos locales. Para contrarrestar este tipo de amenazas, se presenta un marco de actuación genéricamente denominado "detection of wormhole attacks using range-free methods" (DWARF) dentro del cual derivamos dos estrategias para de detección de agujeros de gusano: el primer enfoque (DWARFLoc) realiza conjuntamente la localización y la detección de ataques, mientras que el otro (DWARFTest) valida la posición estimada por el nodo una vez finalizado el protocolo de localización. Las simulaciones muestran que ambas estrategias son eficaces en la detección de ataques tipo "agujero de gusano", y sus prestaciones se comparan con las de un test convencional basado en la razón de verosimilitudes
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