89 research outputs found
Two-Factor Authentication Approach Based on Behavior Patterns for Defeating Puppet Attacks
Fingerprint traits are widely recognized for their unique qualities and
security benefits. Despite their extensive use, fingerprint features can be
vulnerable to puppet attacks, where attackers manipulate a reluctant but
genuine user into completing the authentication process. Defending against such
attacks is challenging due to the coexistence of a legitimate identity and an
illegitimate intent. In this paper, we propose PUPGUARD, a solution designed to
guard against puppet attacks. This method is based on user behavioral patterns,
specifically, the user needs to press the capture device twice successively
with different fingers during the authentication process. PUPGUARD leverages
both the image features of fingerprints and the timing characteristics of the
pressing intervals to establish two-factor authentication. More specifically,
after extracting image features and timing characteristics, and performing
feature selection on the image features, PUPGUARD fuses these two features into
a one-dimensional feature vector, and feeds it into a one-class classifier to
obtain the classification result. This two-factor authentication method
emphasizes dynamic behavioral patterns during the authentication process,
thereby enhancing security against puppet attacks. To assess PUPGUARD's
effectiveness, we conducted experiments on datasets collected from 31 subjects,
including image features and timing characteristics. Our experimental results
demonstrate that PUPGUARD achieves an impressive accuracy rate of 97.87% and a
remarkably low false positive rate (FPR) of 1.89%. Furthermore, we conducted
comparative experiments to validate the superiority of combining image features
and timing characteristics within PUPGUARD for enhancing resistance against
puppet attacks
Incorporating Recurrent Reinforcement Learning into Model Predictive Control for Adaptive Control in Autonomous Driving
Model Predictive Control (MPC) is attracting tremendous attention in the
autonomous driving task as a powerful control technique. The success of an MPC
controller strongly depends on an accurate internal dynamics model. However,
the static parameters, usually learned by system identification, often fail to
adapt to both internal and external perturbations in real-world scenarios. In
this paper, we firstly (1) reformulate the problem as a Partially Observed
Markov Decision Process (POMDP) that absorbs the uncertainties into
observations and maintains Markov property into hidden states; and (2) learn a
recurrent policy continually adapting the parameters of the dynamics model via
Recurrent Reinforcement Learning (RRL) for optimal and adaptive control; and
(3) finally evaluate the proposed algorithm (referred as ) in
CARLA simulator and leading to robust behaviours under a wide range of
perturbations
Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments
Deep learning-based physical-layer secret key generation (PKG) has been used
to overcome the imperfect uplink/downlink channel reciprocity in frequency
division duplexing (FDD) orthogonal frequency division multiplexing (OFDM)
systems. However, existing efforts have focused on key generation for users in
a specific environment where the training samples and test samples obey the
same distribution, which is unrealistic for real world applications. This paper
formulates the PKG problem in multiple environments as a learning-based problem
by learning the knowledge such as data and models from known environments to
generate keys quickly and efficiently in multiple new environments.
Specifically, we propose deep transfer learning (DTL) and meta-learning-based
channel feature mapping algorithms for key generation. The two algorithms use
different training methods to pre-train the model in the known environments,
and then quickly adapt and deploy the model to new environments. Simulation
results show that compared with the methods without adaptation, the DTL and
meta-learning algorithms both can improve the performance of generated keys. In
addition, the complexity analysis shows that the meta-learning algorithm can
achieve better performance than the DTL algorithm with less time, lower CPU and
GPU resources
Reconfigurable Intelligent Surface-Assisted Secret Key Generation in Spatially Correlated Channels
Reconfigurable intelligent surface (RIS) is a disruptive technology to
enhance the performance of physical-layer key generation (PKG) thanks to its
ability to smartly customize the radio environments. Existing RIS-assisted PKG
methods are mainly based on the idealistic assumption of an independent and
identically distributed (i.i.d.) channel model at both the base station (BS)
and the RIS. However, the i.i.d. model is inaccurate for a typical RIS in an
isotropic scattering environment and neglecting the existence of channel
spatial correlation would possibly degrade the PKG performance. In this paper,
we establish a general spatially correlated channel model and propose a new
channel probing framework based on the transmit and the reflective beamforming.
We derive a closed-form key generation rate (KGR) expression and formulate an
optimization problem, which is solved by using the low-complexity Block
Successive Upper-bound Minimization (BSUM) with Mirror-Prox method. Simulation
results show that compared to the existing methods based on the i.i.d. fading
model, our proposed method achieves about dB transmit power gain when the
spacing between two neighboring RIS elements is a quarter of the wavelength.
Also, the KGR increases significantly with the number of RIS elements while
that increases marginally with the number of BS antennas.Comment: arXiv admin note: text overlap with arXiv:2207.1175
LATITUDE: Robotic Global Localization with Truncated Dynamic Low-pass Filter in City-scale NeRF
Neural Radiance Fields (NeRFs) have made great success in representing
complex 3D scenes with high-resolution details and efficient memory.
Nevertheless, current NeRF-based pose estimators have no initial pose
prediction and are prone to local optima during optimization. In this paper, we
present LATITUDE: Global Localization with Truncated Dynamic Low-pass Filter,
which introduces a two-stage localization mechanism in city-scale NeRF. In
place recognition stage, we train a regressor through images generated from
trained NeRFs, which provides an initial value for global localization. In pose
optimization stage, we minimize the residual between the observed image and
rendered image by directly optimizing the pose on tangent plane. To avoid
convergence to local optimum, we introduce a Truncated Dynamic Low-pass Filter
(TDLF) for coarse-to-fine pose registration. We evaluate our method on both
synthetic and real-world data and show its potential applications for
high-precision navigation in large-scale city scenes. Codes and data will be
publicly available at https://github.com/jike5/LATITUDE.Comment: 7 pages, 6 figures, submitted to ICRA 202
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