15,679 research outputs found
Why It Takes So Long to Connect to a WiFi Access Point
Today's WiFi networks deliver a large fraction of traffic. However, the
performance and quality of WiFi networks are still far from satisfactory. Among
many popular quality metrics (throughput, latency), the probability of
successfully connecting to WiFi APs and the time cost of the WiFi connection
set-up process are the two of the most critical metrics that affect WiFi users'
experience. To understand the WiFi connection set-up process in real-world
settings, we carry out measurement studies on million mobile users from
representative cities associating with million APs in billion WiFi
sessions, collected from a mobile "WiFi Manager" App that tops the Android/iOS
App market. To the best of our knowledge, we are the first to do such large
scale study on: how large the WiFi connection set-up time cost is, what factors
affect the WiFi connection set-up process, and what can be done to reduce the
WiFi connection set-up time cost. Based on the measurement analysis, we develop
a machine learning based AP selection strategy that can significantly improve
WiFi connection set-up performance, against the conventional strategy purely
based on signal strength, by reducing the connection set-up failures from
to and reducing time costs of the connection set-up
processes by more than times.Comment: 11pages, conferenc
Biometric Backdoors: A Poisoning Attack Against Unsupervised Template Updating
In this work, we investigate the concept of biometric backdoors: a template
poisoning attack on biometric systems that allows adversaries to stealthily and
effortlessly impersonate users in the long-term by exploiting the template
update procedure. We show that such attacks can be carried out even by
attackers with physical limitations (no digital access to the sensor) and zero
knowledge of training data (they know neither decision boundaries nor user
template). Based on the adversaries' own templates, they craft several
intermediate samples that incrementally bridge the distance between their own
template and the legitimate user's. As these adversarial samples are added to
the template, the attacker is eventually accepted alongside the legitimate
user. To avoid detection, we design the attack to minimize the number of
rejected samples.
We design our method to cope with the weak assumptions for the attacker and
we evaluate the effectiveness of this approach on state-of-the-art face
recognition pipelines based on deep neural networks. We find that in scenarios
where the deep network is known, adversaries can successfully carry out the
attack over 70% of cases with less than ten injection attempts. Even in
black-box scenarios, we find that exploiting the transferability of adversarial
samples from surrogate models can lead to successful attacks in around 15% of
cases. Finally, we design a poisoning detection technique that leverages the
consistent directionality of template updates in feature space to discriminate
between legitimate and malicious updates. We evaluate such a countermeasure
with a set of intra-user variability factors which may present the same
directionality characteristics, obtaining equal error rates for the detection
between 7-14% and leading to over 99% of attacks being detected after only two
sample injections.Comment: 12 page
Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier
Active authentication refers to the process in which users are unobtrusively
monitored and authenticated continuously throughout their interactions with
mobile devices. Generally, an active authentication problem is modelled as a
one class classification problem due to the unavailability of data from the
impostor users. Normally, the enrolled user is considered as the target class
(genuine) and the unauthorized users are considered as unknown classes
(impostor). We propose a convolutional neural network (CNN) based approach for
one class classification in which a zero centered Gaussian noise and an
autoencoder are used to model the pseudo-negative class and to regularize the
network to learn meaningful feature representations for one class data,
respectively. The overall network is trained using a combination of the
cross-entropy and the reconstruction error losses. A key feature of the
proposed approach is that any pre-trained CNN can be used as the base network
for one class classification. Effectiveness of the proposed framework is
demonstrated using three publically available face-based active authentication
datasets and it is shown that the proposed method achieves superior performance
compared to the traditional one class classification methods. The source code
is available at: github.com/otkupjnoz/oc-acnn.Comment: Accepted and to appear at AFGR 201
Feature Representation for Online Signature Verification
Biometrics systems have been used in a wide range of applications and have
improved people authentication. Signature verification is one of the most
common biometric methods with techniques that employ various specifications of
a signature. Recently, deep learning has achieved great success in many fields,
such as image, sounds and text processing. In this paper, deep learning method
has been used for feature extraction and feature selection.Comment: 10 pages, 10 figures, Submitted to IEEE Transactions on Information
Forensics and Securit
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