45,435 research outputs found
Ubic: Bridging the gap between digital cryptography and the physical world
Advances in computing technology increasingly blur the boundary between the
digital domain and the physical world. Although the research community has
developed a large number of cryptographic primitives and has demonstrated their
usability in all-digital communication, many of them have not yet made their
way into the real world due to usability aspects. We aim to make another step
towards a tighter integration of digital cryptography into real world
interactions. We describe Ubic, a framework that allows users to bridge the gap
between digital cryptography and the physical world. Ubic relies on
head-mounted displays, like Google Glass, resource-friendly computer vision
techniques as well as mathematically sound cryptographic primitives to provide
users with better security and privacy guarantees. The framework covers key
cryptographic primitives, such as secure identification, document verification
using a novel secure physical document format, as well as content hiding. To
make a contribution of practical value, we focused on making Ubic as simple,
easily deployable, and user friendly as possible.Comment: In ESORICS 2014, volume 8712 of Lecture Notes in Computer Science,
pp. 56-75, Wroclaw, Poland, September 7-11, 2014. Springer, Berlin, German
A traffic classification method using machine learning algorithm
Applying concepts of attack investigation in IT industry, this idea has been developed to design
a Traffic Classification Method using Data Mining techniques at the intersection of Machine
Learning Algorithm, Which will classify the normal and malicious traffic. This classification will
help to learn about the unknown attacks faced by IT industry. The notion of traffic classification
is not a new concept; plenty of work has been done to classify the network traffic for
heterogeneous application nowadays. Existing techniques such as (payload based, port based
and statistical based) have their own pros and cons which will be discussed in this
literature later, but classification using Machine Learning techniques is still an open field to explore and has provided very promising results up till now
Attributes2Classname: A discriminative model for attribute-based unsupervised zero-shot learning
We propose a novel approach for unsupervised zero-shot learning (ZSL) of
classes based on their names. Most existing unsupervised ZSL methods aim to
learn a model for directly comparing image features and class names. However,
this proves to be a difficult task due to dominance of non-visual semantics in
underlying vector-space embeddings of class names. To address this issue, we
discriminatively learn a word representation such that the similarities between
class and combination of attribute names fall in line with the visual
similarity. Contrary to the traditional zero-shot learning approaches that are
built upon attribute presence, our approach bypasses the laborious
attribute-class relation annotations for unseen classes. In addition, our
proposed approach renders text-only training possible, hence, the training can
be augmented without the need to collect additional image data. The
experimental results show that our method yields state-of-the-art results for
unsupervised ZSL in three benchmark datasets.Comment: To appear at IEEE Int. Conference on Computer Vision (ICCV) 201
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