3,549 research outputs found
Let Your CyberAlter Ego Share Information and Manage Spam
Almost all of us have multiple cyberspace identities, and these {\em
cyber}alter egos are networked together to form a vast cyberspace social
network. This network is distinct from the world-wide-web (WWW), which is being
queried and mined to the tune of billions of dollars everyday, and until
recently, has gone largely unexplored. Empirically, the cyberspace social
networks have been found to possess many of the same complex features that
characterize its real counterparts, including scale-free degree distributions,
low diameter, and extensive connectivity. We show that these topological
features make the latent networks particularly suitable for explorations and
management via local-only messaging protocols. {\em Cyber}alter egos can
communicate via their direct links (i.e., using only their own address books)
and set up a highly decentralized and scalable message passing network that can
allow large-scale sharing of information and data. As one particular example of
such collaborative systems, we provide a design of a spam filtering system, and
our large-scale simulations show that the system achieves a spam detection rate
close to 100%, while the false positive rate is kept around zero. This system
has several advantages over other recent proposals (i) It uses an already
existing network, created by the same social dynamics that govern our daily
lives, and no dedicated peer-to-peer (P2P) systems or centralized server-based
systems need be constructed; (ii) It utilizes a percolation search algorithm
that makes the query-generated traffic scalable; (iii) The network has a built
in trust system (just as in social networks) that can be used to thwart
malicious attacks; iv) It can be implemented right now as a plugin to popular
email programs, such as MS Outlook, Eudora, and Sendmail.Comment: 13 pages, 10 figure
An ontology enhanced parallel SVM for scalable spam filter training
This is the post-print version of the final paper published in Neurocomputing. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Spam, under a variety of shapes and forms, continues to inflict increased damage. Varying approaches including Support Vector Machine (SVM) techniques have been proposed for spam filter training and classification. However, SVM training is a computationally intensive process. This paper presents a MapReduce based parallel SVM algorithm for scalable spam filter training. By distributing, processing and optimizing the subsets of the training data across multiple participating computer nodes, the parallel SVM reduces the training time significantly. Ontology semantics are employed to minimize the impact of accuracy degradation when distributing the training data among a number of SVM classifiers. Experimental results show that ontology based augmentation improves the accuracy level of the parallel SVM beyond the original sequential counterpart
Analysis of adversarial attacks against CNN-based image forgery detectors
With the ubiquitous diffusion of social networks, images are becoming a
dominant and powerful communication channel. Not surprisingly, they are also
increasingly subject to manipulations aimed at distorting information and
spreading fake news. In recent years, the scientific community has devoted
major efforts to contrast this menace, and many image forgery detectors have
been proposed. Currently, due to the success of deep learning in many
multimedia processing tasks, there is high interest towards CNN-based
detectors, and early results are already very promising. Recent studies in
computer vision, however, have shown CNNs to be highly vulnerable to
adversarial attacks, small perturbations of the input data which drive the
network towards erroneous classification. In this paper we analyze the
vulnerability of CNN-based image forensics methods to adversarial attacks,
considering several detectors and several types of attack, and testing
performance on a wide range of common manipulations, both easily and hardly
detectable
Hikester - the event management application
Today social networks and services are one of the most important part of our
everyday life. Most of the daily activities, such as communicating with
friends, reading news or dating is usually done using social networks. However,
there are activities for which social networks do not yet provide adequate
support. This paper focuses on event management and introduces "Hikester". The
main objective of this service is to provide users with the possibility to
create any event they desire and to invite other users. "Hikester" supports the
creation and management of events like attendance of football matches, quest
rooms, shared train rides or visit of museums in foreign countries. Here we
discuss the project architecture as well as the detailed implementation of the
system components: the recommender system, the spam recognition service and the
parameters optimizer
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