783 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
Stacking classifiers for anti-spam filtering of e-mail
We evaluate empirically a scheme for combining classifiers, known as stacked
generalization, in the context of anti-spam filtering, a novel cost-sensitive
application of text categorization. Unsolicited commercial e-mail, or "spam",
floods mailboxes, causing frustration, wasting bandwidth, and exposing minors
to unsuitable content. Using a public corpus, we show that stacking can improve
the efficiency of automatically induced anti-spam filters, and that such
filters can be used in real-life applications
Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach
We investigate the performance of two machine learning algorithms in the
context of anti-spam filtering. The increasing volume of unsolicited bulk
e-mail (spam) has generated a need for reliable anti-spam filters. Filters of
this type have so far been based mostly on keyword patterns that are
constructed by hand and perform poorly. The Naive Bayesian classifier has
recently been suggested as an effective method to construct automatically
anti-spam filters with superior performance. We investigate thoroughly the
performance of the Naive Bayesian filter on a publicly available corpus,
contributing towards standard benchmarks. At the same time, we compare the
performance of the Naive Bayesian filter to an alternative memory-based
learning approach, after introducing suitable cost-sensitive evaluation
measures. Both methods achieve very accurate spam filtering, outperforming
clearly the keyword-based filter of a widely used e-mail reader
Learning to detect spam messages
The problem of unwanted e-mails (or spam messages) has been increasing for years. Different methods have been proposed in order to deal with this problem wich includes blacklists of known spammers, handcrafted rules and machine learning techniques.
In this paper we investigate the performance of the k Nearest Neighbours (k-NN) method in spam detection tasks. At this end, a number of different document codifications were tested.
Moreover, we study how the vocabulary size reduction affects this task. In the experimental design, different k values were considered and results were analyzed with respect to a public mailing list and personal e-mail collections. The experiments showed that results with public mailing lists tend to be very optimistic and they should not be considered representative of those expected with personal user accounts.VI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI
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