Zusammenfassung
Artificial immune systems (AIS) use the concepts and algorithms inspired
by the theory of how the human immune system works. This document
presents the design and initial evaluation of a new artificial immune
system for collaborative spam filtering. Collaborative spam filtering
allows for the detection of not-previously-seen spam content, by
exploiting its bulkiness. Our system uses two novel and possibly
advantageous techniques for collaborative spam filtering. The first
novelty is local processing of the signatures created from the emails
prior to deciding whether and which of the generated signatures will
be exchanged with other collaborating antispam systems. This processing
exploits both the email-content profiles of the users and implicit
or explicit feedback from the users, and it uses customized AIS algorithms.
The idea is to enable only good quality and effective information
to be exchanged among collaborating antispam systems. The second
novelty is the representation of the email content, based on a sampling
of text strings of a predefined length and at random positions within
the emails, and a use of a custom similarity hashing of these strings.
Compared to the existing signature generation methods, the proposed
sampling and hashing are aimed at achieving a better resistance to
spam obfuscation (especially text additions) - which means better
detection of spam, and a better precision in learning spam patterns
and distinguishing them well from normal text - which means lowering
the false detection of good emails. Initial evaluation of the system
shows that it achieves promising detection results under modest collaboration,
and that it is rather resistant under the tested obfuscation. In
order to confirm our understanding of why the system performed well
under this initial evaluation, an additional factorial analysis should
be done. Also, evaluation under more sophisticated spammer models
is necessary for a more complete assessment of the system abilities.
Nutzer