ora-schriften-s-2013

Prof. Dr.-Ing. habil. Ulrike Lucke
Up to now, the description of algorithms to
analyze textual or visual content elements
was rather vague. The next section provides
in introduction to technical possibilities for
this analysis.
4. Which mechanisms can we use
for detection?
The benefits of such a solution are that
every decision (in our case: on approval or
rejection of a message to be classified as
suspicious or not) can be justified in terms
of the rules that have been applied.
Moreover, there is experience from several
application fields that are dealing with rulebased
systems for several years.
For the field of
Cyber-Grooming,
extensive
empirical studies
will be necessary
in order to make
pattern of
offenders and
victims explicit,
before automated
detection
based on such
rules can be
applied.
The description of image analysis revealed
that a tradeoff between performance and
accuracy of detection algorithms may
become necessary, given the limited time
for analyzing and rating messages in a
stream. Moreover, technology asks for
another tradeoff between accuracy and
transparency, since transparent detection
algorithms (i.e. those who come along with
a reason for their rating) tend to be rather
error-prone, and vice-versa. The next two
sections present relevant approaches at
either ends of this scale, followed by a third
section describing a system architecture
that integrates such algorithms and tools
into a complete framework.
4.1 Rule sets
Rule-based systems are a well-studied
approach to make knowledge of human
experts explicit and interpretable by
automated processing. Such an expert
system defines a set of conditions on how
to handle incoming data [12]. For this
purpose, it consists of two kinds of machinereadable
information:
●●
A knowledge base contains a set of
information that was proven to be
true.
●●
An interference engine describes a
set of operations how this knowledge
as well as incoming data can be
transformed.
The expert system tries to infer new
statements from this basis, with the final
goal to conclude with a statement on
approval or rejection of the message to be
classified.
However, there are some weak points. First
of all, the success of this approach depends
on the precision and completeness of given
rules. This requires explicit modeling of
knowledge in the respective field, which is a
challenge where empirical data is still
missing or not yet valid enough. For the
field of Cyber-Grooming, extensive
empirical studies will be necessary in order
to make pattern of offenders and victims
explicit, before automated detection based
on such rules can be applied. Finally,
complex rule sets make high demands on
processing power. Current research on
answer set programming [11] will help to
tackle this problem.
These pro’s and con’s make rule-based
systems more appropriate for textual
analysis in the detection process described
above.
4.2 Artificial neural networks
At the contrasting end of the scale, a wellknown
computational approach inspired by
nature is available. Artificial neural networks
imitate the structure and behavior of a brain
in order to make a decision [2]. A number of
switching elements (neurons) are arranged
in a multi-layered architecture. They are
connected with some preceding and
subsequent switches following a given
topology. Each connection is associated
with a certain weight in order to strengthen
or weaken the transmitted signal. Moreover,
every switch has its own scheme to derive
an output signal from the set of incoming
signals. The overall output is calculated by
the switches in the last layer. A network
functions as follows:
78 Special Edition 2013