This thesis presents the case of dynamically and
adaptively learning signatures for network intrusion
detection using genetic based machine learning
techniques. The two major criticisms of the
signature based intrusion detection systems are
their i) reliance on domain experts to handcraft
intrusion signatures and ii) inability to detect
previously unknown attacks or the attacks for which
no signatures are available at the time.
In this thesis, we present a biologically-inspired
computational approach to address these two issues.
This is done by adaptively learning maximally
general...