Аннотация
The hierarchical RSS-DSS algorithm is introduced for
dynamically filtering large datasets based on the
concepts of training pattern age and difficulty, while
using a data structure to facilitate the efficient use
of memory hierarchies. Such a scheme provides the basis
for training genetic programming (GP) on a data set of
half a million patterns in 15 min. The method is
generic, thus, not specific to a particular GP
structure, computing platform, or application context.
The method is demonstrated on the real-world KDD-99
intrusion detection data set, resulting in solutions
competitive with those identified in the original
KDD-99 competition, while only using a fraction of the
original features. Parameters of the RSS-DSS algorithm
are demonstrated to be effective over a wide range of
values. An analysis of different cost functions
indicates that hierarchical fitness functions provide
the most effective solutions.
- (artificial
- (dss),
- algorithm,
- algorithms,
- anomaly
- cost
- data
- data,
- dataset
- detection
- detection,
- dynamical
- filtering,
- fitness
- function,
- functions,
- genetic
- hierarchical
- intelligence),
- intrusion
- kdd-99
- large
- learning
- mining,
- of
- programming
- programming,
- real-world
- rss-dss
- security
- selection
- set,dynamic
- sets
- subset
- training,
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