| dc.description.abstract | Nowadays, in data technology, data preservation has become a good issue. Computers
and completely different security breaches are incessantly attacked by
security threats. There are completely different malicious network based or
host based attacks that are a massive threat to networks. For protecting computer
and networks from attacks and threats, the intrusion detection system
has been used that is signature based. An Intrusion detection system gathers
needed data to perform analytical actions. So that, it could determine threats
that would be generated from a system or organizations inner or outer atmosphere.
A great amount of data that is worked by the intrusion detection system,
takes varied inappropriate and unnecessary features that result in raised
execution time and low detection rate. As a result, in intrusion detection, an
undeniable role is played by feature selection. To cover up such aspect various
literature were revealed by completely different profound authors. During this
analysis, we’ve approached to select some important features based on some
feature selection algorithm so that the computational cost, space complexity
and intrusion detection time can be reduced. Our analysis over NSL-KDD data
set shows that once feature reduction, except Naive Bayes classifier the accuracy
of the foremost classifiers is sort of as same because the performance with
none feature reduction. | en_US |
