@Article{JICS-15-022,
author = {Azhagiri Mahendiran, Rajesh Appusamy , Rajesh Prabhakaran and Gowtham Sethupathi},
title = {CRF Based Intrusion Detection System Using Genetic Search Feature Selection for NSSA},
journal = {Journal of Information and Computing Science},
year = {2020},
volume = {15},
number = {1},
pages = {022--030},
abstract = {Abstract -  Network security situational awareness systems helps in better managing the security concerns 
of  a  network,  by  monitoring  for  any  anomalies  in  the  network  connections  and  recommending  remedial 
actions upon detecting an attack. An Intrusion Detection System helps in identifying the security concerns of 
a network, by monitoring for any anomalies in the network connections. We have proposed a CRF based IDS 
system using genetic search feature selection algorithm for network security situational awareness to detect 
any  anomalies  in  the  network.  The  conditional  random  fields  being  discriminative  models  are  capable  of 
directly  modeling  the  conditional  probabilities  rather  than  joint  probabilities  there  by  achieving  better 
classification accuracy. The genetic search feature selection algorithm is capable of identifying the optimal 
subset  among  the  features  based  on  the  best  population  of  features  associated  with  the  target  class.  The 
proposed system, when trained and tested on the bench mark NSL-KDD dataset exhibited higher accuracy in 
identifying an attack and also classifying the attack category. 
},
issn = {3080-180X},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22394.html}
}