@Article{JICS-9-037,
author = {MradulDhakar and Akhilesh Tiwari},
title = {A Novel Data Mining based Hybrid Intrusion Detection Framework},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {9},
number = {1},
pages = {037--048},
abstract = { The  prosperity  of  technology  worldwide  has  made  the  concerns  of  security  tend  to  increase 
rapidly.  The  enormous  usage  of  internetworking  has  raised  the  need  of  protecting  system(s)  as  well  as 
intrusive  activities,  several 
network(s)  from 
countermeasures have been found in literature viz. firewall, antivirus and currently widely preferred Intrusion 
detection System (IDS). IDS, is a detection mechanism for detecting the intrusive activities hidden among the 
normal activities. The revolutionary establishment of IDS has attracted analysts to work dedicatedly enabling 
the  system  to  deal  with  technological  advancements.  Hence  in  this  regard,  various  beneficial  schemes  and 
models have been proposed in order to achieve enhanced IDS. This paper proposes a novel hybrid model for 
intrusion  detection.    The  proposed  framework  in  this  paper  may  be  expected  as  another  step  towards 
advancement of IDS. The framework utilizes the crucial data mining classification algorithms beneficial for 
intrusion detection. The Hybrid framework would henceforth, will lead to effective, adaptive and intelligent 
intrusion detection. 
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22595.html}
}