European Journal of Molecular & Clinical Medicine

Abstract

Data mining is now used by many institutions widely and generally. Intrusion detection
for network operators & security specialists is one of the top priorities and challenges.
Sensitive data, anonymity and device availability from attacks are protected by the Intrusion
detection system. In order to describe resources from those in the database through a network,
IDS uses data mining techniques. A robust algorithm must also be built to produce successful
rules for the detection of attacks. In this paper, optimization algorithms focused on
classification were used to detect attacks over the NSL KDD dataset. Depending on this
stranglehold, the current method is explained an improved Hoeffiding Induction Tree
algorithm to resolve the drawbacks. The results demonstrate that the proposed HITNB
algorithm has improved precision, a lower alarm rate and the ability to detect a new type
efficiently.