  With the increasing number of new attacks on ever growing network traffic, it
is becoming challenging to alert immediately any malicious activities to avoid
loss of sensitive data and money. This is making intrusion detection as one of
the major areas of concern in network security. Anomaly based network intrusion
detection technique is one of the most commonly used technique. Depending upon
the dataset used to test those techniques, the accuracy varies. Most of the
times this dataset does not represent the real network traffic. Considering
this, this project involves analysis of different machine learning algorithms
used in intrusion detection systems, when tested upon two datasets which are
similar to current real world network traffic(CICIDS2017) and an improvement of
KDD 99 (NSL-KDD). After the analysis of different intrusion detection systems
on both the datasets, this project aimed to develop a new hybrid model for
intrusion detection systems. This new hybrid approach combines decision tree
and random forest algorithms using stacking scheme to achieve an accuracy of
85.2% and precision of 86.2% for NSL-KDD dataset, and achieve an accuracy of
98% and precision of 98% for CICIDS2017 dataset.
