  Network security has become the biggest concern in the area of cyber security
because of the exponential growth in computer networks and applications.
Intrusion detection plays an important role in the security of information
systems or networks devices. The purpose of an intrusion detection system (IDS)
is to detect malicious activities and then generate an alarm against these
activities. Having a large amount of data is one of the key problems in
detecting attacks. Most of the intrusion detection systems use all features of
datasets to evaluate the models and result in is, low detection rate, high
computational time and uses of many computer resources. For fast attacks
detection IDS needs a lightweight data. A feature selection method plays a key
role to select best features to achieve maximum accuracy. This research work
conduct experiments by considering on two updated attacks datasets, UNSW-NB15
and CICDDoS2019. This work suggests a wrapper based Genetic Algorithm (GA)
features selection method with ensemble classifiers. GA select the best feature
subsets and achieve high accuracy, detection rate (DR) and low false alarm rate
(FAR) compared to existing approaches. This research focuses on multi-class
classification. Implements two ensemble methods: stacking and bagging to detect
different types of attacks. The results show that GA improve the accuracy
significantly with stacking ensemble classifier.
