  Network intrusion detection systems (NIDSs) have a role of identifying
malicious activities by monitoring the behavior of networks. Due to the
currently high volume of networks trafic in addition to the increased number of
attacks and their dynamic properties, NIDSs have the challenge of improving
their classification performance. Bio-Inspired Optimization Algorithms (BIOs)
are used to automatically extract the the discrimination rules of normal or
abnormal behavior to improve the classification accuracy and the detection
ability of NIDS. A quantum vaccined immune clonal algorithm with the estimation
of distribution algorithm (QVICA-with EDA) is proposed in this paper to build a
new NIDS. The proposed algorithm is used as classification algorithm of the new
NIDS where it is trained and tested using the KDD data set. Also, the new NIDS
is compared with another detection system based on particle swarm optimization
(PSO). Results shows the ability of the proposed algorithm of achieving high
intrusions classification accuracy where the highest obtained accuracy is 94.8
%.
