A Network Intrusions Detection System based on a Quantum Bio Inspired Algorithm
This work addresses the problem of detecting network intrusions for cybersecurity applications, but it appears incremental as it builds on existing bio-inspired optimization methods.
The paper tackled the challenge of improving classification performance in network intrusion detection systems (NIDS) due to high traffic and dynamic attacks, proposing a quantum bio-inspired algorithm (QVICA-with EDA) that achieved a highest accuracy of 94.8% on the KDD dataset.
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 %.