Publication

Abstract : Modern threats are increasing as traditional cyber security methods lose their efficacy. Predicting attacks before they happen, however, can be enhanced by the ability of big data-based Quantum computing to train anomaly-based algorithms. To normalize data the most pertinent collection of features has been chosen and big data analytics tools are used for feature scaling and selection. The improved models’ prediction accuracy has been compared with proposed model after experimental analysis. According to the findings of performance evaluation, and comparative analysis it is found that Quantum computing-based intrusion detection have good accuracy as compared to classical method-based intrusion detection in the field of cyber security. The extensive literature survey based on existing models is also done in this article using a large dataset (Big Data Set- CIC-Bell-IDS2017) in which the predictive method trained three machine learning classifiers separately before and after feature selection. The Comparative analysis of Quantum machine learning models for intrusion detection using Big Data based Quantum machine learning methods is also done in this article.