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              <text>XGBoost Design byMulti-verse Optimiser: An Application forNetwork Intrusion Detection</text>
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              <text>Computer networks; Intrusion detection; Machine learning; Multi-verse optimiser; Optimisation; Parameter tuning</text>
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              <text>This article presents the results of an experimental study, which aims to assess the efficiency of the performance of a novel multi-verse optimiser algorithm for the optimisation of parameters of a network intrusion detection system event classifier. The article gives an overview of computer network intrusion detection, outlines common issues faced by software solutions tackling this problem, and proposes using a machine learning algorithm to help solve some of these common issues. An XGBoost classification model with a multi-verse optimisation algorithm for adaptive search and optimisation is used to solve the network intrusion detection system event classifier hyper-parameter optimisation problem. Results of this experimental study are presented and discussed, the improvements compared to previous solutions is shown, and a possible direction of future work in this domain is given in the conclusion.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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              <text>Tair M.; Bacanin N.; Zivkovic M.; Venkatachalam K.; Strumberger I.</text>
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              <text>Lecture Notes on Data Engineering and Communications Technologies, Vol-126, pp. 1-16.</text>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-19-2069-1_1" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-19-2069-1_1&lt;/a&gt;
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              <text>ISSN: 23674512</text>
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              <text>Tair M., Singidunum University, Danijelova 32, Belgrade, 11000, Serbia; Bacanin N., Singidunum University, Danijelova 32, Belgrade, 11000, Serbia; Zivkovic M., Singidunum University, Danijelova 32, Belgrade, 11000, Serbia; Venkatachalam K., Christ (Deemed to be University), Bangalore, India; Strumberger I., Singidunum University, Danijelova 32, Belgrade, 11000, Serbia</text>
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