Intrusion Detection by XGBoost Model Tuned by Improved Multi-verse Optimizer

DOI

10.2991/978-94-6463-110-4_15How to use a DOI?

Keywords

intrusion detection; swarm intelligence; XGBoost; optimization; multi-verse optimizer algorithm

Abstract

Artificial intelligence and internet of things (IoT) fields have contributed to the flourishment of the industry 4.0 concept. The main benefits include the improvements in terms of device communication, productivity, and efficiency. Nevertheless, there is a downside concerning the security of these systems. The amount of devices and their diversity prove a security risk. Due to this intrusion detection systems are paramount. This paper proposes a novel framework exploiting extreme gradient boosting machine learning model which is optimized by a modified version of the multi-verse optimizer metaheuristic. The UNSW-NB intrusion dataset was used for experimental purposes on which the other cutting-edge techniques were tested and compared. The results provide the proof of improvement as the proposed method outperformed all other overall metaheuristic performances. Furthermore, the units for truthfulness and polarity for the case have been established as a standard evaluation system. True and false positives exist alongside the same negative counterparts. The results provided by these metrics have been visualized and used for further comparison proving the superiority of the performance of the proposed solution.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Aleksandar Petrovic
AU  - Milos Antonijevic
AU  - Ivana Strumberger
AU  - Nebojsa Budimirovic
AU  - Nikola Savanovic
AU  - Stefana Janicijevic
PY  - 2023
DA  - 2023/01/30
TI  - Intrusion Detection by XGBoost Model Tuned by Improved Multi-verse Optimizer
BT  - Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)
PB  - Atlantis Press
SP  - 203
EP  - 218
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-110-4_15
DO  - 10.2991/978-94-6463-110-4_15
ID  - Petrovic2023
ER  -