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

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.