Abstract:
Anomaly detection using machine learning (ML) algorithms is the key research theme in the modern digital era. Though
the recent ML-based anomaly detection models have better detecting ability, the vast volume of data and its multi-dimensionality
limit their ability with less accuracy, a low detection rate, and high learning complexity. This paper aims to enhance the performance
of anomaly detection by combining various optimized ensemble learning algorithms, such as random forest (RF), extreme gradient
boosting (XG Boost), adaptive boosting (Ada Boost), and light gradient boosting machine (LGBM), with a new hybrid feature
selection approach. An evolved version of particle swarm optimization (IPSO) is initially developed, which integrates the elimination
and opposition-based learning approaches to enhance PSO and then hybridizes it with the Chi-square method (Chi-IPSO). The
developed model is evaluated using two standard datasets: UNSW NB 15, and CICIDS 2017. The research results show that the RF
algorithm with Chi-IPSO performs better with an accuracy of 94.58% for the UNSW NB 15, and 99.70% for the CICIDS 2017.
Several assessment measures, including F-score, MCC value, accuracy, precision, and recall, are used to highlight the outcome
analysis of the suggested model. The results clearly show that the created model performs better than other modern approaches.
