Zivkovic Miodrag, Tair Milan, K Venkatachalam, Bacanin Nebojsa, Hubálovský Štěpán, Trojovský Pavel
Singidunum University, Belgrade, Serbia.
Department of Applied Cybernetics, Faculty of Science, University of Hradec Králové, Hradec Kralove, Hradec Kralove, Czech Republic.
PeerJ Comput Sci. 2022 Apr 29;8:e956. doi: 10.7717/peerj-cs.956. eCollection 2022.
The research proposed in this article presents a novel improved version of the widely adopted firefly algorithm and its application for tuning and optimising XGBoost classifier hyper-parameters for network intrusion detection. One of the greatest issues in the domain of network intrusion detection systems are relatively high false positives and false negatives rates. In the proposed study, by using XGBoost classifier optimised with improved firefly algorithm, this challenge is addressed. Based on the established practice from the modern literature, the proposed improved firefly algorithm was first validated on 28 well-known CEC2013 benchmark instances a comparative analysis with the original firefly algorithm and other state-of-the-art metaheuristics was conducted. Afterwards, the devised method was adopted and tested for XGBoost hyper-parameters optimisation and the tuned classifier was tested on the widely used benchmarking NSL-KDD dataset and more recent USNW-NB15 dataset for network intrusion detection. Obtained experimental results prove that the proposed metaheuristics has significant potential in tackling machine learning hyper-parameters optimisation challenge and that it can be used for improving classification accuracy and average precision of network intrusion detection systems.
本文提出的研究展示了广泛采用的萤火虫算法的一种新颖改进版本，及其在调整和优化用于网络入侵检测的XGBoost分类器超参数方面的应用。网络入侵检测系统领域最大的问题之一是相对较高的误报率和漏报率。在本研究中，通过使用经改进的萤火虫算法优化的XGBoost分类器，解决了这一挑战。基于现代文献中的既定做法，首先在28个著名的CEC2013基准实例上对提出的改进萤火虫算法进行验证，并与原始萤火虫算法和其他先进的元启发式算法进行比较分析。之后，采用所设计的方法对XGBoost超参数进行优化，并在广泛使用的基准NSL-KDD数据集和更新的USNW-NB15网络入侵检测数据集上对调优后的分类器进行测试。获得的实验结果证明，所提出的元启发式算法在应对机器学习超参数优化挑战方面具有巨大潜力，并且可用于提高网络入侵检测系统的分类准确率和平均精度。
