ArticlePeerJ. Computer science2022
Novel hybrid firefly algorithm: an application to enhance XGBoost tuning for intrusion detection classification.
Article in PeerJ. Computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM.Scientific reports · 2025Article
- An optimized LSTM-based deep learning model for anomaly network intrusion detection.Scientific reports · 2025Article
- Optimizing VGG16 deep learning model with enhanced hunger games search for logo classification.Scientific reports · 2024Article
- Sentiment classification for insider threat identification using metaheuristic optimized machine learning classifiers.Scientific reports · 2024Article
- Intrusion detection using metaheuristic optimization within IoT/IIoT systems and software of autonomous vehicles.Scientific reports · 2024Article
- Yield prediction for crops by gradient-based algorithms.PloS one · 2024Article
- Decomposition aided attention-based recurrent neural networks for multistep ahead time-series forecasting of renewable power generation.PeerJ. Computer science · 2024Article
- Data-centric automated approach to predict autism spectrum disorder based on selective features and explainable artificial intelligence.Frontiers in computational neuroscience · 2024Article
- Research on adverse event classification algorithm of da Vinci surgical robot based on Bert-BiLSTM model.Frontiers in computational neuroscience · 2024Article
- An improved U-net and attention mechanism-based model for sugar beet and weed segmentation.Frontiers in plant science · 2024Article
- A Graph-Based Technique for Securing the Distributed Cyber-Physical System Infrastructure.Sensors (Basel, Switzerland) · 2023Article
- Article
- Addressing Internet of Things security by enhanced sine cosine metaheuristics tuned hybrid machine learning model and results interpretation based on SHAP approach.PeerJ. Computer science · 2023Article
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6 authors.
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Abstract
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.
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