Issue
Security and Safety
Volume 4, 2025
Security and Safety in Artificial Intelligence
Article Number 2024025
Number of page(s) 15
Section Other Fields
DOI https://doi.org/10.1051/sands/2024025
Published online 22 July 2025
  1. Liu Z, Wang Y and Feng F et al. A DDoS detection method based on feature engineering and machine learning in software-defined networks. Sensors 2023; 23: 6176. [Google Scholar]
  2. Saied M, Guirguis S and Madbouly M. Review of artificial intelligence for enhancing intrusion detection in the internet of things. Eng Appl Artif Intell 2024; 127: 107231. [Google Scholar]
  3. Jiang H, He Z and Zhang H. Network intrusion detection based on PSO-XG-boost model. IEEE Access, 2020; 8: 58392–401. [Google Scholar]
  4. Aziz NAM, Yunus R and Abd Hamid H et al. An acceleration of microwave-assisted transesterification of palm oil-based methyl ester into trimethylolpropane ester. Sci Rep 2020; 10: 19652. [Google Scholar]
  5. Su P, Liu Y and Song X. Research on intrusion detection method based on improved smote and XG-boost. In: Proceedings of the 8th International Conference on Communication and Network Security, 2018. [Google Scholar]
  6. Abdulkareem-Alsultan G, Asikin-Mijan HV and Lee YH et al. Biofuels: past, present, future. Innovations Sustainable Energy Cleaner Environ 2020; 489–504. [Google Scholar]
  7. Kamil FH, Ali S and Shahruzzaman RMHR et al. Characterization and application of aluminum dross as catalyst in pyrolysis of waste cooking oil. Bull Chem React Eng Catal 2017; 12: 81–8. [Google Scholar]
  8. Abdulkareem-Alsultan G, Asikin-Mijan N and Obeas LK et al. In-situ operando and ex-situ study on light hydrocarbon-like-diesel and catalyst deactivation kinetic and mechanism study during deoxygenation of sludge oil. Chem Eng J 2022; 429: 132206. [Google Scholar]
  9. Venkatesan S, Design an intrusion detection system based on feature selection using ML algorithms. Math Stat Eng Appl 2023; 72: 702–10. [Google Scholar]
  10. Zivkovic M, Tair M and Venkatachalam K et al. Novel hybrid firefly algorithm: An application to enhance XG-boost tuning for intrusion detection classification. PeerJ Comput Sci 2022; 8: e956. [Google Scholar]
  11. Fuhnwi GS, Revelle M and Izurieta C. Improving network intrusion detection performance: an empirical evaluation using extreme gradient boosting (XG-boost) with recursive feature elimination. In: 2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC). IEEE, 2024. [Google Scholar]
  12. Leevy J, Machine Learning Algorithms for Predicting Botnet Attacks in IoT Networks. 2022, Florida Atlantic University. [Google Scholar]
  13. Bhattacharya S, Siva RS and Praveen KRM et al. A novel PCA-firefly based XG-boost classification model for intrusion detection in networks using GPU. Electronics 2020; 9: 219. [Google Scholar]
  14. Cheng P, Xu K and Li S et al. TCAN-IDS: intrusion detection system for internet of vehicle using temporal convolutional attention network. Symmetry 2022; 14: 310. [Google Scholar]
  15. Gupta N, Jindal V and Bedi P. CSE-IDS: Using cost-sensitive deep learning and ensemble algorithms to handle class imbalance in network-based intrusion detection systems. Comput Secur 2022; 112: 102499. [Google Scholar]
  16. Tama BA and Lim S. Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation. Comput Sci Rev 2021; 39: 100357. [Google Scholar]
  17. Mhawi DN, Aldallal A and Hassan S. Advanced feature-selection-based hybrid ensemble learning algorithms for network intrusion detection systems. Symmetry. 2022; 14: 1461. [Google Scholar]
  18. Chen T and Guestrin C. XG-boost: A scalable tree boosting system. In: Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining. 2016. [Google Scholar]
  19. Basheri M and Ragab M. Quantum cat swarm optimization based clustering with intrusion detection technique for future internet of things environment. Comput Syst Sci Eng 2023; 46: 3783. [Google Scholar]
  20. Ke G, Meng Q and Finley T et al. Lightgbm: A highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst 2017; 30: 3146. [Google Scholar]
  21. AlHosni N, Jovanovic L and Antonijevic M et al. The XG-boost model for network intrusion detection boosted by enhanced sine cosine algorithm. In: International Conference on Image Processing and Capsule Networks. Springer, 2022. [Google Scholar]
  22. Dhaliwal SS, Nahid A-A and Abbas R. Effective intrusion detection system using XG-boost. Information 2018; 9: 149. [CrossRef] [Google Scholar]
  23. Wang J and Zhou S. CS-GA-XG-boost-based model for a radio-frequency power amplifier under different temperatures. Micromachines 2023; 14: 1673. [Google Scholar]
  24. Ihsan RR, Almufti SM and Ormani BMS et al. A survey on Cat Swarm Optimization algorithm. Asian J Res Comput Sci 2021; 10: 22–32. [Google Scholar]
  25. Seyyedabbasi A and Kiani F. Sand Cat swarm optimization: A nature-inspired algorithm to solve global optimization problems. Eng Comput 2023; 39: 2627–51. [Google Scholar]
  26. Chu SC, Tsai PW and Pan JS. Cat swarm optimization. In: PRICAI 2006: Trends in Artificial Intelligence: 9th Pacific Rim International Conference on Artificial Intelligence Guilin, China, August 7-11, 2006 Proceedings 9. Springer, 2006. [Google Scholar]
  27. Abdulraheem SA, Aliyu S and Abdullahi FB. Hyper-parameter tuning for support vector machine using an improved cat swarm optimization algorithm. J Nigerian Soc Phys Sci 2023; 5: 1007. [Google Scholar]
  28. Neto ECP, Dadkhah S and Ferreira R et al. CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment. Sensors 2023; 23: 5941. [Google Scholar]
  29. Ahmed AM, Rashid TA and Saeed SAM. Cat swarm optimization algorithm: a survey and performance evaluation. Comput Intell Neurosci 2020; 2020: 4854895. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.