Open Access
| Issue |
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
|
|
|---|---|---|
| Article Number | 00162 | |
| Number of page(s) | 7 | |
| DOI | https://doi.org/10.1051/bioconf/20249700162 | |
| Published online | 05 April 2024 | |
- M. A. Al-Garadi, A. Mohamed, A. K. Al-Ali, X. Du, I. Ali, and M. Guizani, “A survey of machine and deep learning methods for internet of things (IoT) security,” IEEE Commun. Surv. Tutorials, vol. 22, no. 3, pp. 1646–1685, 2020. [CrossRef] [Google Scholar]
- I. H. Sarker, M. H. Furhad, and R. Nowrozy, “Ai-driven cybersecurity: an overview, security intelligence modeling and research directions,” SN Comput. Sci., vol. 2, pp. 1–18, 2021. [CrossRef] [Google Scholar]
- M. M. Hassan, A. Gumaei, A. Alsanad, M. Alrubaian, and G. Fortino, “A hybrid deep learning model for efficient intrusion detection in big data environment,” Inf. Sci. (Ny)., vol. 513, pp. 386–396, 2020. [CrossRef] [Google Scholar]
- S. Mohammadi, H. Mirvaziri, M. Ghazizadeh-Ahsaee, and H. Karimipour, “Cyber intrusion detection by combined feature selection algorithm,” J. Inf. Secur. Appl., vol. 44, pp. 80–88, 2019. [Google Scholar]
- N. Moustafa, “A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets,” Sustain. Cities Soc., vol. 72, p. 102994, 2021. [CrossRef] [Google Scholar]
- C. Wu, A. Qian, X. Dong, and Y. Zhang, “Feature-oriented design of visual analytics system for interpretable deep learning based intrusion detection,” in 2020 International Symposium on Theoretical Aspects of Software Engineering (TASE), 2020, pp. 73–80. [CrossRef] [Google Scholar]
- S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, and S. M. Mirjalili, “Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems,” Adv. Eng. Softw., vol. 114, pp. 163–191, 2017. [CrossRef] [Google Scholar]
- J. Zhang and J.-S. Wang, “Improved salp swarm algorithm based on levy flight and sine cosine operator,” Ieee Access, vol. 8, pp. 99740–99771, 2020. [CrossRef] [Google Scholar]
- L. Abualigah, M. Shehab, M. Alshinwan, and H. Alabool, “Salp swarm algorithm: a comprehensive survey,” Neural Comput. Appl., vol. 32, pp. 11195–11215, 2020. [CrossRef] [Google Scholar]
- M. T. Ribeiro, S. Singh, and C. Guestrin, “‘Why should i trust you?’ Explaining the predictions of any classifier,” in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 2016, pp. 1135–1144. [CrossRef] [Google Scholar]
- S. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” May 2017, (accessed on 19 March 2024). [Online]. Available: http://arxiv.org/abs/1705.07874 [Google Scholar]
- D. B. Gillies, Some theorems on n-person games. Princeton University, 1953. [Google Scholar]
- A. Joseph, “Shapley regressions: A framework for statistical inference on machine learning models,” 2019. [Google Scholar]
- T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction, vol. 2. Springer, 2009. [Google Scholar]
- H. Hindy, E. Bayne, M. Bures, R. Atkinson, C. Tachtatzis, and X. Bellekens, “Machine learning based IoT intrusion detection system: An MQTT case study (MQTT-IoT-IDS2020 dataset),” in International networking conference, 2020, pp. 73–84. [Google Scholar]
- H. Hindy, C. Tachtatzis, R. Atkinson, E. Bayne, and X. Bellekens, “Mqtt internet of things intrusion detection dataset.” Jun, 2020. [Google Scholar]
- M. Buckland and F. Gey, “The relationship between recall and precision,” J. Am. Soc. Inf. Sci., vol. 45, no. 1, pp. 12–19, 1994. [CrossRef] [Google Scholar]
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