Author:
Balogun Abdullateef O.,Adewole Kayode S.,Bajeh Amos O.,Jimoh Rasheed G.
Reference44 articles.
1. Mohammad, R.M., Thabtah, F., McCluskey, L.: Predicting phishing websites based on self-structuring neural network. Neural Comput. Appl. 25(2), 443–458 (2013). https://doi.org/10.1007/s00521-013-1490-z
2. Vrbančič, G., Fister, I., Jr., Podgorelec, V.: Swarm intelligence approaches for parameter setting of deep learning neural network: case study on phishing websites classification. In: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, pp. 1–8 (2018)
3. Adeyemo, V.E., Azween, A., JhanJhi, N., Mahadevan, S., Balogun, A.O.: Ensemble and deep-learning methods for two-class and multi-attack anomaly intrusion detection: an empirical study. Int. J. Adv. Comput. Sci. Appl. 10, 520–528 (2019)
4. Ali, W., Ahmed, A.A.: Hybrid intelligent phishing website prediction using deep neural networks with genetic algorithm-based feature selection and weighting. IET Inf. Secur. 13, 659–669 (2019)
5. Verma, R., Das, A.: What’s in a URL: fast feature extraction and malicious url detection. In: Proceedings of the 3rd ACM on International Workshop on Security and Privacy Analytics, pp. 55–63 (2017)
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献