Open Access
| Issue |
E3S Web Conf.
Volume 297, 2021
The 4th International Conference of Computer Science and Renewable Energies (ICCSRE'2021)
|
|
|---|---|---|
| Article Number | 01032 | |
| Number of page(s) | 10 | |
| DOI | https://doi.org/10.1051/e3sconf/202129701032 | |
| Published online | 22 September 2021 | |
- Andronicus A. Akinyelu and Aderemi O. Adewumi. Classification of Phishing Email using Random forest Machine Learning Technique 2014. [Google Scholar]
- Gilchan Park, Julia M. Taylor, Using Syntactic Features for Phishing Detection 2015, https://arxiv.org/ftp/arxiv/papers/1506/1506.00037.pdf [Google Scholar]
- G. Tan, P. Zhang, Q. Liu, X. Liu, C. Zhu, and L. Guo, “Malfilter: A lightweight real-time malicious url filtering system in large-scale networks” 2018 IEEE ISPA/IUCC/BDCloud/SocialCom/SustainCom.IEEE, 2018, pp. 565–571. [Google Scholar]
- J. Ma, L. K. Saul, S. Savage, and G.M. Voelker, “Beyond blacklists : learning to detect malicious web sites from suspicious urls” in Proceedings of the 15th ACM SIGKDD in international conference on Knowledge discovery and data mining. ACM, 2009, pp. 1245–1254. [Google Scholar]
- R. Verma and A. Das, “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. ACM, 2017, pp. 55–63. [Google Scholar]
- O. K. Sahingoz, E. Buber, O. Demir, and B. Diri, “Machine learning based phishing detection from urls,” Expert Systems with Applications, vol. 117, pp. 345–357, 2019. [Google Scholar]
- Random Forest : https://www.javatpoint.com/machine-learning-random-forest-algorithm; https://williamkoehrsen.medium.com/random-forest-simple-explanation-377895a60d2d [Google Scholar]
- Logistic Regression : https://machinelearningmastery.com/logistic-regression-for-machine-learning/ [Google Scholar]
- Decision Tree : • https://www.geeksforgeeks.org/decision-tree/ • https://www.xoriant.com/blog/product-engineering/decision-trees-machine-learning-algorithm.html [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.