Abstract
The rapid advancements in technology come with complex security challenges. One such challenge is phishing attack. Often a fake website is deployed to trick users into believing the website is legitimate and is safe to give away sensitive information such as their passwords. Anti-phishing frameworks have been developed in various forms. The most recent implementation involves datasets used to train machines in detecting phishing sites. This chapter focuses on implementing a Deep Feedforward Artificial Neural Network using supervised learning to detect phishing URLs. Several models were created that used a single feature to train. We compared how effective each feature was in detecting phishing URLs. Groups of features were also used to train models. Most models using only one feature yielded low accuracies, while models using more features showed better accuracies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
G. Xiang, J. Hong, C.P. Rose, L. Cranor, Cantina. ACM Trans. Inf. Syst. Secur. 14(2), 1–28 (2011). Web
D. Sahoo et al, Malicious URL detection using machine learning: A Survey. [1701.07179], 16 March 2017, arxiv.org/pdf/1701.07179.pdf
KnowBe4, History of phishing. Phishing, www.phishing.org/history-of-phishing
DMBisson, David BissonFollow, 6 common phishing attacks and how to protect against them. The State of Security, 3 June 2016, www.tripwire.com/state-of-security/security-awareness/6-common-phishing-attacks-and-how-to-protect-against-them/
J. Chen, C. Guo, Online detection and prevention of phishing attacks, in 2006 First International Conference on Communications and Networking in China, (2006) n. pag. Web
C.J. Chandan et al., A Machine learning approach for detection of phished websites using neural networks. Https://Pdfs.semanticscholar.org. Int. J. Recent Innov Trends Comput Commun. 2014. pdfs.semanticscholar.org/7e3f/4613751db651f3cbf43836fa783b843318bd.pdf
N.G.M. Jameel, E.G. Loay, Detection of phishing emails using feedforward neural network. Https://Pdfs.semanticscholar.org. Int. J.Comput Appl. (0975–8887) 77(7) (2013), pdfs.semanticscholar.org/33fa/22cc24349b4a27872f269baf424badd41db1.pdf
N. Abdelhamid et al., Phishing detection: A recent intelligent machine learning comparison based on models content and features. IEEE Xplore, 2017, ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8004877. 978-1-5090-6727-5/17/ ©2017 IEEE
D.P. Kingma, J.L. Ba, Adam: A method for stochastic optimization. Https://Arxiv.org, ICLR 2015, 23 July 2015, arxiv.org/abs/1412.6980
X. Glorot, Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, in Proceeding.MLR.Press, 13th International Conference on Artificial Intelligence and Statistics, 2010, proceedings.mlr.press/v9/glorot10a/glorot10a.pdf. Chia La-guna Resort, Sardinia, Italy. Volume 9 of JMLR: W&CP 9
N. Zhang, Y. Yuan, Phishing detection using neural network – cs229.Stanford.edu, cs229.stanford.edu/proj2012/ZhangYuan-PhishingDetectionUsingNeuralNetwork.pdf
A. Le et al., PhishDef: URL names say it all. ArXiv.org, 12 September 2010, arxiv.org/pdf/1009.2275.pdf. arXiv:1009.2275v1
H. Liu et al., Learning based malicious web sites detection using suspicious URLs. Https://Pdfs.sematicscholar.org, pdfs.semanticscholar.org/bf5e/84bc5572c4dc535cc01da87a49d79ba5bf46.pdf
W. Chu et al., protect sensitive sites from phishing attacks using features extractable from inaccessible phishing URLs, in Protect Sensitive Sites from Phishing Attacks Using Features Extractable from Inaccessible Phishing URLs – IEEE Conference Publication, pdfs.semanticscholar.org/dad5/a7b4d1eb0e51805090e3289955de38275991.pdf
M. Darling, A lexical approach for classifying malicious URLs. Digitalrepository.unm.edu, Engineering ETDs at UNM digital repository, 2015, digitalrepository.unm.edu/cgi/viewcontent.cgi?article=1062&context=ece_etds
R.M. Mohammad et al., Intelligent rule-based phishing websites classification. Https://Pdfs.sematicscholar.org,, pdfs.semanticscholar.org/17fe/6bb5a2248c1524dda71bdbfbc8346c479224.pdf
Fancyarora, Fancyarora/URL-feature-extraction. GitHub, github.com/fancyarora/URL-Feature-Extraction
R. Mohammad et al., Phishing websites features, pp. 1–7
PhishTank > Developer Information, PhishTank – out of the net, into the tank, www.phishtank.com/developer_info.php
Rohk, One week of global news feeds | Kaggle, 29 September 2017, www.kaggle.com/therohk/global-news-week/data
A. Kumar, Phishing website dataset. Kaggle, 12 January 2018, www.kaggle.com/akashkr/phishing-website-dataset/data
S. Xu, L. Chen, A novel approach for determining the optimal number of hidden layer neurons for FNN’s and its application in data mining, in Semantic Scholar, 5th International Conference on Information Technology and Applications (ICITA2008), (2008)., pdfs.semanticscholar.org/254f/3f0fce2905675445d48ca8ab61e3761b1e9b.pdf
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Cerda, B.M., Yuan, S., Chen, L. (2021). Phishing Detection using Deep Learning. In: Daimi, K., Arabnia, H.R., Deligiannidis, L., Hwang, MS., Tinetti, F.G. (eds) Advances in Security, Networks, and Internet of Things. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-71017-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-71017-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-71016-3
Online ISBN: 978-3-030-71017-0
eBook Packages: EngineeringEngineering (R0)