HTMLPhish: Enabling Phishing Web Page Detection by Applying Deep Learning Techniques on HTML Analysis

E. Borgida and N. Brekke, “The base rate fallacy in attribution and prediction,” New directions in attribution research , vol. 3, pp. 63–95, 1981

1981

Earlier work this paper cites.

W. Chu, B. B. Zhu, F. Xue, X. Guan, and Z. Cai, “Protect sensitive sites from phishing attacks using features extractable from inaccessible phishing urls,” in 2013 IEEE International Conference on Communications (ICC) . IEEE, 2013, pp. 1990–1994

1994

Earlier work this paper cites.

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997

1997

Earlier work this paper cites.

Y. Zhang, J. I. Hong, and L. F. Cranor, “Cantina: a content-based approach to detecting phishing web sites,” in Proceedings of the 16th international conference on World Wide Web . ACM, 2007, pp. 639–648

2007

Earlier work this paper cites.

C. Whittaker, B. Ryner, and M. Nazif, “Large-scale automatic classification of phishing pages.” in NDSS , vol. 10, 2010, p. 2010

2010

Earlier work this paper cites.

T. Acar, M. Belenkiy, and A. Küpçü, “Single password authentication,” Computer Networks , vol. 57, no. 13, pp. 2597–2614, 2013

2013

Earlier work this paper cites.

Y. Kim, “Convolutional neural networks for sentence classification,” arXiv preprint arXiv:1408.5882 , 2014

Original

2014

Earlier work this paper cites.

K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078 , 2014

Original

2014

Earlier work this paper cites.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature , vol. 521, no. 7553, p. 436, 2015

2015

Earlier work this paper cites.

D. Kingma and J. Ba, “Adam: a method for stochastic optimization (2014),” arXiv preprint arXiv:1412.6980 , vol. 15, 2015

Original

2015

Earlier work this paper cites.

G. Varshney, M. Misra, and P. K. Atrey, “A phish detector using lightweight search features,” Computers & Security , vol. 62, pp. 213–228, 2016

2016

Cited alongside, same era.

I. Goodfellow, Y. Bengio, and A. Courville, Deep learning . MIT press, 2016

2016

Cited alongside, same era.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778

2016

Cited alongside, same era.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning . MIT Press, 2016, http://www.deeplearningbook.org

2016

Cited alongside, same era.

C. Amrutkar, Y. S. Kim, and P. Traynor, “Detecting mobile malicious webpages in real time,” IEEE Transactions on Mobile Computing , no. 8, pp. 2184–2197, 2017

2017

Cited alongside, same era.

J. Lopez and J. E. Rubio, “Access control for cyber-physical systems interconnected to the cloud,” Computer Networks , vol. 134, pp. 46–54, 2018

2018

Later among the works it cites.

APWG, “Phishing activity trends report, 1st quarter 2018,” Tech. Rep., 2018

2018

Later among the works it cites.

D. Chattaraj, M. Sarma, and A. K. Das, “A new two-server authentication and key agreement protocol for accessing secure cloud services,” Computer Networks , vol. 131, pp. 144–164, 2018

2018

Later among the works it cites.

C. N. Gutierrez, T. Kim, R. Della Corte, J. Avery, D. Goldwasser, M. Cinque, and S. Bagchi, “Learning from the ones that got away: Detecting new forms of phishing attacks,” IEEE Transactions on Dependable and Secure Computing , vol. 15, no. 6, pp. 988–1001, 2018

2018

Later among the works it cites.