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Malicious web content is a serious problem on the Internet today.
B. Feinstein, D. Peck, and I. SecureWorks, “Caffeine monkey: Automated collection, detection and analysis of malicious javascript,” Black Hat USA
2007
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C. Seifert, I. Welch, and P. Komisarczuk, “Identification of malicious web pages with static heuristics,” in Telecommunication Networks and Applications Conference, 2008. ATNAC 2008. Australasian
2008
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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 international conference on Knowledge discovery and data mining
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H. Choi, B. B. Zhu, and H. Lee, “Detecting malicious web links and identifying their attack types.,” WebApps
2011
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D. Canali, M. Cova, G. Vigna, and C. Kruegel, “Prophiler: a fast filter for the large-scale detection of malicious web pages,” in Proceedings of the 20th international conference on World wide web
2011
Cited alongside, same era.
K. P. Kumar, N. Jaisankar, and N. Mythili, “An efficient technique for detection of suspicious malicious web site,” Journal of Advances in Information Technology
2011
Cited alongside, same era.
C. N. Dos Santos and M. Gatti, “Deep convolutional neural networks for sentiment analysis of short texts.,” in COLING
2014
Cited alongside, same era.
2014
Cited alongside, same era.
X. Zhang, J. Zhao, and Y. LeCun, “Character-level convolutional networks for text classification,” in Advances in neural information processing systems
2015
Later among the works it cites.
J. Saxe and K. Berlin, “Deep neural network based malware detection using two dimensional binary program features,” in Malicious and Unwanted Software (MALWARE), 2015 10th International Conference on
2015
Later among the works it cites.
J. L. Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,” arXiv preprint arXiv:1607.06450
2016
Later among the works it cites.
2017
Later among the works it cites.
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