Unsupervised Hilbert Huang Transform with Pruned Exact Linear Time Algorithm for Anomaly Detection in Web Data
Resumo
With the massive use of digital technologies, many activities in society have transitioned to the web, from shopping and social interactions to business, industry, and, unfortunately, a crime. Recent reports reveal that criminals targeted more companies in 2020 than in 2019.[...]
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Referências
CSIC. CSIC Dataset 2010. https://www.isi.csic.es/dataset/. Acceso: 17/10/2016. 2010.
CSIC. Torpeda CSIC Dataset 2012. https://www.tic.itefi.csic.es/torpeda/datasets.html. Acceso: 17/10/2016. 2012.
Ralf Funk, Nico Epp, et al. “Anomaly-based web application firewall using http-specific features and one-class svm”. In: Revista Eletrônica Argentina-Brasil de Tecnologias da Informação e da Comunicação 2.1 (2018).
N. E Huang et al. “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis”. In: Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 454.1971 (1998), pp. 903– 995.
Rebecca Killick, Paul Fearnhead, and I.A. Eckley. “Optimal Detection of Changepoints With a Linear Computational Cost”. In: Journal of the American Statistical Association 107 (Dec. 2012), pp. 1590–1598. doi: 10.1080/01621459.2012.737745.
A. Kozakevicius et al. “URL query string anomaly sensor designed with the bidimensional Haar wavelet transform”. In: International Journal of Information Security 14.6 (2015), pp. 561–581.
K. Limthong, P. Watanapongse, and F. Kensuke. “A wavelet-based anomaly detection for outbound network traffic”. In: 8th Asia-Pacific Symposium on Information and Telecommunication Technologies. 2010, pp. 1–6.
M. Thottan and Chuanyi Ji. “Anomaly detection in IP networks”. In: IEEE Transactions on Signal Processing 51.8 (2003), pp. 2191–2204. doi: 10.1109/TSP.2003.814797.
