Joint Hacking and Latent Hazard Rate Estimation

Autor:	Liu, Ziqi, Smola, Alexander J., Soska, Kyle, Wang, Yu-Xiang, Zheng, Qinghua
Rok vydání:	2016
Předmět:	Statistics - Applications
Druh dokumentu:	Working Paper
Popis:	In this paper we describe an algorithm for predicting the websites at risk in a long range hacking activity, while jointly inferring the provenance and evolution of vulnerabilities on websites over continuous time. Specifically, we use hazard regression with a time-varying additive hazard function parameterized in a generalized linear form. The activation coefficients on each feature are continuous-time functions constrained with total variation penalty inspired by hacking campaigns. We show that the optimal solution is a 0th order spline with a finite number of adaptively chosen knots, and can be solved efficiently. Experiments on real data show that our method significantly outperforms classic methods while providing meaningful interpretability. Comment: Presented at NIPS 2016 Workshop on Interpretable Machine Learning in Complex Systems
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1611.06843 Zobrazit plný text záznamu View this record from Arxiv