Joint Hacking and Latent Hazard Rate Estimation
  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. 
Ziqi Liu and Alexander J. Smola and Kyle Soska and Yu-Xiang Wang and   Qinghua Zheng
stat.AP