{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/attributing-hacks","title":"Attributing Hacks","arxiv_id":"1611.03021","date":"2016-11-07","proceeding":null,"authors":["Ziqi Liu","Alexander J. Smola","Kyle Soska","Yu-Xiang Wang","Qinghua Zheng","Jun Zhou"],"abstract":"In this paper we describe an algorithm for estimating the provenance of hacks\non websites. That is, given properties of sites and the temporal occurrence of\nattacks, we are able to attribute individual attacks to joint causes and\nvulnerabilities, as well as estimating the evolution of these vulnerabilities\nover time. Specifically, we use hazard regression with a time-varying additive\nhazard function parameterized in a generalized linear form. The activation\ncoefficients on each feature are continuous-time functions over time. We\nformulate the problem of learning these functions as a constrained variational\nmaximum likelihood estimation problem with total variation penalty and show\nthat the optimal solution is a 0th order spline (a piecewise constant function)\nwith a finite number of known knots. This allows the inference problem to be\nsolved efficiently and at scale by solving a finite dimensional optimization\nproblem. Extensive experiments on real data sets show that our method\nsignificantly outperforms Cox's proportional hazard model. We also conduct a\ncase study and verify that the fitted functions are indeed recovering\nvulnerable features and real-life events such as the release of code to exploit\nthese features in hacker blogs.","url_abs":"http://arxiv.org/abs/1611.03021v2","url_pdf":"http://arxiv.org/pdf/1611.03021v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"attributing-hacks","repo_url":"https://github.com/ziqilau/Experimental-HazardRegression","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}