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Stop Fixing All Security Vulnerabilities, Say B-Sides Security Presenters 88

PMcGovern writes "At BSidesLV in Las Vegas, Ed Bellis and Data Scientist Michael Roytman gave a talk explaining how security vulnerability statistics should be done: 'Don't fix all security issues. Fix the security issues that matter, based on statistical relevance.' They looked at 23,000,000 live vulnerabilities across 1,000,000 real assets, which belonged to 9,500 clients, to explain their thesis."
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Stop Fixing All Security Vulnerabilities, Say B-Sides Security Presenters

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  • Re:erm, no? (Score:5, Interesting)

    by Anonymous Coward on Thursday August 08, 2013 @01:15PM (#44511485)

    The article is talking about fixing what you can. It simply outlines how to prioritize the issues in order to figure out what you can fix with limited resources.

    That's a pretty damn weak model. It doesn't take a genius to understand that if you use statistics to prioritize security issues to address (or more to your point, cull out ones you won't address due to limited resources), then it's only a matter of time before attackers start figuring out ways to use those statistical models against you, ultimately learning about the "can't-get-to-it" threat list and focusing attack vectors there.

    Not to mention management being "sold" on this model and cutting 20% of your IT support staff next year due to the "increased efficiencies of patch management". Have fun doing more work.

  • by nuckfuts ( 690967 ) on Thursday August 08, 2013 @01:32PM (#44511633)
    OpenBSD takes the approach of proactive code audits and of fixing all bugs found [openbsd.org], even those that have no apparent potential for exploitation. This has really paid off over the years. Often when vulnerabilities came to light, they were found to not affect OpenBSD because the underlying bug had already been fixed.
  • Re:erm, no? (Score:4, Interesting)

    by martas ( 1439879 ) on Thursday August 08, 2013 @03:06PM (#44512631)
    That's why you need a game-theoretic, adversarial model instead of a simple statistical model based on past observations. Regret minimization, multi-arm bandits, etc.

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