Rogue certi\ufb01cates are valid certi\ufb01cates issued by a legitimate certi\ufb01cate authority (CA) which are nonetheless untrustworthy; yet trusted by web browsers and users. With the current public key infrastructure (PKI) there exists a window of vulnerability between the time a rogue certi\ufb01cate is issued and when it is detected. Rogue certi\ufb01cates from recent compromises have been trusted for as long as weeks before detection and revocation. Previous proposals to close this window of vulnerability require changes in the infrastructure, Internet protocols, or end user experience. We present a method for detecting rogue certi\ufb01cates from trusted CAs developed from a large and timely collection of certi\ufb01cates. This method automates classi\ufb01cation by building machine learning models with Deep Neural Networks (DNN). Despite the scarcity of rogue instances in the dataset DNN produced a classi\ufb01cation method that is proven both in simulation and in the July 2014 compromise of the India CCA. We report the details of the classi\ufb01cation method and illustrate that it is repeatable, such as with datasets obtained from crawling. We describe the classi\ufb01cation performance under our current research deployment.<\/p>\n","protected":false},"excerpt":{"rendered":"
Rogue certi\ufb01cates are valid certi\ufb01cates issued by a legitimate certi\ufb01cate authority (CA) which are nonetheless untrustworthy; yet trusted by web browsers and users. With the current public key infrastructure (PKI) there exists a window of vulnerability between the time a rogue certi\ufb01cate is issued and when it is detected. 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