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      Automated U.S Diplomatic Cables Security Classification: Topic Model Pruning vs. Classification Based on Clusters

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          Abstract

          The U.S Government has been the target for cyber-attacks from all over the world. Just recently, former President Obama accused the Russian government of the leaking emails to Wikileaks and declared that the U.S. might be forced to respond. While Russia denied involvement, it is clear that the U.S. has to take some defensive measures to protect its data infrastructure. Insider threats have been the cause of other sensitive information leaks too, including the infamous Edward Snowden incident. Most of the recent leaks were in the form of text. Due to the nature of text data, security classifications are assigned manually. In an adversarial environment, insiders can leak texts through E-mail, printers, or any untrusted channels. The optimal defense is to automatically detect the unstructured text security class and enforce the appropriate protection mechanism without degrading services or daily tasks. Unfortunately, existing Data Leak Prevention (DLP) systems are not well suited for detecting unstructured texts. In this paper, we compare two recent approaches in the literature for text security classification, evaluating them on actual sensitive text data from the WikiLeaks dataset.

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          A Survey of Stealth Malware Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions

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            Data Leak Prevention through Named Entity Recognition

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              Office 365 Compliance and Data Loss Prevention

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                Author and article information

                Journal
                2017-03-07
                Article
                1703.02248

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                Pre-print of camera-ready copy accepted to the 2017 IEEE Homeland Security Technologies (HST) conference
                cs.CR

                Security & Cryptology

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