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How Much Privacy Do We Have Today? A Study Of The Life Of Marc Mezvinsky, Miguel Mares, Salomon Gilles, Brian D. Gobran, Dan Engels
How Much Privacy Do We Have Today? A Study Of The Life Of Marc Mezvinsky, Miguel Mares, Salomon Gilles, Brian D. Gobran, Dan Engels
SMU Data Science Review
In this paper, we present a case study evaluating the level of information available about an individual through public, Internet-accessible sources. Privacy is a basic tenet of democratic society, but technological advances have made access to information and the identification of individuals much easier through Internet-accessible databases and information stores. To determine the potential level of privacy available to an individual in today’s interconnected world, we sought to develop a detailed history of Marc Mezvinsky, a semi-public figure, husband of Chelsea Clinton, and son of two former members of the United States House of Representatives. By utilizing only publicly and …
Comparative Study Of Deep Learning Models For Network Intrusion Detection, Brian Lee, Sandhya Amaresh, Clifford Green, Daniel Engels
Comparative Study Of Deep Learning Models For Network Intrusion Detection, Brian Lee, Sandhya Amaresh, Clifford Green, Daniel Engels
SMU Data Science Review
In this paper, we present a comparative evaluation of deep learning approaches to network intrusion detection. A Network Intrusion Detection System (NIDS) is a critical component of every Internet connected system due to likely attacks from both external and internal sources. A NIDS is used to detect network born attacks such as Denial of Service (DoS) attacks, malware replication, and intruders that are operating within the system. Multiple deep learning approaches have been proposed for intrusion detection systems. We evaluate three models, a vanilla deep neural net (DNN), self-taught learning (STL) approach, and Recurrent Neural Network (RNN) based Long Short …