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	ACMSE 2013 ACM         Options: Date Reviewed Title Author Publisher Published Date Descending Order Ascending Order     1-5 of 5 reviews Date Reviewed     Network reconnaissance, attack, and defense laboratories for an introductory cyber-security course Greenlaw R., Phillips A., Schultz J., Stahl D., Standard S.  ACMSE 2013 (Proceedings of the 51st ACM Southeast Conference, Savannah, GA, Apr 4-6, 2013) 1-6, 2013.  Type: Proceedings Anyone setting up a hands-on cybersecurity training course should read this paper. Greenlaw and associates describe a clear set of learning objectives, and the steps they took. While there are several components missing from their desc... Nov 3 2014   Comparing NoSQL MongoDB to an SQL DB Parker Z., Poe S., Vrbsky S.  ACMSE 2013 (Proceedings of the 51st ACM Southeast Conference, Savannah, Georgia, Apr 4-6, 2013) 1-6, 2013.  Type: Proceedings Results are presented of a performance comparison between SQL Server and NoSQL MongoDB database systems. The comparison involved insert, three kinds of update, four kinds of simple select, and three kinds of complex select operations. ... Jul 3 2014   Observing industrial control system attacks launched via Metasploit framework Wallace N., Atkison T.  ACMSE 2013 (Proceedings of the 51st ACM Southeast Conference, Savannah, GA, Apr 4-6, 2013) 1-4, 2013.  Type: Proceedings Wallace and Atkison, in this paper, model several attacks against programmable logic controllers and observe packet timing during the attacks. During a denial of service attack, the researchers record observations of legitimate and spo... Nov 20 2013   MoReCon: a mobile RESTful context-aware middleware Dogdu E., Soyer O.  ACMSE 2013 (Proceedings of the 51st ACM Southeast Conference, Savannah, GA, Apr 4-6, 2013) 1-6, 2013.  Type: Proceedings Mobile RESTful context-aware middleware, or MoReCon, was created by the authors and is described in this paper. It’s essentially a new code library, one of those low-profile, glamor-free incremental innovations that keeps tec... Aug 15 2013   Using artificial neural networks to predict first-year traditional students second year retention rates Plagge M.  ACMSE 2013 (Proceedings of the 51st ACM Southeast Conference, Savannah, GA, Apr 4-6, 2013) 1-5, 2013.  Type: Proceedings This paper reports the results of a project using neural networks and data supplied by the IT department of Columbus State University in Georgia to predict which students will drop out after their first year of college. The author bega... Aug 8 2013
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