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ACMSE 2013
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  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 description, the...

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. The data use...

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 spoofed command...

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 technology moving s...

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 began with two-l...

Aug 8 2013
 
 
   
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