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Type I and Type II Security Engineering Errors

By Roy D. Follendore iII
Copyright (c) 2002 RDFollendoreIII
 
Engineers who attempt to justify security through statistics are prone to the errors induced by formulating those statistics.  Because of its rational origins, no statistical conclusion can exist as an entirely objective result of logic. Security involves the evaluation of questions of complexity associated within hypothetical systems. Beliefs, attitudes, interactions and intentions play a role in concept of security analysis.  It is impossible to carelessly substitute an extensional phrasing of one policy question equivalently for another. If a useful statistical result is expected probable, then it can be easily appreciated that within such a hypothesis it is possible to make an error.  The test itself can too easily become a self fulfilling prophecy.  As in all statistical hypotheses, an assumption is made, an experiment is designed and executed to test it, and some calculations are performed to determine if the results are probable given the assumptions.  If not, then the assumption is thrown out and an alternative assumption is created.  To quote John Allen Paulos, "Proving a statistic is more a matter of disconfirming proposition rather than confirming them.'  
 
Two types of errors exist within procedure of attempting to find a statistical proof.  A Type I procedure exists when a true hypothesis is rejected.  A Type II procedure exists when a false hypothesis is accepted.  The distinction between these two types of security errors is important. A high level example can be found in the issue of choosing the right algorithm.  One Government security expert tries to avoid Type I policy errors where the good guys don't get to receive high quality cryptographic results.  Another Government security expert tries to avoid Type II policy errors where the potential criminals get to use a high quality cryptographic results. A potential statistical compromise may not exist because it is possible that unacceptable rates of both a Type I and Type II error can simultaneously coexist.
 
Other examples can be found within the process cryptographic design.  In general, the more cryptographic processing that can be performed, the stronger the result with fewer errors, the longer it takes and the slower the processing. One Engineer tries to avoid a Type I design errors where very high speed processes don't get the necessary throughput needed to be passed through a cryptographic system.  Another Engineer attempts to avoid a Type II design error where the throughput of a very high rates of cryptographic processing may fail to produce adequate level of protection.  The lessons learned from understanding this is that the statistical specification of a cryptographic design can not exist independently of its rational application. 
 
Should a manager wait to design a system that is acceptable to a security hypothesis or simply accept existing problems and solutions that are unacceptable?
 

Once a system exists, should managers fix systems that are secure or wait until the systems become insecure?  These are the problems underlying the issues surrounding the security engineering process.     

 
 

 

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Copyright (c) 2001-2007 RDFollendoreIII All Rights Reserved