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Recent studies at California State University, Northridge using the Kolbe A™ index have shown differences in conative profiles between computer science students near graduation and those just entering the program. Since conation, or a person's inherent talent or natural way of doing things, relates to how a student approaches learning, it may provide clues as to why some students quit the program prior to graduation. The Kolbe instrument used in this study is a tool for measuring conative talents. It has been widely used in industry to aid in management activities and assist in the formation of effective teams. Its use in education has been limited, but it has potential for providing insight regarding differences in student learning and in understanding issues related to student retention. For example, one recent study showed a significantly higher implementor conative instinct among entering students than existed among graduating students. This suggests that some students with strong implementor talents may have dropped out of the program, possibly because they were not given sufficient opportunity to engage in "hands on" activities. It may be that some students are discouraged from continuing in the major because they find the learning environment incompatible with the natural ways they approach learning.

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As public internetworks are increasingly used for secure communications, the need grows for end-to-end protection fi'om traffic analysis. The additional protection of Traffic Flow Confidentiality can be detrimental to performance when padding is used to mask traffic patterns. Traffic masking policies that are responsive to system service requirements can improve performance, but secure adaptive traffic masking has to balance performance requirements with system protection requirements. This paper addresses the information leaks that result J~om adaptations in security mechanisms. Introduction and BackgroundTraffic flow confidentiality (TFC) is concemed with hiding communication patterns that, if exploited, could reveal or compromise sensitive information. Sources of traffic flow information that need to be protected are frequency and length of transmittals, origin/destination traffic patterns, and protocol headers [6,7]. TFC is becoming more important as government agencies and private companies are moving away from private networks and using open data networks to meet their needs. While the security of open data networks is a concern, designers of network security are faced with an explosion of worldwide communications that includes increased data rates, universal connectivity, new services, and higher standards for performance. In such environments TFC can meet the growing need for protection from traffic analysis, but can be expensive because traffic masking involves the use of padding. Secure dynamic adaptive traffic masking (S-DATM) contributes to a global vision, providing the capability of operating in a commercial environment via traffic protected by appropriate levels of TFC with minimal impact on other traffic.

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