Over-fitting issue in a classification problem (unbalanced data)
I'm working on a similar classification problem using Quinlans C5.0 and in my experience there are couple of tweaks inside that might help you. 


First, I believe using such big number (trials=500) for boosting is
affecting the overfitting, since the model will repeatedly try and
improve on the misclassified instances in every iteration. Try using
a smaller boosting size, or even turning it off - it might help with
the overfitting problem.
There are a couple of other parameters you might find useful. The minimal leaf size (minCases in C5.0Control() is something to you want to keep as big as possible when avoiding overfitting issues. 
Another one is the confidence factor (CF) which lets you control
the severity of pruning of the tree, meaning lower factor levels will likely prune away the leaves which overspecify the classification. 
Finally, you might try using the fuzzyThreshold control option which lets you 'soften' sharp thresholds with an inbetween grey area. 


These are all very well documented in the C5.0 vignette as well as on the informal tutorial page. 

Hope this helps you a bit.