Aliasing and interaction are completely different concepts.
Aliasing occurs when there is a correlation between the factors themselves. For instance, the GLM software would split the factors of a categorical variable into separate variables for ease of computation. Therefore, gender (assuming it has 2 levels - M and F) would have isMale and isFemale as two variables in the GLM model. These two levels are highly correlated (i.e., if you are Male, you are automatically not a female). Thus, these two are said to be intrinsically aliased.
Interaction occurs when the combined effect of two factors provides a better explanation of the output when compared to trying to explain the model with the two factors individually. So the factors influence the output in a different way (for instance, a classic case is the interaction between gender and age). Younger males could be more risky than younger females but older males could be more safer than older females. When you plot the relativities of the two factors (by exposure), you would see that the relativities "cross" each other, which is somewhat of a layperson explanation of interaction
You also have to understand that interaction increases the complexity of the model (because it adds additional variables to the model) while aliasing implicitly makes the model simpler (since it removes factors from the equation). Intrinsic aliasing is somethign that you woudn't have to worry about (as the GLM software automatically takes care of this). You will have to be more careful about extrinsic aliasing, which occurs due to issues in data (along with other factors)
