i am studying about the use of n-gram models to classify web attacks based on several parameters like, requested resources, query parameters and attributes, characters distribution and so on. Most research papers use n-grams frequencies as feature vectors for their machine learning algorithms.
Ascii code only allows the use of 256 characters, so if i want to use 1-gram, my feature vector will have 256 dimensions, and for n-grams it will $ 256^n\,$ dimensions.
A feature vector with such a long dimension leads to the so called "curse of dimensionality".
The use of bi-grams frecuencies as feature vectors results in good accuracy by classification algorithms.
If the use of bi-grams gives greats results, the use of tri-grams give better results against bi-grams.
So, my question is as follows : If we increase n, we should have better results compared to lower values of n when classifying queries? Can i conclude that for example the use of 7-grams would produce greater accuracy when compared to 4-grams?