and the primary domain name. This was done by having a separate dictionary for each of these segments. The distinction would allow for preserving some of the order in which the words occurred. For example, it allows us to distinguish between the presence of “com” in the top-level domain vs other parts of the URL. [@blum2010lexical] try to enhance the lexical features by considering the usage of bi-gram features, i.e., they construct a dictionary, where in addition to single-words the presence of a set of 2-words in the same URL is considered a feature. In addition, they record the position of sensitive tokens and bigrams to exploit the token context sensitivity.

The entire bag-of-word features approach can be viewed as a form of machine learning compatible fuzzy blacklist approach. Instead of focussing on the entire URL string, it assigns scores to the URL based on smaller components of the URL string. While this approach offers us an extensive number of features, it can become problematic while running sophisticated algorithms on them. For example, [@ma2009identifying] collected a dataset of 2 million URLs, having almost as many lexical features. This number may grow even larger if bi-gram features were considered. [@kolari2006svms] considered 