Classification of obfuscated text data
The text is obfuscated for you but to the computer, this is as incomprehensible as English for instance. When classifying text, you always first vectorize your input, meaning that your text becomes a vector. This vector typically represents the frequency of the tokens in your vocabulary in that particular string (bag-of-words representation).

What you can do is represent each document/string as a vector representing the frequencies of characters, character n-grams, or even words. One you have your input, you can deal with it as any classification task.

Naive Bayes has shown to be a good lower bound for these types of tasks.