I have a homework. The task is to decide, if the PRNG generated lottery is attackable/crackable or not.
Details:
Lottery: There is a lottery game where you have to choose 8 numbers between 1-20 for the field A and choose 1 number between 1-4 for field B. It is generated every 5 minutes(7:05 - 22:00), so there are ~64k draws/year.
For example:
- A: [3, 5, 6, 7, 10, 13, 17, 18]
- B: 2
Possible dependent variables: Timestamp, DrawNum (It is between 85-264 every day. 7:05 is 85 because there are 425 minutes between 00:00 and 7:05. (425/5=85))
Unfortunately we don't have too much dependent variable and there is no clue for the PRNG algorithm. I think this two dependent variable is not enough to predict the numbers. I am thinking on an LSTM to predict the next 1stNum based on the previous ones and use the same model for the other numbers.
What do you think? How would you predict the next set of numbers? Which ML algorithm is the best for this use case?