Using the Hidden Markov Model from lecture $4$ as inspiration, build a metamorphic malware detector tool that takes as input sequences of opcodes as a text file and outputs the probability that the opcodes are instructions from malware within the metamorphic family or not. Also, in your video, highlight the design features mentioned below. Turn in your video as well as your code as a python file or Juypter notebook along with a readme file describing how to run your code $($the video should also illustrate this point$).10\%$ of your grade will come from the ability of your tool to classify $2$ unseen opcode sequences as from metamorphic malware or a legit application.

Design Features

To do this, use the dataset I supplied and the ML analytic development process to develop a metamorphic detector model. Then export this model and write code that uses this exported model to read in opcode sequences and determines if they are part of the metamorphic family that the model was trained on$.$