<transcript><audio_54>our machine learning approach so using our system we collected a new data set of 307 apps taking the top 10 free apps from the 35 Google Play Store categories and automating these apps we collected</transcript><transcript><audio_55>37,000 packets 13,000 of which were sent by advertising and tracking libraries using this data we trained several classifiers first we trained what we refer to as a general classifier meaning we took our entire data set and trained</transcript><transcript><audio_56>one decision tree on that data set and we evaluated using various features on this data set starting by using the hostname alone with this feature alone we were able to achieve an f1 score of 90% and the training time here was passed</transcript><transcript><audio_57>because it's just one feature next we evaluate other features here I will only focus on our full feature set which is the full URL and all the HTTP headers please refer to the paper for more details</transcript><transcript><audio_58>so using more features increased our f1 score to over 95% however the training time went up to over 6 close to 6 hours and the resultant tree node size was 371 nodes to see if we can improve on this</transcript><transcript><audio_59>we also explored training what we refer to as a per app classifier so taking a particular apps data alone and training a specialized decision tree for that app here using the hostname alone was already outperforming the general</transcript> our machine learning approach so using our system we collected a new data set of 307 apps taking the top 10 free apps from the 35 Google Play Store categories and automating these apps we collected 37,000 packets 13,000 of which were sent by advertising and tracking libraries using this data we trained several classifiers first we trained what we refer to as a general classifier meaning we took our entire data set and trained one decision tree on that data set and we evaluated using various features on this data set starting by using the hostname alone with this feature alone we were able to achieve an f1 score of 90% and the training time here was passed because it's just one feature next we evaluate other features here I will only focus on our full feature set which is the full URL and all the HTTP headers please refer to the paper for more details so using more features increased our f1 score to over 95% however the training time went up to over 6 close to 6 hours and the resultant tree node size was 371 nodes to see if we can improve on this we also explored training what we refer to as a per app classifier so taking a particular apps data alone and training a specialized decision tree for that app here using the hostname alone was already outperforming the general
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