<transcript><audio_60>classifier and achieving an f1 score of 93% again training time is passed and tree nodes are size is small on average however when using the full feature set on the perab classifier we noticed that it does slightly worse than the general</transcript><transcript><audio_61>one but still achieves a high FF score of 93% and on top of that it's training time as much faster it's only dozens of milliseconds compared to ours and the resultant tree size is small and</transcript><transcript><audio_62>intuitive so here's an example of one such tree we see that whenever there is an app ID parameter in the URL we label we label the packet as advertising and tracking or when there is crashlytics</transcript><transcript><audio_63>again we label it as advertising and tracking in terms of prediction time both classifiers perform well in the orders of milliseconds so both can be used on top of VPN interception service and predict packets in real time however</transcript><transcript><audio_64>we recommend the pair up classifier since it's much more intuitive to look at and faster to train and retrain one apps update to evaluate our approaches both the Nomo ats labels and our</transcript><transcript><audio_65>classifiers we took our data set and fed them through several filter lists specifically we used ez lists which is the most common list used for ad locking in browsers an easy privacy which is used to supplement easy lists and block trackers as well</transcript><transcript><audio_66>we also used a mobile specific lists which has an interesting name it's called mother of all ad blocking which I will refer to as more a be using these lists we labeled our data and then compared these labels with our own no</transcript><transcript><audio_67>Mightiest labels so out of the 37,000 requests that we collected both approaches meaning the lists and know my TS labelled 16,000 samples as negative in terms of positive labels here's a Venn diagram of the approaches where</transcript> classifier and achieving an f1 score of 93% again training time is passed and tree nodes are size is small on average however when using the full feature set on the perab classifier we noticed that it does slightly worse than the general one but still achieves a high FF score of 93% and on top of that it's training time as much faster it's only dozens of milliseconds compared to ours and the resultant tree size is small and intuitive so here's an example of one such tree we see that whenever there is an app ID parameter in the URL we label we label the packet as advertising and tracking or when there is crashlytics again we label it as advertising and tracking in terms of prediction time both classifiers perform well in the orders of milliseconds so both can be used on top of VPN interception service and predict packets in real time however we recommend the pair up classifier since it's much more intuitive to look at and faster to train and retrain one apps update to evaluate our approaches both the Nomo ats labels and our classifiers we took our data set and fed them through several filter lists specifically we used ez lists which is the most common list used for ad locking in browsers an easy privacy which is used to supplement easy lists and block trackers as well we also used a mobile specific lists which has an interesting name it's called mother of all ad blocking which I will refer to as more a be using these lists we labeled our data and then compared these labels with our own no Mightiest labels so out of the 37,000 requests that we collected both approaches meaning the lists and know my TS labelled 16,000 samples as negative in terms of positive labels here's a Venn diagram of the approaches where
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