<transcript><audio_30>destined to advertising and tracking services the labels can then be used to Train no more ads like machine learning classifiers as I show you here next so on the server side we have no more ets collecting data and then that data can</transcript><transcript><audio_31>be fed into training machine learning classifiers the classifiers can then be pushed to the Android device where we also have a VPN interception service such as ant monitor this service will</transcript><transcript><audio_32>then feed it the traffic data to the classifiers and the classifiers will provide back a decision another thing provided by VPN services such as an Tamala term is the ability to map packets to applications so we can</transcript><transcript><audio_33>potentially build specialized classifiers that are tailored to a particular app so next I will describe our no more ets system this is where our main contribution is and then later I will describe our machine learning</transcript><transcript><audio_34>approach so first how do we build gnome ideas let me share with you some insights we had prior to building this our primary interests here are third party advertising and tracking libraries and one thing to note is that application code is separated into</transcript><transcript><audio_35>labelled packages for instance here I am showing the Z job which is some wallpaper app and we see the package belonging to this edge itself as well as several packages belonging to third-party libraries and the second</transcript><transcript><audio_36>thing to note is that stack traces indicate package names as well so here I show an SSL handshake being initiated by the mop-up library here's one more stack trace where we don't see any third-party library we only see the zedge app itself</transcript> destined to advertising and tracking services the labels can then be used to Train no more ads like machine learning classifiers as I show you here next so on the server side we have no more ets collecting data and then that data can be fed into training machine learning classifiers the classifiers can then be pushed to the Android device where we also have a VPN interception service such as ant monitor this service will then feed it the traffic data to the classifiers and the classifiers will provide back a decision another thing provided by VPN services such as an Tamala term is the ability to map packets to applications so we can potentially build specialized classifiers that are tailored to a particular app so next I will describe our no more ets system this is where our main contribution is and then later I will describe our machine learning approach so first how do we build gnome ideas let me share with you some insights we had prior to building this our primary interests here are third party advertising and tracking libraries and one thing to note is that application code is separated into labelled packages for instance here I am showing the Z job which is some wallpaper app and we see the package belonging to this edge itself as well as several packages belonging to third-party libraries and the second thing to note is that stack traces indicate package names as well so here I show an SSL handshake being initiated by the mop-up library here's one more stack trace where we don't see any third-party library we only see the zedge app itself
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