Semi-supervised Content AD Sensor Sample Clauses

Semi-supervised Content AD Sensor. We have implemented a content-based AD sensor that represents an adaptation of Xxxxxxx’x ideas [14]. Xxxxxxx proposes an algorithm that incorporates only the most heavily weighted grams to the behavior profile. These grams are the ones that best discriminate between two or more classes of data. Although Xxxxxxx is more expensive than other AD sensors, we chose it because it rapidly captures the significant information of the traffic being exchanged. In our sensor, we consider two classes of data (content): good samples (goodS ) and bad samples (badS ). The content of the traffic exchanged is captured as 3- grams. This choice is less computationally expensive than higher n-grams and appropriately captures the specifics of email traffic (as it will be shown in Section 4.3). The weight (frequency) of each 3-gram observed during training is calcu- lated using Xxxxxxx’x Formula (see Equation 2), where the frequency W of each 3-gram i (W(i)=F(i)×U(i)×A(i)) is expressed as, W (i) = log( xi )×( Ng logNg ΣNg ) j=1 (pij(log( 1 pij
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