Research Article
Network Traffic Obfuscation against Traffic Classification
Algorithm 1
Improved BIM using method 1.
| Input: classifier f; loss function J; original sample x; authentic label y; disturbance size α; fixed number of iterations n; number of iterations T. | | output: adversarial sample. | (1) | ; | (2) | ; | (3) | for to do | (4) | get gradient of func f with respect to ; | (5) | //update the adversarial sample | (6) | | (7) | if then | (8) | //modify | (9) | | (10) | set all elements greater than 0 in the vector to | (11) | 0; | (12) | ; | (13) | end if | | end for. | | return ; |
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