Binary Black-Box Adversarial Attacks with Evolutionary Learning against IoT Malware Detection

Algorithm 1

Malware sample evolution.

Input: malware samples , population scale, number of generations

Output: modified samples

BEGIN

for in do

  Initialize the population;

  while current generation or action sequence is not minimum do

   Map binary sequences to action sequences;

   Modify malware sample based on the action sequences;

   Calculate fitness;

   Select the best offspring;

   Perform crossover;

   Perform mutation;

   Increase current generation;

  end while

  Append the optimal result to ;

end for

Return ;

END