Research Article

LNNLS-KH: A Feature Selection Method for Network Intrusion Detection

Algorithm 1

The LNNLS-KH algorithm.
Input: Training set
Output: Global best solution, the number of selected features, and feature selection time
(1) Begin:
(2) Initialize algorithm parameters:
(3) Initialize the krill herd position
(4) Evaluate the fitness of krill individuals and find the individuals with the best and worst fitness values
(5) fortodo
(6) for each krill individual do
(7)  Calculate the three components of motion:
(8)   (1) The motion induced by other krill individuals
(9)   (2) The foraging activity
(10)   (3) The nonlinear optimized physical diffusion
(11)  Implement crossover operator
(12)  Update krill herd position and fitness values
(13)   Calculate the linear nearest neighbor lasso step and new position using equations (24) and (25), and update new fitness values.
(14)  if Kyk > Ki or (Kj)
(16)   Leave Ki or (Kj) and delete Kyk
(17)  else
(18)   Leave Kyk and delete Ki or (Kj)
(19)  end if
(19) end for
(20) Update Xgb and Kgb of the globally optimal individuals
(21) end for
(22) Output the global best solution, the number of selected features and feature selection time
(23) End