Towards the Development of a Cloud Computing Intrusion Detection Framework Using an Ensemble Hybrid Feature Selection Approach

Table 6

The HEFSM algorithm.

Step	Description
Step 1	Let F represent the array of feature set in the dataset, where n is the number of features in the dataset such that n = 41. Fn = F1, F2, F3,…….., F41 and let E represent an empty array of the selected features from the ensemble FS process.
Step 2	Let T represent the target class of the dataset such that T is either 0 or 1, where 0 is a normal traffic and 1 is intrusion.
Step 3	Using the filter FS method with chi-square approach, compute a score for each feature in Fn and store the results in an array X.
Step 4	Using the wrapper FS method with Random Forest ML inbuilt FS algorithm, compute the feature importance score for each feature in Fn and store the results in Array Y.
Step 5	Using the embedded FS method with AdaBoost ML inbuilt FS algorithm, train the model and select the best performing features with the highest classification accuracy and assign scores to each feature in Fn and store results in an array Z.
Step 6	Remove features in Arrays X, Y, and Z where score is equal to zero.
Step 7	Set j = 1 and repeat step 8, step 9, and step 10 while j ≤ 41.
Step 8	IF Fj is in Array X and Fj is in Array Y and Fj is in Array Z, then go to step 9 or otherwise go to step 10.
Step 9	Store Fj in the final array of the ensemble features E.
Step 10	Increment j by 1.
Step 11	Output the final feature set in Array E as the ensemble features.