Effect Improved for High-Dimensional and Unbalanced Data Anomaly Detection Model Based on KNN-SMOTE-LSTM

Algorithm 1

The SMOTE algorithm steps.

	Input:T for training sample, N for oversampling rate, K for k-nearest neighbor parameter, and n for the number of the small-sized sample ()
	Output:S for new training sample
	Step 1: calculate the k-nearest neighbors of of minority samples () (Euclidean distance is adopted in this paper)
	Step 2: randomly select a sample from the k-nearest neighbors.
	Step 3: generate a random number ζ between 0 and 1 for synthesis of a new sample
	.
	Step 4: repeat the step 2 and step 3 according to the oversampling rate N
	Step 5: obtain new training sample S
	Step 6: the new training sample S was classified with a classifier
	Step 7: output classification results