An autoencoder setup for anomaly detection

I am doing anomaly detection using machine learning. i have tried different models like isolation forest, SVM and KNN. The maximum accuracy that I can get from each of them is $80\%$ accordind to my dataset which contains $5$ features and $4000$ data samples, $18\%$ of them are anomalous. When I use autoencoder and I adjust the proper reconstruction loss threshold I can get $92\%$ accuracy but the hidden layers setup of the autoencoder does not seems right despite the accuracy I get. As I said, I have only $5$ features and the setup as follows

 self.encoder = tf.keras.Sequential([
  layers.Dense(64, activation=relu),
  layers.Dense(32, activation=relu),
  layers.Dense(16, activation=relu)])

self.decoder = tf.keras.Sequential([
  layers.Dense(32, activation=relu),
  layers.Dense(64, activation=relu),
  layers.Dense(5, activation=sigmoid)])

Is this reasonable? because it looks like an encoder upsampling then downsampling that I did not see before which makes me missing something here.