AI: Anomaly Detection in logfiles

 ➤ IMPORTANT: This page is still under construction.

Summary

This guide will create a basic AI model to perform binary classification in order to detect anomalies in logfiles. This AI model is also suitable for the Jetson AGX Xavier Development Kit

Requirements

• Packages: TensorFlow, Keras, Pandas, sklearn, numpy, seaborn, matplotlib
• Software: Pycharm or any other python editor

Description

Step 1 - Create a model

First we need to create a sequential model, which can be trained later.

model = Sequential()

The next step is to create an input layer consisting of 63 nodes, one for every feature we have in our dataset.

 model.add(Dense(63))

Next a hidden layer consisting of 128 nodes with the ReLU (Rectified Linear Unit) activation function.

model.add(Dense(128, Activation('relu')))

And finally the output layer consisting of 1 node which represents 'attack' or 'no attack'

model.add(Dense(1))

Now we could change the learning rate to a specific value, but we just leave it at the default 0.001

learning_rate = 0.001

For the optimizer we just use the Adam Optimizer with the pre-defined learning rate.

optimizer = tf.optimizers.Adam(learning_rate)

Lastly we need to compile the model, for the loss function we use BinaryCorssentropy, our optimizer and the metric should be the accuarcy of the model

model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), 
  optimizer=optimizer,
  metrics=['accuracy']) 

WIP

Step 2

WIP

Used Hardware

• Jetson AGX Xavier Development Kit