AI: Anomaly Detection in logfiles

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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 0 - Import the needed packages/libraries

from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.models import Sequential, load_model
from keras.layers import  Dense
import pandas as pd
import sklearn.model_selection as sk
from DataCleaner import cleaner

pd.options.mode.chained_assignment = None
import numpy as np
import tensorflow as tf
import seaborn as sns 
from sklearn.metrics import confusion_matrix
from matplotlib import pyplot as plt

Step 1 - Read the dataset

First we need to read the data, for that we can use the predefined function from pandas 'read_csv'

logfile_features = pd.read_csv(path)

Afterwards we replace the infinite values with nans and drop them all together

logfile_features.replace([np.inf, -np.inf], np.nan, inplace=True)
logfile_features.dropna(inplace=True)

Our dataset has labels which define if its an attack or not, so we replace them with numericals (0 and 1)

logfile_features["Label"].replace({"Benign": 0, "DoS attacks-Slowloris": 1, "DoS attacks-GoldenEye": 1}, inplace=True)

Next we shuffle our dataset

logfile_features = logfile_features.sample(frac=1) 

Now we need to split our data into 3 parts: Training data (60%), Test data (20%) and Validation data (20%). To do that we use the following methods

train_dataset, temp_test_dataset = sk.train_test_split(logfile_features, test_size=0.4)
test_dataset, valid_dataset = sk.train_test_split(temp_test_dataset, test_size=0.5)

Next we extract the labels from the actual dataset, we need them extra for our training

train_labels = train_dataset.pop('Label')
test_labels = test_dataset.pop('Label')
valid_labels = valid_dataset.pop('Label')

To norm our data correctly we need to get the stats of our training data (mean and standard deviation)

train_stats = train_dataset.describe()
train_stats = train_stats.transpose()

For norming we use a following method

def norm(x, stats):
    return (x - stats['mean']) / stats['std']

This method can then be used the following way

 normed_train_data = norm(train_dataset, train_stats)
 normed_test_data = norm(test_dataset, train_stats)
 normed_valid_dataset = norm(valid_dataset, train_stats)

Step 2 - 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 with exactly as many nodes as features in our training data

 model.add(Dense(normed_train_data.shape[1],input_shape=(normed_train_data.shape[1],))

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']) 

Step 3 - Train the model

First we set our epochs, a complete pass of the normed training data through the model, and our batch size, after how many datapoints the model gets updated

EPOCHS = 5000
batch_size = 1024

To not have to wait for 5000 training epochs to finish and to prevent overfitting we can set an early stop

es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=2)

Finally the training, model fitting, can start

with tf.device('/CPU:0'): 
   # with tf.device('/GPU:0'): # wenn man mit der Grafikkarte trainieren will
   history = model.fit(
       normed_train_data,
       train_labels,
       batch_size=batch_size,
       epochs=EPOCHS,
       verbose=1,
       shuffle=True,
       steps_per_epoch=int(normed_train_data.shape[0] / batch_size),
       validation_data=(normed_valid_dataset, valid_labels), callbacks=[es],
   )

Step 4 - Plot the results

After the training has been completed you can easily plot the accuarcy and validation accuracy during the training using

plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['Train', 'Cross-Validation'], loc='upper left')
plt.show()

To show the accuarcy when tested against the test data, meaning data the model didn't train with, we can use a confusion matrix the following

ax = plt.subplot()
predict_results = model.predict(normed_test_data)
predict_results = (predict_results > 0.5)
cm = confusion_matrix(test_labels, predict_results)
ax.set_xlabel('Predicted labels')
ax.set_ylabel('True labels')
ax.set_title('Confusion Matrix')
ax.xaxis.set_ticklabels(['No Attack', 'Attack'])
ax.yaxis.set_ticklabels(['No Attack', 'Attack'])
plt.show()

Used Hardware

• Jetson AGX Xavier Development Kit