  Intrusion detection for computer network systems becomes one of the most
critical tasks for network administrators today. It has an important role for
organizations, governments and our society due to its valuable resources on
computer networks. Traditional misuse detection strategies are unable to detect
new and unknown intrusion. Besides, anomaly detection in network security is
aim to distinguish between illegal or malicious events and normal behavior of
network systems. Anomaly detection can be considered as a classification
problem where it builds models of normal network behavior, which it uses to
detect new patterns that significantly deviate from the model. Most of the cur-
rent research on anomaly detection is based on the learning of normally and
anomaly behaviors. They do not take into account the previous, re- cent events
to detect the new incoming one. In this paper, we propose a real time
collective anomaly detection model based on neural network learning and feature
operating. Normally a Long Short Term Memory Recurrent Neural Network (LSTM
RNN) is trained only on normal data and it is capable of predicting several
time steps ahead of an input. In our approach, a LSTM RNN is trained with
normal time series data before performing a live prediction for each time step.
Instead of considering each time step separately, the observation of prediction
errors from a certain number of time steps is now proposed as a new idea for
detecting collective anomalies. The prediction errors from a number of the
latest time steps above a threshold will indicate a collective anomaly. The
model is built on a time series version of the KDD 1999 dataset. The
experiments demonstrate that it is possible to offer reliable and efficient for
collective anomaly detection.
