  Intrusion detection for computer network systems has been becoming one of the
most critical tasks for network administrators today. It has an important role
for organizations, governments and our society due to the valuable resources
hosted on computer networks. Traditional misuse detection strategies are unable
to detect new and unknown intrusion types. In contrast, anomaly detection in
network security aims 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, of
which it uses to detect new patterns that significantly deviate from the model.
Most of the current approaches on anomaly detection is based on the learning of
normal behavior and anomalous actions. They do not include memory that is they
do not take into account previous events classify new ones. In this paper, we
propose a one class collective anomaly detection model based on neural network
learning. 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 on normal time
series data before performing a 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 of a certain number of the latest
time-steps above a threshold will indicate a collective anomaly. The model is
evaluated on a time series version of the KDD 1999 dataset. The experiments
demonstrate that the proposed model is capable to detect collective anomaly
efficiently
