Network related services, programs and applications are developing greatly, however, network security breaches are also developing with them. Network security is an evolving, challenging and a critical task. It is essential that there is a system in place to identify any harmful movement happening in network. An Intrusion detection system (IDS) has become the prerequisite software addressing cyber security in the modern era. Especially, with the greater complexity of advanced cyber-attacks and as such the uncertainty surrounding the detection of the types of attacks. This thesis proposes a novel approach using an ensemble of K-Means and Gaussian Mixture clustering combined with a deep neural network (DNN) algorithm. When compared with traditional artificial neural network’s (ANN’s) used within an IDS, our approach implements modern advanc
