Anomaly Detection of Computer Networks using Deep Learning Techniques

In daily realistic activities many users are using the Internet. A tremendous increase in the various number of computer network threats compromises the network system that motivate the network anomaly detection system to be relevant and necessary to be implemented using various deep learning approaches for computer network issue. In the network related systems, the detection of such anomalous situations is still challenging. To tackle the issues related to the anomaly situations, deep neural networks with multiple hidden layers is proposed. Anomaly detection based on the user behavior is most essential to secure the machines from unauthorized activities. Anomalies of real time network data packets, net flow records are collected and applied an appropriated deep leaning models to enhance accuracy and speed by using python programming language. Main objective of the project is to identify anomalies in a computer network traffic using deep learning techniques for appropriate dataset. From the experimental results Random forest algorithm overtops support vector machine, Kohonen-self organizing maps and kmeans algorithms on UNSW-ND15 dataset due to the fact that this dataset doesn’t have any redundant network connections and connections being fairly distributed across all the classes.