Anomaly Detection using Network Metadata

The proliferation of numerous network function today gave rise to the importance of network traffic classification against various cyber-attacks. Automatic training with a huge number of representative data necessitates the creation of a model for an efficient classifier. As a result, automatic categorization requires using training techniques capable of assigning classes to data objects based on the activities supplied to learn classes. Predefined classes allow for the detection of new items. However, the analysis and categorization of data activity in intrusion detection systems are vulnerable to a wide range of threats. Thus, New methods of analysis must be developed in order to establish an appropriate approach for monitoring circulating traffic in order to solve this problem. The major goal of this research is to develop and verify a heterogeneous traffic classifier that can classify the collected metadata of networks. In this study, a new model is proposed, which is based on machine learning technique, to increase the accuracy of prediction. Prior to the analysis stage, the gathered traffic is subjected to preprocessing. This paper aims to provide the mathematical validation of a novel machine learning classifier for heterogeneous traffic and anomaly detection.