DETECTION OF ANOMALIES IN THE TELECOMMUNICATIONS TRAFFIC BY STATISTICAL METHODS

Anomaly detection is an important task in many areas of human life. Many statistical methods are used to detect anomalies. In this paper, statistical methods of data analysis, such as survival analysis, time series analysis (fractal), classification method (decision trees), cluster analysis, entropy method were chosen to detect anomalies. A description of the selected methods is given. To analyze anomalies, the traffic and attack implementations from an open dataset were taken. More than 3 million packets from the dataset were used to analyze the described methods. The dataset contained legitimate traffic (75%) and attacks (25%). Simulation modeling of the selected statistical methods was performed on the example of network traffic implementations of telecommunication networks of different protocols. To implement the simulation, programs were written in the Pyton programming language. DDoS attacks, UDP-flood, TCP SYN, ARP attacks and HTTP-flood were chosen as anomalies. A comparative analysis of the performance of these methods to detect anomalies (attacks) on such parameters as the probability of anomaly detection, the probability of false positive detection, the running time of e