Network Traffic Anomaly Detection Algorithm Based on Intuitionistic Fuzzy Time Series Graph Mining

Abstract

Network traffic anomaly detection is an important technology in cyberspace security. Combining information entropy theory and a variable ordering heuristic intuitionistic fuzzy time series forecasting model, we present a traffic anomaly detection algorithm based on intuitionistic fuzzy time series graph mining. For multi-dimensional attribute entropy of network traffic data, we establish multiple parallel and independent variable ordering heuristic intuitionistic fuzzy time series forecasting models. At each moment, using the multi-dimensional attribute entropy values as vertices, we construct complete graphs using amplitudes of the change in entropy values and edge weights between vertices defined by similarity, and establish an intuitionistic fuzzy time series graph of the traffic data in the time dimension. We perform frequent subgraph mining on the intuitionistic fuzzy time series graph; build the anomaly vectors based on the mining results, and implement adaptive determination for network traffic anomalies by fitting the anomaly vectors. Comparative experiments on universal datasets verify the superior performance of the algorithm.