An Entropy-based Hybrid Mechanism for Large-Scale Wireless Network Traffic Prediction

The rising of the Internet of Things (IoT) applications fosters the exponential increase of smart devices, expanding the Internet’s attacking surface. Anomaly prediction mechanisms are mandatory to anticipate security threats. Besides, traffic monitoring and prediction models deliver more resilient and efficient network services. This paper proposes a lightweight user-behavior prediction mechanism based on the decomposition of the network traffic features’ entropy through Discrete Wavelet Transform (DWT) applied to network-flow Shannon Entropy’s time series. The DWT decomposes the entropy into linear and nonlinear components. We compare two forecasting models using Long Short Term Memory (LSTM) Networks and Auto-Regressive Integrated Moving Averages (ARIMA). We evaluate our mechanism in a large-scale academic wireless network, with more than 500 access points. LSTM performs up to 10 times better than ARIMA for predicting the real value of nonlinear flow-source entropy. Considering the transport protocol entropy, LSTM is up to 8 times better than ARIMA, and our results show a high entropy value. LSTM also outperforms ARIMA concerning the prediction time, which is 42% lower for LSTM’s worst-case training time than ARIMA’s best-case training time.