A hybrid IndRNNLSTM approach for real-time anomaly detection in software-defined networks

Anomaly detection in SDN using data flow prediction is a difficult task. This problem is included in the category of time series and regression problems. Machine learning approaches are challenging in this field due to the manual selection of features. On the other hand, deep learning approaches have important features due to the automatic selection of features. Meanwhile, RNN-based approaches have been used the most. The LSTM and GRU approaches learn dependent entities well; on the other hand, the IndRNN …

anomaly anomaly detection cs.ai cs.lg cs.ni data data flow deep learning detection features flow hybrid machine machine learning networks prediction real-time regression series software software-defined time series