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 approach learns non-dependent entities in time series. The proposed approach tried to use a combination of IndRNN and LSTM approaches to learn dependent and non-dependent features. Feature selection approaches also provide a suitable view of features for the models; for this purpose, four feature selection models, Filter, Wrapper, Embedded, and Autoencoder were used. The proposed IndRNNLSTM algorithm, in combination with Embedded, was able to achieve MAE=1.22 and RMSE=9.92 on NSL-KDD data.

翻译：利用数据流预测检测软件定义网络（SDN）中的异常是一项困难任务。该问题属于时间序列与回归问题范畴。传统机器学习方法在该领域面临挑战，主要原因在于需手动选取特征。而深度学习方法则具备自动选取特征的重要优势，其中基于循环神经网络（RNN）的方法应用最为广泛。LSTM与GRU方法可良好学习依赖实体，而IndRNN方法则擅长学习时间序列中的非依赖实体。本研究提出的方法尝试结合IndRNN与LSTM方法，以同时学习依赖与非依赖特征。此外，特征选择方法为模型提供了适宜的特征视图；为此，本文采用了四种特征选择模型：过滤式（Filter）、封装式（Wrapper）、嵌入式（Embedded）及自编码器（Autoencoder）。所提出的IndRNNLSTM算法与嵌入式特征选择方法结合后，在NSL-KDD数据集上达到了MAE=1.22、RMSE=9.92的性能指标。