论文标题
异常ISP流量预测的深层序列建模
Deep Sequence Modeling for Anomalous ISP Traffic Prediction
论文作者
论文摘要
现实世界中的互联网流量容易受到各种外部和内部因素的影响，这些因素可能会突然改变正常的交通流量。这些意外的变化被认为是流量的异常值。但是，深层序列模型已被用来预测复杂的IP流量，但是尚未对它们的异常流量进行比较性能。在本文中，我们调查并评估了不同深层序列模型的性能进行异常交通预测的性能。实施了几个深层序列模型，以预测没有异常值的实际流量，并在现实世界流量预测中显示出异常检测的重要性。首先，应用了两种不同的离群检测技术，例如三个sigma规则和隔离森林，以识别异常。其次，在训练模型之前，我们使用向后填充技术调整了这些异常数据点。最后，比较了异常和调整后流量的不同模型的性能。 LSTM_ENCODER_DECODER（LSTM_EN_DE）是我们实验中的最佳预测模型，在调整异常值后，实际和预测流量之间的偏差将超过11 \％。所有其他模型，包括复发性神经网络（RNN），长期短期记忆（LSTM），具有注意力层的LSTM_EN_DE（LSTM_EN_DE_ATN），门控复发单元（GRU），在更换异位时显示出更好的预测，并将预测误差替换为29％，超过29％，超过29％，24％，19％和10％。我们的实验结果表明，数据中的异常值可以显着影响预测的质量。因此，离群检测和缓解措施有助于深层序列模型学习一般趋势并做出更好的预测。
Internet traffic in the real world is susceptible to various external and internal factors which may abruptly change the normal traffic flow. Those unexpected changes are considered outliers in traffic. However, deep sequence models have been used to predict complex IP traffic, but their comparative performance for anomalous traffic has not been studied extensively. In this paper, we investigated and evaluated the performance of different deep sequence models for anomalous traffic prediction. Several deep sequences models were implemented to predict real traffic without and with outliers and show the significance of outlier detection in real-world traffic prediction. First, two different outlier detection techniques, such as the Three-Sigma rule and Isolation Forest, were applied to identify the anomaly. Second, we adjusted those abnormal data points using the Backward Filling technique before training the model. Finally, the performance of different models was compared for abnormal and adjusted traffic. LSTM_Encoder_Decoder (LSTM_En_De) is the best prediction model in our experiment, reducing the deviation between actual and predicted traffic by more than 11\% after adjusting the outliers. All other models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), LSTM_En_De with Attention layer (LSTM_En_De_Atn), Gated Recurrent Unit (GRU), show better prediction after replacing the outliers and decreasing prediction error by more than 29%, 24%, 19%, and 10% respectively. Our experimental results indicate that the outliers in the data can significantly impact the quality of the prediction. Thus, outlier detection and mitigation assist the deep sequence model in learning the general trend and making better predictions.
