# Deep Sequence Modeling for Anomalous ISP Traffic Prediction

arXiv: https://arxiv.org/abs/2205.01685 · HTML: https://mlsanity.com/paper/2205.01685/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 2 of 2)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Theano: A Python framework for fast computation of mathematical expressions](https://mlsanity.com/paper/1605.02688/) (2016)

## Built on by (most cited first, 2 of 2)

- [Wavelet-Based Hybrid Machine Learning Model for Out-of-distribution Internet Traffic Prediction](https://mlsanity.com/paper/2205.04333/) (2022)
- [Transfer Learning Based Efficient Traffic Prediction with Limited Training Data](https://mlsanity.com/paper/2205.04344/) (2022)

## Abstract (arXiv)

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. LSTMEncoderDecoder (LSTMEnDe) 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), LSTMEnDe with Attention layer (LSTMEnDeAtn), 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.