# A Review of Network Traffic Analysis and Prediction Techniques

arXiv: https://arxiv.org/abs/1507.05722 · HTML: https://mlsanity.com/paper/1507.05722/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2015.

## Built on by (most cited first, 4 of 4)

- [Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction](https://mlsanity.com/paper/2404.14389/) (2024)
- [Temporal Graph Signal Decomposition](https://mlsanity.com/paper/2106.13517/) (2021)
- [Deep Learning Model Security: Threats and Defenses](https://mlsanity.com/paper/2412.08969/) (2024)
- [Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecasting](https://mlsanity.com/paper/2503.17410/) (2025)

## Abstract (arXiv)

Analysis and prediction of network traffic has applications in wide comprehensive set of areas and has newly attracted significant number of studies. Different kinds of experiments are conducted and summarized to identify various problems in existing computer network applications. Network traffic analysis and prediction is a proactive approach to ensure secure, reliable and qualitative network communication. Various techniques are proposed and experimented for analyzing network traffic including neural network based techniques to data mining techniques. Similarly, various Linear and non-linear models are proposed for network traffic prediction. Several interesting combinations of network analysis and prediction techniques are implemented to attain efficient and effective results. This paper presents a survey on various such network analysis and traffic prediction techniques. The uniqueness and rules of previous studies are investigated. Moreover, various accomplished areas of analysis and prediction of network traffic have been summed.