A Review of Network Traffic Analysis and Prediction Techniques
Not on the map. Why?
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jul 2015.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 2; 2025: 1.
4 papers.
- 2024Poisoning Attacks on Federated Learning-based Wireless Traffic Predictioncited by 10
- 2021Temporal Graph Signal Decompositioncited by 8
- 2024Deep Learning Model Security: Threats and Defensescited by 4
- 2025Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecastingcited by 1
- 2025Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecasting20 Mar 2025
- 2024Deep Learning Model Security: Threats and Defenses12 Dec 2024
- 2024Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction22 Apr 2024
- 2021Temporal Graph Signal Decomposition25 Jun 2021