Towards an Ensemble Regressor Model for ISP Traffic Prediction with Anomaly Detection and Mitigation

Saha, Sajal; Haque, Anwar; Sidebottom, Greg

doi:10.1109/isncc55209.2022.9851774

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Therefore, our proposed prediction model's performance has been evaluated based on multiple combinations of (features and targets). We provided five different varieties of feature and target variables, including {(6, 6), (9,6), (12,6), (15,6), and (18, 6)}, to our prediction model to find the best input set for six-step prediction. The exact process has been followed for other forecasting lengths in our experiment.…”

Section: Feature Extraction For Multi-step Predictionmentioning

confidence: 99%

“…Our preliminary investigation [ 6 ] involved studying different machine learning models belonging to the gradient boosting and descent categories for the purpose of single-step forecasting. Additionally, we created a new machine learning model that includes an anomaly detection and mitigation module, and it was found to be superior to existing prediction models.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Step Internet Traffic Forecasting Models with Variable Forecast Horizons for Proactive Network Management

Saha,

Haque,

Sidebottom

2024

Sensors

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The ISP (Internet Service Provider) industry relies heavily on internet traffic forecasting (ITF) for long-term business strategy planning and proactive network management. Effective ITF frameworks are necessary to manage these networks and prevent network congestion and over-provisioning. This study introduces an ITF model designed for proactive network management. It innovatively combines outlier detection and mitigation techniques with advanced gradient descent and boosting algorithms, including Gradient Boosting Regressor (GBR), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), CatBoost Regressor (CBR), and Stochastic Gradient Descent (SGD). In contrast to traditional methods that rely on synthetic datasets, our model addresses the problems caused by real aberrant ISP traffic data. We evaluated our model across varying forecast horizons—six, nine, and twelve steps—demonstrating its adaptability and superior predictive accuracy compared to traditional forecasting models. The integration of the outlier detection and mitigation module significantly enhances the model’s performance, ensuring robust and accurate predictions even in the presence of data volatility and anomalies. To guarantee that our suggested model works in real-world situations, our research is based on an extensive experimental setup that uses real internet traffic monitoring from high-speed ISP networks.

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Section: Feature Extraction For Multi-step Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Step Internet Traffic Forecasting Models with Variable Forecast Horizons for Proactive Network Management

Saha,

Haque,

Sidebottom

2024

Sensors

Self Cite

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“…At present, there is a clear trend related to identifying the best AI models for anomaly detection in ISP links in order to achieve the best possible detection performance. Applications in this area include both supervised and unsupervised methods [8], [9], [10], [11]. Of course, previously, network traffic sampling methods [12] were used for anomaly detection using traditional IDS probes.…”

Section: Introductionmentioning

confidence: 99%

Use of traffic sampling in anomaly detection for high-throughput network links

Bolanowski,

Paszkiewicz,

Mazur

2023

Annals of Computer Science and Information Systems

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Currently, anomaly detection is an increasingly important issue in terms of research work and applications in production systems. Information about system malfunction allows the implementation of precise diagnostic and corrective actions. Two main approaches based on statistical analysis and machine learning techniques are used in anomaly detection systems, which are computationally complex, especially when dealing with high traffic volumes in computer network. In this paper, the limitation of the sampling frequency for network traffic parameters is proposed as a technique to reduce the computational complexity of anomaly detection methods. The proposed approach has been verified in a real network link monitoring system for a mediumsized ISP. The results obtained are promising and can be used to build a production system that enables the development of early warning systems in the area of security incident detection dedicated to high-speed access links.

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