most citedTowards an Ensemble Regressor Model for Anomalous ISP Traffic Prediction

2 citations · 7 across the 7 of their papers we have counts for

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cs.LG2024

Overcoming Data Limitations in Internet Traffic Forecasting: LSTM Models with Transfer Learning and Wavelet Augmentation

Sajal Saha, Anwar Haque, Greg Sidebottom

Effective internet traffic prediction in smaller ISP networks is challenged by limited data availability. This paper explores this issue using transfer learning and data augmentati…

cs.LG20242 cited

An Adaptive End-to-End IoT Security Framework Using Explainable AI and LLMs

Sudipto Baral, Sajal Saha, Anwar Haque

The exponential growth of the Internet of Things (IoT) has significantly increased the complexity and volume of cybersecurity threats, necessitating the development of advanced, sc…

cs.LG20221 cited

Transfer Learning Based Efficient Traffic Prediction with Limited Training Data

Sajal Saha, Anwar Haque, Greg Sidebottom

Efficient prediction of internet traffic is an essential part of Self Organizing Network (SON) for ensuring proactive management. There are many existing solutions for internet tra…

cs.LG2022

Wavelet-Based Hybrid Machine Learning Model for Out-of-distribution Internet Traffic Prediction

Sajal Saha, Anwar Haque, Greg Sidebottom

Efficient prediction of internet traffic is essential for ensuring proactive management of computer networks. Nowadays, machine learning approaches show promising performance in mo…

cs.LG2022

Deep Sequence Modeling for Anomalous ISP Traffic Prediction

Sajal Saha, Anwar Haque, Greg Sidebottom

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 considere…

cs.LG20222 cited

Towards an Ensemble Regressor Model for Anomalous ISP Traffic Prediction

Sajal Saha, Anwar Haque, Greg Sidebottom

Prediction of network traffic behavior is significant for the effective management of modern telecommunication networks. However, the intuitive approach of predicting network traff…