activity
20222024
most citedAn Adaptive End-to-End IoT Security Framework Using Explainable AI and LLMs

2 citations · 7 across the 10 of their papers we have counts for

collaborators

11 papers

cs.CV2024★ 2 cited

Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network

Sareh Soltani Nejad, Anwar Haque

The widespread implementation of urban surveillance systems has necessitated more sophisticated techniques for anomaly detection to ensure enhanced public safety. This paper presen…

cs.NI2024

Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach

Wang Wumian, Sajal Saha, Anwar Haque +1

Traffic routing is vital for the proper functioning of the Internet. As users and network traffic increase, researchers try to develop adaptive and intelligent routing algorithms t…

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.LG2024★ 2 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.LG2023

DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction

Sajal Saha, Sudipto Baral, Anwar Haque

Internet traffic volume estimation has a significant impact on the business policies of the ISP (Internet Service Provider) industry and business successions. Forecasting the inter…

cs.CR2023

DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection

Caroline Strickland, Chandrika Saha, Muhammad Zakar +4

Our increasingly connected world continues to face an ever-growing amount of network-based attacks. Intrusion detection systems (IDS) are an essential security technology for detec…

cs.LG2022★ 1 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.NI2022

An Empirical Study on Internet Traffic Prediction Using Statistical Rolling Model

Sajal Saha, Anwar Haque, Greg Sidebottom

Real-world IP network traffic is susceptible to external and internal factors such as new internet service integration, traffic migration, internet application, etc. Due to these f…

cs.LG2022★ 2 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…