26 citations · 52 across the 13 of their papers we have counts for
14 papers
A Resource Estimation Model for the Hardware-Software Co-Design of Distributed Quantum Architectures
Raymond P. H. Wu, Chathurika Ranaweera, Sutharshan Rajasegarar +3
In distributed quantum computing (DQC), executing monolithic quantum circuits across multiple interconnected quantum processing units (QPUs) requires dedicated communication qubits…
Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks
Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj Pokhrel
We propose a Quantum Federated Autoencoder for Anomaly Detection, a framework that leverages quantum federated learning for efficient, secure, and distributed processing in IoT net…
Q-AGNN: Quantum-Enhanced Attentive Graph Neural Network for Intrusion Detection
Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj Pokhrel
With the rapid growth of interconnected devices, accurately detecting malicious activities in network traffic has become increasingly challenging. Most existing deep learning-based…
In-network Attack Detection with Federated Deep Learning in IoT Networks: Real Implementation and Analysis
Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj Pokhrel +2
The rapid expansion of the Internet of Things (IoT) and its integration with backbone networks have heightened the risk of security breaches. Traditional centralized approaches to…
Efficient Time-Aware Partitioning of Quantum Circuits for Distributed Quantum Computing
Raymond P. H. Wu, Chathu Ranaweera, Sutharshan Rajasegarar +3
To overcome the physical limitations of scaling monolithic quantum computers, distributed quantum computing (DQC) interconnects multiple smaller-scale quantum processing units (QPU…
Modeling Wavelet Transformed Quantum Support Vector for Network Intrusion Detection
Swati Kumari, Shiva Raj Pokhrel, Swathi Chandrasekhar +5
Network traffic anomaly detection is a critical cybersecurity challenge requiring robust solutions for complex Internet of Things (IoT) environments. We present a novel hybrid quan…
Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Survey
Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj Pokhrel
This survey explores the integration of Federated Learning (FL) with Network Intrusion Detection Systems (NIDS), with particular emphasis on deep learning and quantum machine learn…
Robust Zero Trust Architecture: Joint Blockchain based Federated learning and Anomaly Detection based Framework
Shiva Raj Pokhrel, Luxing Yang, Sutharshan Rajasegarar +1
This paper introduces a robust zero-trust architecture (ZTA) tailored for the decentralized system that empowers efficient remote work and collaboration within IoT networks. Using…
Risk-Constrained Community Battery Utilisation Optimisation for Electric Vehicle Charging with Photovoltaic Resources
Khalil Gholami, Asef Nazari, Dhananjay Thiruvady +3
High penetration of renewable generation in the electricity grid presents power system operators with challenges including voltage instability mainly due to fluctuating power gener…
Double Attention-based Lightweight Network for Plant Pest Recognition
Sivasubramaniam Janarthan, Selvarajah Thuseethan, Sutharshan Rajasegarar +1
Timely recognition of plant pests from field images is significant to avoid potential losses of crop yields. Traditional convolutional neural network-based deep learning models dem…
ECG-Adv-GAN: Detecting ECG Adversarial Examples with Conditional Generative Adversarial Networks
Khondker Fariha Hossain, Sharif Amit Kamran, Alireza Tavakkoli +4
Electrocardiogram (ECG) acquisition requires an automated system and analysis pipeline for understanding specific rhythm irregularities. Deep neural networks have become a popular…
Effect of stress on cardiorespiratory synchronization of Ironmen athletes
Maia Angelova, Philip M. Holloway, Sergiy Shelyag +2
The aim of this paper is to investigate the cardiorespiratory synchronization in athletes subjected to extreme physical stress combined with a cognitive stress tasks. ECG and respi…
Scalable Bottom-up Subspace Clustering using FP-Trees for High Dimensional Data
Minh Tuan Doan, Jianzhong Qi, Sutharshan Rajasegarar +1
Subspace clustering aims to find groups of similar objects (clusters) that exist in lower dimensional subspaces from a high dimensional dataset. It has a wide range of applications…
A Scalable Framework for Trajectory Prediction
Punit Rathore, Dheeraj Kumar, Sutharshan Rajasegarar +2
Trajectory prediction (TP) is of great importance for a wide range of location-based applications in intelligent transport systems such as location-based advertising, route plannin…