arXiv subjects

Sutharshan Rajasegarar

Publications and source records attributed to Sutharshan Rajasegarar.

14 recordsLinked to original sources

arXiv2026

A Resource Estimation Model for the Hardware-Software Co-Design of Distributed Quantum Architectures

In distributed quantum computing (DQC), executing monolithic quantum circuits across multiple interconnected quantum processing units (QPUs) requires dedicated communication qubits to generate and distribute entanglement. Because the number of physical qubits within a QPU is finite, a trade-off emerges where allocating more communication qubits increases the capacity of quantum channels for concurrent non-local operations, but reduces the number of computational qubits available for local gate operations. Distributed quantum compilation routinely ignores this channel capacity, while hardware architects lack a method to determine it prior to quantum circuit partitioning. Moreover, scheduling entanglement on demand introduces severe latency, whereas pre-fetching exposes stored pairs to decoherence. We propose an economic order quantity model from perishable inventory theory to optimize the trade-off between entanglement distribution latency and the time cost of decoherence. The resulting estimate is driven by algorithmic demand and physical constraints, offering a dual application for the hardware-software co-design of high-performance DQC: for hardware architects, it gives the optimal allocation of dedicated communication qubits in static heterogeneous architectures; for compiler developers, it gives the optimal number to reserve dynamically in homogeneous architectures.

quant-ph↗

arXiv2026

Q-AGNN: Quantum-Enhanced Attentive Graph Neural Network for Intrusion Detection

With the rapid growth of interconnected devices, accurately detecting malicious activities in network traffic has become increasingly challenging. Most existing deep learning-based intrusion detection systems treat network flows as independent instances, thereby failing to exploit the relational dependencies inherent in network communications. To address this limitation, we propose Q-AGNN, a Quantum-Enhanced Attentive Graph Neural Network for intrusion detection, where network flows are modeled as nodes and edges represent similarity relationships. Q-AGNN leverages parameterized quantum circuits (PQCs) to encode multi-hop neighborhood information into a high-dimensional latent space, inducing a bounded quantum feature map that implements a second-order polynomial graph filter in a quantum-induced Hilbert space. An attention mechanism is subsequently applied to adaptively weight the quantum-enhanced embeddings, allowing the model to focus on the most influential nodes contributing to anomalous behavior. Extensive experiments conducted on four benchmark intrusion detection datasets demonstrate that Q-AGNN achieves competitive or superior detection performance compared to state-of-the-art graph-based methods, while consistently maintaining low false positive rates under hardware-calibrated noise conditions. Moreover, we also executed the Q-AGNN framework on actual IBM quantum hardware to demonstrate the practical operability of the proposed pipeline under real NISQ conditions. These results highlight the effectiveness of integrating quantum-enhanced representations with attention mechanisms for graph-based intrusion detection and underscore the potential of hybrid quantum-classical learning frameworks in cybersecurity applications.

cs.CR↗

arXiv2026

In-network Attack Detection with Federated Deep Learning in IoT Networks: Real Implementation and Analysis

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 anomaly detection, which require transferring large volumes of data to central servers, suffer from privacy, scalability, and latency limitations. This paper proposes a lightweight autoencoder-based anomaly detection framework designed for deployment on resource-constrained edge devices, enabling real-time detection while minimizing data transfer and preserving privacy. Federated learning is employed to train models collaboratively across distributed devices, where local training occurs on edge nodes and only model weights are aggregated at a central server. A real-world IoT testbed using Raspberry Pi sensor nodes was developed to collect normal and attack traffic data. The proposed federated anomaly detection system, implemented and evaluated on the testbed, demonstrates its effectiveness in accurately identifying network attacks. The communication overhead was reduced significantly while achieving comparable performance to the centralized method.

cs.LG↗

arXiv2026

Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks

We propose a Quantum Federated Autoencoder for Anomaly Detection, a framework that leverages quantum federated learning for efficient, secure, and distributed processing in IoT networks. By harnessing quantum autoencoders for high-dimensional feature representation and federated learning for decentralized model training, the approach transforms localized learning on edge devices without requiring transmission of raw data, thereby preserving privacy and minimizing communication overhead. The model leverages quantum advantage in pattern recognition to enhance detection sensitivity, particularly in complex and dynamic IoT network traffic. Experiments on a real-world IoT dataset show that the proposed method delivers anomaly detection accuracy and robustness comparable to centralized approaches, while ensuring data privacy.

quant-ph↗

arXiv2026

Efficient Time-Aware Partitioning of Quantum Circuits for Distributed Quantum Computing

To overcome the physical limitations of scaling monolithic quantum computers, distributed quantum computing (DQC) interconnects multiple smaller-scale quantum processing units (QPUs) to form a quantum network. However, this approach introduces a critical challenge, namely the high cost of quantum communication between remote QPUs incurred by quantum state teleportation and quantum gate teleportation. To minimize this communication overhead, DQC compilers must strategically partition quantum circuits by mapping logical qubits to distributed physical QPUs. Static graph partitioning methods are fundamentally ill-equipped for this task as they ignore execution dynamics and underlying network topology, while metaheuristics require substantial computational runtime. In this work, we propose a heuristic based on beam search to solve the circuit partitioning problem. Our time-aware algorithm incrementally constructs a low-cost sequence of qubit assignments across successive time steps to minimize overall communication overhead. The time and space complexities of the proposed algorithm scale quadratically with the number of qubits and linearly with circuit depth, offering a significant computational speedup over common metaheuristics. We demonstrate that our proposed algorithm consistently achieves significantly lower communication costs than static baselines across varying circuit sizes, depths, and network topologies, providing an efficient compilation tool for near-term distributed quantum hardware.

quant-ph↗

arXiv2025

Modeling Wavelet Transformed Quantum Support Vector for Network Intrusion Detection

Network traffic anomaly detection is a critical cybersecurity challenge requiring robust solutions for complex Internet of Things (IoT) environments. We present a novel hybrid quantum-classical framework integrating an enhanced Quantum Support Vector Machine (QSVM) with the Quantum Haar Wavelet Packet Transform (QWPT) for superior anomaly classification under realistic noisy intermediate-scale Quantum conditions. Our methodology employs amplitude-encoded quantum state preparation, multi-level QWPT feature extraction, and behavioral analysis via Shannon Entropy profiling and Chi-square testing. Features are classified using QSVM with fidelity-based quantum kernels optimized through hybrid training with simultaneous perturbation stochastic approximation (SPSA) optimizer. Evaluation under noiseless and depolarizing noise conditions demonstrates exceptional performance: 96.67% accuracy on BoT-IoT and 89.67% on IoT-23 datasets, surpassing quantum autoencoder approaches by over 7 percentage points.

quant-ph↗

arXiv2025

Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Survey

This survey explores the integration of Federated Learning (FL) with Network Intrusion Detection Systems (NIDS), with particular emphasis on deep learning and quantum machine learning approaches. FL enables collaborative model training across distributed devices while preserving data privacy-a critical requirement in network security contexts where sensitive traffic data cannot be centralized. Our comprehensive analysis systematically examines the full spectrum of FL architectures, deployment strategies, communication protocols, and aggregation methods specifically tailored for intrusion detection. We provide an in-depth investigation of privacy-preserving techniques, model compression approaches, and attack-specific federated solutions for threats including DDoS, MITM, and botnet attacks. The survey further delivers a pioneering exploration of Quantum FL (QFL), discussing quantum feature encoding, quantum machine learning algorithms, and quantum-specific aggregation methods that promise exponential speedups for complex pattern recognition in network traffic. Through rigorous comparative analysis of classical and quantum approaches, identification of research gaps, and evaluation of real-world deployments, we outline a concrete roadmap for industrial adoption and future research directions. This work serves as an authoritative reference for researchers and practitioners seeking to enhance privacy, efficiency, and robustness of federated intrusion detection systems in increasingly complex network environments, while preparing for the quantum-enhanced cybersecurity landscape of tomorrow.

cs.CR↗

arXiv2024

Robust Zero Trust Architecture: Joint Blockchain based Federated learning and Anomaly Detection based Framework

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 blockchain-based federated learning principles, our proposed framework includes a robust aggregation mechanism designed to counteract malicious updates from compromised clients, enhancing the security of the global learning process. Moreover, secure and reliable trust computation is essential for remote work and collaboration. The robust ZTA framework integrates anomaly detection and trust computation, ensuring secure and reliable device collaboration in a decentralized fashion. We introduce an adaptive algorithm that dynamically adjusts to varying user contexts, using unsupervised clustering to detect novel anomalies, like zero-day attacks. To ensure a reliable and scalable trust computation, we develop an algorithm that dynamically adapts to varying user contexts by employing incremental anomaly detection and clustering techniques to identify and share local and global anomalies between nodes. Future directions include scalability improvements, Dirichlet process for advanced anomaly detection, privacy-preserving techniques, and the integration of post-quantum cryptographic methods to safeguard against emerging quantum threats.

cs.CR↗

arXiv2024

Risk-Constrained Community Battery Utilisation Optimisation for Electric Vehicle Charging with Photovoltaic Resources

High penetration of renewable generation in the electricity grid presents power system operators with challenges including voltage instability mainly due to fluctuating power generation. To cope with intermittent generation, community batteries introduce an elegant solution for storing excess generation of renewable resources and reverting to the grid in peak demand periods. The question of the right battery type and size coupled with the right investment is challenging. Furthermore, the growth in adapting EVs imposes additional demand challenges on the power system compared to traditional industrial and household demand. This paper introduces long-term planning for community batteries to capture the surplus generation of PV resources for a given area and redirect these resources to charge EVs, without direct injection to the grid. For long-term investment planning on batteries, we consider 15 years' worth of historical data associated with solar irradiance, temperature, EV demands, and household demands. A novel stochastic mathematical model is proposed for decision-making on battery specifications (the type and capacity per year) based on the four standard battery types provided by the CSIRO in Australia. Uncertainties related to the EVs and RESs are captured by a non-parametric robust technique, named information gap decision theory, from optimistic and pessimistic perspectives. The investment decision-making part is formulated as mixed-integer linear programming taking advantage of the powerful commercial solver -- GUROBI -- which leads to finding feasible global solutions with low computational burden. The outcomes of this investigation not only detect optimal battery installation strategies to improve the stability profile of the grid by capturing the excess generation of PV resources but also facilitate EV integration in the community toward reaching net-zero emissions targets.

math.OC↗

arXiv2022

Double Attention-based Lightweight Network for Plant Pest Recognition

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 demand high computational capability and require large labelled samples for each pest type for training. On the other hand, the existing lightweight network-based approaches suffer in correctly classifying the pests because of common characteristics and high similarity between multiple plant pests. In this work, a novel double attention-based lightweight deep learning architecture is proposed to automatically recognize different plant pests. The lightweight network facilitates faster and small data training while the double attention module increases performance by focusing on the most pertinent information. The proposed approach achieves 96.61%, 99.08% and 91.60% on three variants of two publicly available datasets with 5869, 545 and 500 samples, respectively. Moreover, the comparison results reveal that the proposed approach outperforms existing approaches on both small and large datasets consistently.

cs.CV↗

arXiv2021

ECG-Adv-GAN: Detecting ECG Adversarial Examples with Conditional Generative Adversarial Networks

Electrocardiogram (ECG) acquisition requires an automated system and analysis pipeline for understanding specific rhythm irregularities. Deep neural networks have become a popular technique for tracing ECG signals, outperforming human experts. Despite this, convolutional neural networks are susceptible to adversarial examples that can misclassify ECG signals and decrease the model's precision. Moreover, they do not generalize well on the out-of-distribution dataset. The GAN architecture has been employed in recent works to synthesize adversarial ECG signals to increase existing training data. However, they use a disjointed CNN-based classification architecture to detect arrhythmia. Till now, no versatile architecture has been proposed that can detect adversarial examples and classify arrhythmia simultaneously. To alleviate this, we propose a novel Conditional Generative Adversarial Network to simultaneously generate ECG signals for different categories and detect cardiac abnormalities. Moreover, the model is conditioned on class-specific ECG signals to synthesize realistic adversarial examples. Consequently, we compare our architecture and show how it outperforms other classification models in normal/abnormal ECG signal detection by benchmarking real world and adversarial signals.

cs.LG↗

arXiv2021

Effect of stress on cardiorespiratory synchronization of Ironmen athletes

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 respiration were measured in 14 athletes before and after the Ironmen competition. Stroop test was applied between the measurements before and after the Ironmen competition to induce cognitive stress. Synchrogram and empirical mode decomposition analysis were used for the first time to investigate the effects of physical stress, induced by the Ironmen competition, on the phase synchronization of the cardiac and respiratory systems of Ironmen athletes before and after the competition. A cognitive stress task (Stroop test) was performed both pre- and post-Ironman event in order to prevent the athletes from cognitively controlling their breathing rates. Our analysis showed that cardiorespiratory synchronization increased post-Ironman race compared to pre-Ironman. The results suggest that the amount of stress the athletes are recovering from post-competition is greater than the effects of the Stroop test. This indicates that the recovery phase after the competition is more important for restoring and maintaining homeostasis, which could be another reason for stronger synchronization.

q-bio.QM↗

arXiv2019

A Scalable Framework for Trajectory Prediction

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 planning, traffic management, and early warning systems. In the last few years, the widespread use of GPS navigation systems and wireless communication technology enabled vehicles has resulted in huge volumes of trajectory data. The task of utilizing this data employing spatio-temporal techniques for trajectory prediction in an efficient and accurate manner is an ongoing research problem. Existing TP approaches are limited to short-term predictions. Moreover, they cannot handle a large volume of trajectory data for long-term prediction. To address these limitations, we propose a scalable clustering and Markov chain based hybrid framework, called Traj-clusiVAT-based TP, for both short-term and long-term trajectory prediction, which can handle a large number of overlapping trajectories in a dense road network. Traj-clusiVAT can also determine the number of clusters, which represent different movement behaviours in input trajectory data. In our experiments, we compare our proposed approach with a mixed Markov model (MMM)-based scheme, and a trajectory clustering, NETSCAN-based TP method for both short- and long-term trajectory predictions. We performed our experiments on two real, vehicle trajectory datasets, including a large-scale trajectory dataset consisting of 3.28 million trajectories obtained from 15,061 taxis in Singapore over a period of one month. Experimental results on two real trajectory datasets show that our proposed approach outperforms the existing approaches in terms of both short- and long-term prediction performances, based on prediction accuracy and distance error (in km).

cs.AI↗

arXiv2018

Scalable Bottom-up Subspace Clustering using FP-Trees for High Dimensional Data

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, such as analysing high dimensional sensor data or DNA sequences. However, existing algorithms have limitations in finding clusters in non-disjoint subspaces and scaling to large data, which impinge their applicability in areas such as bioinformatics and the Internet of Things. We aim to address such limitations by proposing a subspace clustering algorithm using a bottom-up strategy. Our algorithm first searches for base clusters in low dimensional subspaces. It then forms clusters in higher-dimensional subspaces using these base clusters, which we formulate as a frequent pattern mining problem. This formulation enables efficient search for clusters in higher-dimensional subspaces, which is done using FP-trees. The proposed algorithm is evaluated against traditional bottom-up clustering algorithms and state-of-the-art subspace clustering algorithms. The experimental results show that the proposed algorithm produces clusters with high accuracy, and scales well to large volumes of data. We also demonstrate the algorithm's performance using real-life data, including ten genomic datasets and a car parking occupancy dataset.

cs.LG↗