[2603.22365] Q-AGNN: Quantum-Enhanced Attentive Graph Neural Network for Intrusion Detection

Computer Science > Cryptography and Security arXiv:2603.22365 (cs) [Submitted on 23 Mar 2026] Title:Q-AGNN: Quantum-Enhanced Attentive Graph Neural Network for Intrusion Detection Authors:Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj Pokhrel View a PDF of the paper titled Q-AGNN: Quantum-Enhanced Attentive Graph Neural Network for Intrusion Detection, by Devashish Chaudhary and 2 other authors View PDF HTML (experimental) Abstract: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 ...