Enhancing Cybersecurity in Smart High-Performance Computing Systems Using Graph-Temporal Deep Learning and Hybrid Optimization

Abstract

High-Performance Computing (HPC) systems have become integral to scientific discovery and Artificial Intelligence (AI), yet their complex, distributed architectures expose them to sophisticated cyber threats capable of disrupting large-scale computational workflows. Traditional intrusion detection systems (IDS) lack the adaptability and contextual understanding required to secure such dynamic environments. This study presents an advanced cybersecurity framework that leverages a Graph-Temporal Transformer Network (GTTN) combined with a Hybrid Adaptive Meta-Optimization (HAMO) algorithm to enhance anomaly detection and classification in HPC infrastructures. The proposed framework first constructs graph representations of inter-node communications and process-level dependencies, capturing both structural and temporal relationships within HPC traffic. A self-supervised pretraining phase is employed to learn normal behavioral patterns without labeled data, improving the model’s resilience to zero-day attacks. The fine-tuned GTTN then integrates spatial attention and temporal encoding to detect deviations indicative of intrusion activities. To further optimize model performance, the HAMO algorithm, which fuses the exploration capability of the Spotted Hyena Optimizer (SHO) with the exploitation strength of the Whale Optimization Algorithm (WOA), is applied to adaptively tune the network’s hyperparameters. Experimental evaluations conducted on HPC-tailored versions of the CIC-IDS 2018 and LANL Cyber dataset reveal that the proposed system achieves detection accuracies of 0.995 and 0.982, outperforming state-of-the-art deep learning (DL) and evolutionary baselines. The framework also demonstrates reduced false alarm rates and superior scalability under high-throughput workloads. These results indicate that the integration of graph-temporal learning and hybrid meta-optimization provides a powerful and adaptive defense strategy for next-generation HPC cybersecurity, offering practical potential for deployment in exascale computing environments.