Institution profile

Tokyo University of Agriculture and Technology

Academic institutionasia · jp
Official website
Research library54linked papers
Opportunities0open roles
Selected work

Representative Papers

External Sinkhole Attack Detection in Large-Scale WSNs Using Metaheuristic Feature Selection

Aug 15, 2026

This study addresses the threat of external blackhole attacks in large-scale wireless sensor networks by proposing a metaheuristic feature selection method based on swarm intelligence optimization. The approach achieves efficient dimensionality reduction and attack detection through intelligent search mechanisms, effectively overcoming performance bottlenecks associated with high-dimensional data. Experimental evaluations in a 2,000-node simulation environment demonstrate that the model reduces the feature set from 16 to 8 dimensions while maintaining a detection accuracy of 0.997. These results significantly enhance WSN security efficacy and validate the innovative application of metaheuristic algorithms in cybersecurity feature engineering, offering a robust solution for intrusion detection in resource-constrained network environments.

0 citationsRead paper

OmniQEC: discovering practical quantum error-correcting codes by an AI scientist

Jul 28, 2026

Designing quantum error-correcting codes entails intricate trade-offs among code structure, hardware constraints, and decoding performance, making it challenging to achieve both efficiency and practicality. This work proposes OmniQEC, an AI-scientist-driven iterative discovery framework that uniquely integrates self-evolving reasoning with a fast-slow collaborative workflow: a fast loop employs low-cost code-level proxies to efficiently screen candidate codes, while a slow loop conducts physically realistic circuit-level simulations for fine-grained evaluation. Orchestrated by a large language model, the framework jointly optimizes code construction, syndrome extraction synthesis, and end-to-end decoder design. Under physical qubit budgets of 98 and 240, the discovered codes outperform canonical Bacon–Bravyi (BB) codes [72,12,6] and [144,12,12], respectively, demonstrating enhanced logical error suppression and hardware compatibility that scale favorably with available resources.

0 citationsRead paper
Recent publications

Latest Papers

External Sinkhole Attack Detection in Large-Scale WSNs Using Metaheuristic Feature Selection

Aug 15, 2026

This study addresses the threat of external blackhole attacks in large-scale wireless sensor networks by proposing a metaheuristic feature selection method based on swarm intelligence optimization. The approach achieves efficient dimensionality reduction and attack detection through intelligent search mechanisms, effectively overcoming performance bottlenecks associated with high-dimensional data. Experimental evaluations in a 2,000-node simulation environment demonstrate that the model reduces the feature set from 16 to 8 dimensions while maintaining a detection accuracy of 0.997. These results significantly enhance WSN security efficacy and validate the innovative application of metaheuristic algorithms in cybersecurity feature engineering, offering a robust solution for intrusion detection in resource-constrained network environments.

0 citationsRead paper

OmniQEC: discovering practical quantum error-correcting codes by an AI scientist

Jul 28, 2026

Designing quantum error-correcting codes entails intricate trade-offs among code structure, hardware constraints, and decoding performance, making it challenging to achieve both efficiency and practicality. This work proposes OmniQEC, an AI-scientist-driven iterative discovery framework that uniquely integrates self-evolving reasoning with a fast-slow collaborative workflow: a fast loop employs low-cost code-level proxies to efficiently screen candidate codes, while a slow loop conducts physically realistic circuit-level simulations for fine-grained evaluation. Orchestrated by a large language model, the framework jointly optimizes code construction, syndrome extraction synthesis, and end-to-end decoder design. Under physical qubit budgets of 98 and 240, the discovered codes outperform canonical Bacon–Bravyi (BB) codes [72,12,6] and [144,12,12], respectively, demonstrating enhanced logical error suppression and hardware compatibility that scale favorably with available resources.

0 citationsRead paper