CARBON-NEUTRAL CYBERSECURITY CIRCUITS USING OSV-BISGA FOR SUSTAINABLE INDUSTRIAL APPLICATIONS
持続可能な産業応用のためのOSV-BiSGAを用いたカーボンニュートラルサイバーセキュリティ回路 (AI 翻訳)
Thomas Samraj Lawrence
🤖 gxceed AI 要約
日本語
本論文は、産業用サイバーフィジカルシステム向けに、カーボンニュートラルを目指した省エネルギーなセキュリティ回路を提案する。提案OSV-BiSGAフレームワークは、One-Class SVMと双方向スノーガチョウ最適化を組み合わせ、動的サンプリングと低リーク部品により消費電力を63%削減しつつ、精度0.95を達成する。
English
This paper proposes an energy-efficient cybersecurity circuit framework, OSV-BiSGA, for industrial cyber-physical systems targeting carbon neutrality. Combining one-class SVM and bidirectional snow geese optimization, it reduces energy consumption by 63% while maintaining high detection accuracy (0.95).
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の産業界では、工場のサイバーセキュリティと省エネ両立が課題。本手法は、PLCや組み込み機器のグリーン設計に応用可能で、SSBJ開示におけるエネルギー効率指標の向上に寄与しうる。
In the global GX context
Globally, aligning cybersecurity with energy efficiency is key for sustainable Industry 4.0. This work offers a circuit-level solution that reduces operational energy, supporting net-zero goals in industrial infrastructure.
👥 読者別の含意
🔬研究者:Circuit designers and cybersecurity researchers can leverage the OSV-BiSGA optimization for low-power intrusion detection.
🏢実務担当者:Industrial system integrators may adopt this framework to reduce energy costs in secure control systems.
🏛政策担当者:Regulators promoting green ICT standards could cite this as an example of energy-aware security design.
📄 Abstract（原文）
Industrial cyber–physical systems increasingly have relied on secure circuits that also have aligned with carbon neutral goals. Prior studies have emphasized detection accuracy, yet the energy footprint of security algorithms has remained marginally addressed. The need for energy-aware cybersecurity circuits has therefore emerged as a critical research direction. Conventional intrusion detection circuits have consumed excessive power due to continuous monitoring that has depended on computationally intensive learning models. These approaches have limited suitability for green industrial infrastructure, where both security assurance and energy efficiency have been demanded simultaneously. A lack of unified frameworks that have integrated low-power algorithms with sustainable circuit deployment has persisted. A novel energy-efficient framework that has been termed OSV-BiSGA has been proposed for industrial cybersecurity circuits. The framework has combined a one-class support vector model with a bidirectional snow geese optimization algorithm that has minimized redundant computations. Continuous monitoring that has been designed at the circuit level has adapted sampling rates dynamically, which has reduced idle power consumption. Green infrastructure principles that have included low-leakage components and adaptive voltage scaling have been incorporated into the circuit design. Optimization that has guided parameter selection has ensured minimal energy usage without degrading detection reliability. The proposed OSV-BiSGA framework achieves an accuracy of 0.95, precision of 0.93, recall of 0.94, and F1-score of 0.94 at a population size of 30, while reducing energy consumption to 40 J. Compared with Static One Class SVM, PSO-Optimized IDS, and Always-On Deep Learning IDS, the framework reduces energy usage by up to 63% while maintaining superior detection performance.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.21917/ijme.2026.0377first seen 2026-07-16 06:26:11
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