R-DNNTEE: Remote Attestation for DNN Models Deployed on Satellite TEEs;R-DNNTEE: 衛星安全環境之深度學習遠端驗證

NCUIR > College of Electrical Engineering & Computer Science > Software Engineer > Electronic Thesis & Dissertation > Item 987654321/98029

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/98029

Title:	R-DNNTEE: Remote Attestation for DNN Models Deployed on Satellite TEEs;R-DNNTEE: 衛星安全環境之深度學習遠端驗證
Authors:	陳以晢;Chen, I-Che
Contributors:	軟體工程研究所
Keywords:	遠端驗證;LEO衛星;可信執行環境;ARM TrustZone;深度學習模型;Remote Attestaion;LEO Satellite;Trusted Execution Environment;ARM TrustZone;Deep Nerual Network Model
Date:	2025-08-04
Issue Date:	2025-10-17 12:16:09 (UTC+8)
Publisher:	國立中央大學
Abstract:	隨著即時衛星影像分析需求的快速成長，將人工智慧（AI）功能直接整合至低地球軌道（LEO）衛星平台，已成為重要的技術發展方向。傳統衛星運作流程需將影像資料下傳至地面站進行分析，不僅產生延遲，也增加通信成本，進而影響決策的時效性。為解決這些問題。本文提出一種安全且輕量的可信 AI 推論架構，專門設計於資源受限且無法實體接觸的 LEO 衛星環境中。並且整合了多項關鍵技術，包括利用以 OPTEE 為基礎的韌體 TPM（optee ftpm）建立信任根，並透過 TPM 2.0 的平台組態註冊（PCR）測量機制與隨機數質詢協定進行遠端驗證，以確保平台完整性與可信度。模型的安全傳輸則仰賴結合 TPM HMAC 的 AES 加密技術，防止在傳輸過程中遭到竄改或竊取。此外，推論過程採用分區式設計，將敏感的模型層保護於可信執行環境（TEE）中，而將其餘運算卸載至具備 GPU 加速能力的非安全區域（REE）執行，兼顧性能與安全。實驗結果顯示，該系統可在合理時間內完成完整性報告生成與模型驗證，同時透過 GPU 加速將推論延遲最高改善至 2.36 倍。在確保資料隱私與系統可信性的前提下，本文所提出的混合式架構成功實現了 AI 模型從傳輸到執行的全程保護與可驗證性，為未來具即時決策能力的衛星星座奠定了實用基礎。;With the growing demand for real-time satellite image analytics, inte- grating artificial intelligence (AI) capabilities directly into Low Earth Or- bit (LEO) platforms has become a critical technological frontier. Traditional satellite workflows rely on downlinking imagery to ground stations for analy- sis, incurring latency and communication overhead that hinder timely decision- making. This paper presents a secure and lightweight architecture for trusted AI inference on LEO satellites, addressing the unique constraints of resource- limited and physically inaccessible environments. Our system combines multiple core technologies: firmware TPM for es- tablishing a root of trust, Remote Attestation using TPM 2.0 PCR measure- ments and nonce-challenge protocols, AES encryption with TPM-HMAC for secure model delivery, and a partitioned inference framework leverag- ing GPU-accelerated REE execution and TEE-based protection for sensitive model layers. This hybrid architecture achieves end-to-end model protection and verifiable execution, laying a practical foundation for secure, real-time AI in future satellite constellations.
Appears in Collections:	[Software Engineer] Electronic Thesis & Dissertation

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