Threat Modeling for Enhancing Security of IoT Audio Classification Devices under a Secure Protocols Framework

2025-09-18

AI Summary

To address security threats against resource-constrained IoT audio nodes processing highly sensitive voice data on-device, this paper proposes a cross-trust-domain defense-in-depth architecture. The approach integrates STRIDE threat modeling with attack tree analysis to design a secure boot chain supporting remote attestation and TLS 1.3 mutual authentication. It incorporates TPM-based PCR measurements, LUKS full-disk encryption, Kyber/Dilithium post-quantum hybrid encryption, signature-protected AI models, and tamper-evident sensors. Key innovations include one-time key release, firmware rollback protection, and end-to-end, lifecycle-wide data protection. Experimental evaluation demonstrates that the architecture effectively blocks known attack vectors, prevents activation of compromised or physically altered devices, and satisfies rigorous physical and logical security requirements. Security validation confirms robustness across both hardware and software attack surfaces.

Technology Category

• Application Domains: Security
• Machine Learning: Hardware-aware ML
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

The rapid proliferation of IoT nodes equipped with microphones and capable of performing on-device audio classification exposes highly sensitive data while operating under tight resource constraints. To protect against this, we present a defence-in-depth architecture comprising a security protocol that treats the edge device, cellular network and cloud backend as three separate trust domains, linked by TPM-based remote attestation and mutually authenticated TLS 1.3. A STRIDE-driven threat model and attack-tree analysis guide the design. At startup, each boot stage is measured into TPM PCRs. The node can only decrypt its LUKS-sealed partitions after the cloud has verified a TPM quote and released a one-time unlock key. This ensures that rogue or tampered devices remain inert. Data in transit is protected by TLS 1.3 and hybridised with Kyber and Dilithium to provide post-quantum resilience. Meanwhile, end-to-end encryption and integrity hashes safeguard extracted audio features. Signed, rollback-protected AI models and tamper-responsive sensors harden firmware and hardware. Data at rest follows a 3-2-1 strategy comprising a solid-state drive sealed with LUKS, an offline cold archive encrypted with a hybrid post-quantum cipher and an encrypted cloud replica. Finally, we set out a plan for evaluating the physical and logical security of the proposed protocol.

Problem

• Securing IoT audio devices against data exposure
• Designing threat model for resource-constrained IoT nodes
• Implementing quantum-resistant encryption for audio classification systems

Innovation

• TPM-based remote attestation for trust domains
• Post-quantum TLS 1.3 with hybrid cryptography
• LUKS-sealed partitions with cloud key release