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

TL;DR

The paper tackles security challenges in IoT audio-classification devices that process sensitive ambient data under tight resource constraints. It proposes a defence-in-depth architecture that integrates TPM-based remote attestation, mutually authenticated TLS 1.3, and post-quantum cryptography to secure edge-to-cloud communications and data at rest. Key contributions include threat-to-control traceability via STRIDE and attack trees, an attestation-gated data-at-rest pattern, a minimal mTLS control plane, a PQC hybrid upgrade path, and a comprehensive evaluation plan encompassing static analysis, penetration testing, and formal audits. The proposed framework emphasizes end-to-end privacy and integrity, supported by secure boot, signed ML updates with rollback, robust certificate management, and physical tamper detection, aiming to enable practical, scalable deployment of privacy-preserving edge audio processing.

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.

Figures (5)

• Figure 1: Overview of the IoT audio classification system architecture.
• Figure 2: Taxonomy of IoT Audio Security. The diagram categorizes IoT audio security strategies into three main areas: Secure Communication Protocols (green), Privacy-Preserving Techniques (purple), and Attack Surface Analysis & Threat Modeling (grey). Each branch presents representative methods or references, highlighting the layered approach needed for secure IoT audio systems.
• Figure 3: Attack Tree for IoT Node Compromise. This diagram illustrates possible attack paths to compromise an IoT audio classification system. Each branch represents a different attack scenario, breaking down complex threats into actionable steps. The tree begins with the main goal, system compromise, and decomposes it into sub-goals such as edge device compromise (green), communication attacks (purple), and backend/API server threats (grey). Each colored block groups related attack vectors, including privilege escalation, spoofing, tampering, denial-of-service, and more specific exploits like adversarial audio injection or API impersonation.
• Figure 4: The diagram illustrates the API-driven LUKS unlocking workflow for IoT devices. The green box represents the IoT Node, where the unlocking process is initiated. The grey box highlights the Remote API for Deciphering.
• Figure 5: This diagram show the IoT architecture described for remote monitoring. Each sensing unit is housed in an IP68-certified cabinet, is powered by solar energy and a battery, and integrates electronic modules, sensors, and 4G cellular connectivity. The data collected by each station is securely transmitted via HTTPS protocols (TLS 1.3) to a central API server. This server is protected by a VPN and a web application firewall (WAF), and is used to manage, visualise and analyse the information received from multiple nodes deployed in the field.