AttackDefense Framework (ADF): Enhancing IoT Devices and Lifecycles Threat Modeling

Threat modeling (TM) is essential to manage, prevent, and ix security and privacy issues in our society. TM requires a data model to represent threats and tools to exploit such data. Current TM data models and tools have signiicant limitations preventing their usage in real-world scenarios. For example, it is challenging to TM embedded devices with current data models and tools as they cannot model their hardware, irmware, and low-level software. Moreover, it is impossible to TM a device lifecycle or security-privacy tradeofs as these data models and tools were developed for other use cases (e.g., software security or user privacy).

We ill this relevant gap by presenting the AttackDefense Framework (ADF), which provides a novel data model and related tools to augment TM. ADF's building block is the AD object that can be used to represent heterogeneous and complex threats. Moreover, ADF provides automations to process a collection of AD objects, including ways to create sets, maps, chains, trees, and wordclouds of AD objects. We present ADF, a toolkit implementing ADF composed of four modules (Catalog, Parse, Check, and Analyze).

We conirm that the data model and tools provided by ADF are useful by running an extensive set of experiments while threat modeling a crypto wallet and its lifecycle. Our experiments involved seven expert groups from academia and industry, each using the ADF on an orthogonal threat class. The evaluation generated 175 high-quality ADs covering ISA/IEC 62433-4-1 SecDev Lifecycle, side-channels, fault injection, microarchitectural attacks, speculative execution, pre-silicon testing, invasive physical chip modiications, Bluetooth protocol and implementation threats, and FIDO2 authentication.

Mots clés

Hardware attacks and countermeasures Tamper-proof and tamper-resistant designs Threat Modeling Embedded Systems Security Hardware Security

Domaines

Cryptographie et sécurité [cs.CR]

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3698396.pdf (728.52 Ko)

Origine	Fichiers produits par l'(les) auteur(s)

Daniele Antonioli : Connectez-vous pour contacter le contributeur

https://hal.science/hal-04735344

Soumis le : lundi 14 octobre 2024-14:41:43

Dernière modification le : jeudi 17 octobre 2024-09:25:36

Dates et versions

hal-04735344 , version 1 (14-10-2024)

Identifiants

HAL Id : hal-04735344 , version 1
DOI : 10.1145/3698396

Citer

Tommaso Sacchetti, Marton Bognar, Jesse de Meulemeester, Benedikt Gierlichs, Frank Piessens, et al.. AttackDefense Framework (ADF): Enhancing IoT Devices and Lifecycles Threat Modeling. ACM Transactions on Embedded Computing Systems (TECS), 2024, ⟨10.1145/3698396⟩. ⟨hal-04735344⟩

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