Fatemeh Arkannezhad (UCLA), Justin Feng (UCLA), Nader Sehatbakhsh (UCLA)

Remote attestation has received much attention recently due to the proliferation of embedded and IoT devices. Among various solutions, methods based on hardware-software co-design (hybrid) are particularly popular due to their low overhead yet effective approaches. Despite their usefulness, hybrid methods still suffer from multiple limitations such as strict protections required for the attestation keys and restrictive operation and threat models such as disabling interrupts and neglecting time-of-check-time-of-use (TOCTOU) attacks.

In this paper, we propose a new hybrid attestation method called IDA, which removes the requirement for disabling interrupts and restrictive access control for the secret key and attestation code, thus improving the system's overall security and flexibility. Rather than making use of a secret key to calculate the response, IDA verifies the attestation process with trusted hardware monitoring and certifies its authenticity only if it was followed precisely. Further, to prevent TOCTOU attacks and handle interrupts, we propose IDA+, which monitors program memory between attestation requests or during interrupts and informs the verifier of changes to the program memory. We implement and evaluate IDA and IDA+ on open-source MSP430 architecture, showing a reasonable overhead in terms of runtime, memory footprint, and hardware overhead while being robust against various attack scenarios. Comparing our method with the state-of-the-art, we show that it has minimal overhead while achieving important new properties such as support for interrupts and DMA requests and detecting TOCTOU attacks.

View More Papers

coucouArray ( [post_type] => ndss-paper [post_status] => publish [posts_per_page] => 4 [orderby] => rand [tax_query] => Array ( [0] => Array ( [taxonomy] => category [field] => id [terms] => Array ( [0] => 104 ) ) ) [post__not_in] => Array ( [0] => 16826 ) )

Facilitating Non-Intrusive In-Vivo Firmware Testing with Stateless Instrumentation

Jiameng Shi (University of Georgia), Wenqiang Li (Independent Researcher), Wenwen Wang (University of Georgia), Le Guan (University of Georgia)

Read More

ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning

Linkang Du (Zhejiang University), Min Chen (CISPA Helmholtz Center for Information Security), Mingyang Sun (Zhejiang University), Shouling Ji (Zhejiang University), Peng Cheng (Zhejiang University), Jiming Chen (Zhejiang University), Zhikun Zhang (CISPA Helmholtz Center for Information Security and Stanford University)

Read More

Low-Quality Training Data Only? A Robust Framework for Detecting...

Yuqi Qing (Tsinghua University), Qilei Yin (Zhongguancun Laboratory), Xinhao Deng (Tsinghua University), Yihao Chen (Tsinghua University), Zhuotao Liu (Tsinghua University), Kun Sun (George Mason University), Ke Xu (Tsinghua University), Jia Zhang (Tsinghua University), Qi Li (Tsinghua University)

Read More

Attributions for ML-based ICS Anomaly Detection: From Theory to...

Clement Fung (Carnegie Mellon University), Eric Zeng (Carnegie Mellon University), Lujo Bauer (Carnegie Mellon University)

Read More

Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)