ALMOST: Adversarial Learning to Mitigate Oracle-less ML Attacks via Synthesis Tuning

no code implementations • 6 Mar 2023 • Animesh Basak Chowdhury, Lilas Alrahis, Luca Collini, Johann Knechtel, Ramesh Karri, Siddharth Garg, Ozgur Sinanoglu, Benjamin Tan

Oracle-less machine learning (ML) attacks have broken various logic locking schemes.

Graph Neural Networks: A Powerful and Versatile Tool for Advancing Design, Reliability, and Security of ICs

1 code implementation • 29 Nov 2022 • Lilas Alrahis, Johann Knechtel, Ozgur Sinanoglu

Graph neural networks (GNNs) have pushed the state-of-the-art (SOTA) for performance in learning and predicting on large-scale data present in social networks, biology, etc.

Security Closure of IC Layouts Against Hardware Trojans

no code implementations • 15 Nov 2022 • Fangzhou Wang, Qijing Wang, Bangqi Fu, Shui Jiang, Xiaopeng Zhang, Lilas Alrahis, Ozgur Sinanoglu, Johann Knechtel, Tsung-Yi Ho, Evangeline F. Y. Young

In this work, we proactively and systematically harden the physical layouts of ICs against post-design insertion of Trojans.

GNN4REL: Graph Neural Networks for Predicting Circuit Reliability Degradation

1 code implementation • 4 Aug 2022 • Lilas Alrahis, Johann Knechtel, Florian Klemme, Hussam Amrouch, Ozgur Sinanoglu

Without a precise understanding of the impact of variations on the delay of circuit paths, guardbands, which keep timing violations at bay, cannot be correctly estimated.

NeuroUnlock: Unlocking the Architecture of Obfuscated Deep Neural Networks

2 code implementations • 1 Jun 2022 • Mahya Morid Ahmadi, Lilas Alrahis, Alessio Colucci, Ozgur Sinanoglu, Muhammad Shafique

We release the NeuroUnlock and the ReDLock as open-source frameworks.

Adversarial Attack