1 research outputs found
Challenging the Security of Logic Locking Schemes in the Era of Deep Learning: A Neuroevolutionary Approach
Logic locking is a prominent technique to protect the integrity of hardware
designs throughout the integrated circuit design and fabrication flow. However,
in recent years, the security of locking schemes has been thoroughly challenged
by the introduction of various deobfuscation attacks. As in most research
branches, deep learning is being introduced in the domain of logic locking as
well. Therefore, in this paper we present SnapShot: a novel attack on logic
locking that is the first of its kind to utilize artificial neural networks to
directly predict a key bit value from a locked synthesized gate-level netlist
without using a golden reference. Hereby, the attack uses a simpler yet more
flexible learning model compared to existing work. Two different approaches are
evaluated. The first approach is based on a simple feedforward fully connected
neural network. The second approach utilizes genetic algorithms to evolve more
complex convolutional neural network architectures specialized for the given
task. The attack flow offers a generic and customizable framework for attacking
locking schemes using machine learning techniques. We perform an extensive
evaluation of SnapShot for two realistic attack scenarios, comprising both
reference benchmark circuits as well as silicon-proven RISC-V core modules. The
evaluation results show that SnapShot achieves an average key prediction
accuracy of 82.60% for the selected attack scenario, with a significant
performance increase of 10.49 percentage points compared to the state of the
art. Moreover, SnapShot outperforms the existing technique on all evaluated
benchmarks. The results indicate that the security foundation of common logic
locking schemes is build on questionable assumptions. The conclusions of the
evaluation offer insights into the challenges of designing future logic locking
schemes that are resilient to machine learning attacks.Comment: 25 pages, 17 figures, accepted at ACM JET