  Circuit obfuscation is a recently proposed defense mechanism to protect
digital integrated circuits (ICs) from reverse engineering by using camouflaged
gates i.e., logic gates whose functionality cannot be precisely determined by
the attacker. There have been effective schemes such as satisfiability-checking
(SAT)-based attacks that can potentially decrypt obfuscated circuits, called
deobfuscation. Deobfuscation runtime could have a large span ranging from few
milliseconds to thousands of years or more, depending on the number and layouts
of the ICs and camouflaged gates. And hence accurately pre-estimating the
deobfuscation runtime is highly crucial for the defenders to maximize it and
optimize their defense. However, estimating the deobfuscation runtime is a
challenging task due to 1) the complexity and heterogeneity of graph-structured
circuit, 2) the unknown and sophisticated mechanisms of the attackers for
deobfuscation. To address the above mentioned challenges, this work proposes
the first machine-learning framework that predicts the deobfuscation runtime
based on graph deep learning techniques. Specifically, we design a new model,
ICNet with new input and convolution layers to characterize and extract graph
frequencies from ICs, which are then integrated by heterogeneous deep
fully-connected layers to obtain final output. ICNet is an end-to-end framework
which can automatically extract the determinant features for deobfuscation
runtime. Extensive experiments demonstrate its effectiveness and efficiency.
