  This is the first work augmenting hardware attacks mounted on obfuscated
circuits by incorporating deep recurrent neural network (D-RNN). Logic
encryption obfuscation has been used for thwarting counterfeiting,
overproduction, and reverse engineering but vulnerable to attacks. There have
been efficient schemes, e.g., satisfiability-checking (SAT) based attack, which
can potentially compromise hardware obfuscation circuits. Nevertheless, not
only there exist countermeasures against such attacks in the state-of-the-art
(including the recent delay+logic locking (DLL) scheme in DAC'17), but the
sheer amount of time/resources to mount the attack could hinder its efficacy.
In this paper, we propose a deep RNN-oriented approach, called BOCANet, to (i)
compromise the obfuscated hardware at least an order-of magnitude more
efficiently (>20X faster with relatively high success rate) compared to
existing attacks; (ii) attack such locked hardware even when the resources to
the attacker are only limited to insignificant number of I/O pairs (< 0.5\%) to
reconstruct the secret key; and (iii) break a number of experimented benchmarks
(ISCAS-85 c423, c1355, c1908, and c7552) successfully.
