# Deep RNN-Oriented Paradigm Shift through BOCANet: Broken Obfuscated Circuit Attack

arXiv: https://arxiv.org/abs/1803.03332 · HTML: https://mlsanity.com/paper/1803.03332/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2018.

## Built on by (most cited first, 2 of 2)

- [Deceptive Logic Locking for Hardware Integrity Protection against Machine Learning Attacks](https://mlsanity.com/paper/2107.08695/) (2021)
- [Logic Locking at the Frontiers of Machine Learning: A Survey on Developments and Opportunities](https://mlsanity.com/paper/2107.01915/) (2021)

## Abstract (arXiv)

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.