3 research outputs found
SAT-hard Cyclic Logic Obfuscation for Protecting the IP in the Manufacturing Supply Chain
State-of-the-art attacks against cyclic logic obfuscation use satisfiability
solvers that are equipped with a set of cycle avoidance clauses. These cycle
avoidance clauses are generated in a pre-processing step and define various key
combinations that could open or close cycles without making the circuit
oscillating or stateful. In this paper, we show that this pre-processing step
has to generate cycle avoidance conditions on all cycles in a netlist,
otherwise, a missing cycle could trap the solver in an infinite loop or make it
exit with an incorrect key. Then, we propose several techniques by which the
number of cycles is exponentially increased as a function of the number of
inserted feedbacks. We further illustrate that when the number of feedbacks is
increased, the pre-processing step of the attack faces an exponential increase
in complexity and runtime, preventing the correct composition of cycle
avoidance clauses in a reasonable time. On the other hand, if the
pre-processing is not concluded, the attack formulated by the satisfiability
solver will either get stuck or exit with an incorrect key. Hence, when the
cyclic obfuscation under the conditions proposed in this paper is implemented,
it would impose an exponentially difficult problem for the satisfiability
solver based attacks.Comment: arXiv admin note: substantial text overlap with arXiv:1804.0916
InterLock: An Intercorrelated Logic and Routing Locking
In this paper, we propose a canonical prune-and-SAT (CP&SAT) attack for
breaking state-of-the-art routing-based obfuscation techniques. In the CP&SAT
attack, we first encode the key-programmable routing blocks (keyRBs) based on
an efficient SAT encoding mechanism suited for detailed routing constraints,
and then efficiently re-encode and reduce the CNF corresponded to the keyRB
using a bounded variable addition (BVA) algorithm. In the CP&SAT attack, this
is done before subjecting the circuit to the SAT attack. We illustrate that
this encoding and BVA-based pre-processing significantly reduces the size of
the CNF corresponded to the routing-based obfuscated circuit, in the result of
which we observe 100% success rate for breaking prior art routing-based
obfuscation techniques. Further, we propose a new intercorrelated logic and
routing locking technique, or in short InterLock, as a countermeasure to
mitigate the CP&SAT attack. In Interlock, in addition to hiding the
connectivity, a part of the logic (gates) in the selected timing paths are also
implemented in the keyRB(s). We illustrate that when the logic gates are
twisted with keyRBs, the BVA could not provide any advantage as a
pre-processing step. Our experimental results show that, by using InterLock,
with only three 88 or only two 16x16 keyRBs (twisted with actual logic
gates), the resilience against existing attacks as well as our new proposed
CP&SAT attack would be guaranteed while, on average, the delay/area overhead is
less than 10% for even medium-size benchmark circuits
NNgSAT: Neural Network guided SAT Attack on Logic Locked Complex Structures
The globalization of the IC supply chain has raised many security threats,
especially when untrusted parties are involved. This has created a demand for a
dependable logic obfuscation solution to combat these threats. Amongst a wide
range of threats and countermeasures on logic obfuscation in the 2010s decade,
the Boolean satisfiability (SAT) attack, or one of its derivatives, could break
almost all state-of-the-art logic obfuscation countermeasures. However, in some
cases, particularly when the logic locked circuits contain complex structures,
such as big multipliers, large routing networks, or big tree structures, the
logic locked circuit is hard-to-be-solved for the SAT attack. Usage of these
structures for obfuscation may lead a strong defense, as many SAT solvers fail
to handle such complexity. However, in this paper, we propose a
neural-network-guided SAT attack (NNgSAT), in which we examine the capability
and effectiveness of a message-passing neural network (MPNN) for solving these
complex structures (SAT-hard instances). In NNgSAT, after being trained as a
classifier to predict SAT/UNSAT on a SAT problem (NN serves as a SAT solver),
the neural network is used to guide/help the actual SAT solver for finding the
SAT assignment(s). By training NN on conjunctive normal forms (CNFs)
corresponded to a dataset of logic locked circuits, as well as fine-tuning the
confidence rate of the NN prediction, our experiments show that NNgSAT could
solve 93.5% of the logic locked circuits containing complex structures within a
reasonable time, while the existing SAT attack cannot proceed the attack flow
in them