Genetic Algorithm-based Electromagnetic Fault Injection
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1 Genetic Algorithm-based Electromagnetic Fault Injection Antun Maldini Niels Samwel Stjepan Picek Lejla Batina Institute for Computing and Information Sciences Digital Security FDTC Antun Maldini GA-based EMFI 1 / 30
2 Outline Antun Maldini GA-based EMFI 2 / 30
3 Fault Injection (FI) supply voltage glitching, clock glitching, EM pulse, laser pulse on SHA-3 (Keccak) but generic which parameters to use? optimization algorithm Antun Maldini GA-based EMFI 3 / 30
4 Idea What we set out to do make an algorithm for parameter optimization use it on SHA-3 (Keccak) make it better than what s previously been done Antun Maldini GA-based EMFI 4 / 30
5 Contribution What we did made an EA for parameter optimization! attacked SHA-3 it s better than the baseline! (and previous results) Antun Maldini GA-based EMFI 5 / 30
6 What are we optimizing? Parameters X, Y the two spatial dimensions offset w.r.t. the trigger intensity power of the EM pulse No. of repetitions a primitive form of pulse shape These are the ones we can control with the equipment we have. Antun Maldini GA-based EMFI 6 / 30
7 Why are we optimizing? most parameter settings don t result in FI exhaustive search impractical Exhaustive search really exhaustive points, 30 years even just spatial, 20 intensity, 100 offset 37 days Antun Maldini GA-based EMFI 7 / 30
8 Related work very little work on FI parameter optimization Madau & al. EMFI susceptibility criterion all surface points ranked by this criterion, reject worst α% reject 50% of chip surface, with 80% faults kept by fault they mean any abnormal behavior Carpi & al. supply voltage glitching two stages: a 2D search, followed by a 1D grid search genetic, later memetic algorithm Antun Maldini GA-based EMFI 8 / 30
9 Experimental setup Device tested: Cortex-M4F on STMicroelectronics board Antun Maldini GA-based EMFI 9 / 30
10 Experimental setup Device tested: Cortex-M4F on STMicroelectronics board Code running: SHA3-512 (WolfSSL implementation, in C) Antun Maldini GA-based EMFI 9 / 30
11 Experimental setup Device tested: Cortex-M4F on STMicroelectronics board Code running: SHA3-512 (WolfSSL implementation, in C) Fault injection by: Riscure EM probe, VCGlitcher Antun Maldini GA-based EMFI 9 / 30
12 Experimental setup Device tested: Cortex-M4F on STMicroelectronics board Code running: SHA3-512 (WolfSSL implementation, in C) Fault injection by: Riscure EM probe, VCGlitcher All controlled by: Python code on PC Antun Maldini GA-based EMFI 9 / 30
13 Measuring different behaviours Some definitions point: a tuple of (X, Y, intensity, offset, #rep.) measurement: a single sampling of a point Antun Maldini GA-based EMFI 10 / 30
14 Measuring different behaviours Some definitions point: a tuple of (X, Y, intensity, offset, #rep.) measurement: a single sampling of a point Several classes of behaviour: NORMAL nothing happens RESET target locks up SUCCESS we get a faulty output of the right length Antun Maldini GA-based EMFI 10 / 30
15 Measuring different behaviours Some definitions point: a tuple of (X, Y, intensity, offset, #rep.) measurement: a single sampling of a point Several classes of behaviour: NORMAL nothing happens RESET target locks up SUCCESS we get a faulty output of the right length Behaviour is not completely determined by the point! do multiple (5) measurements per point behaviour changes CHANGING class Antun Maldini GA-based EMFI 10 / 30
16 Objectives & assumptions Objectives good coverage of the parameter space we know nothing in advance! speed Antun Maldini GA-based EMFI 11 / 30
17 Objectives & assumptions Objectives good coverage of the parameter space we know nothing in advance! speed Assumptions EM pulse too weak NORMAL class EM pulse too strong RESET class desired behaviour is somewhere in between Antun Maldini GA-based EMFI 11 / 30
18 Evolutionary algorithms population-based metaheuristic used for general, non-convex optimization problems exploration vs. exploitation Antun Maldini GA-based EMFI 12 / 30
19 Evolutionary algorithms A general outline: Input : Parameters of the algorithm Output : Optimal solution set t 0 P(0) CreateInitialPopulation while TerminationCriterion not satisfied do t t + 1 P (t) SelectMechanism (P(t 1)) P(t) VariationOperators(P (t)) end while return OptimalSolutionSet(P) Antun Maldini GA-based EMFI 13 / 30
20 Evolutionary algorithms A general outline: Input : Parameters of the algorithm Output : Optimal solution set t 0 P(0) CreateInitialPopulation while TerminationCriterion not satisfied do t t + 1 P (t) SelectMechanism (P(t 1)) P(t) VariationOperators(P (t)) end while return OptimalSolutionSet(P) Antun Maldini GA-based EMFI 13 / 30
21 Genetic algorithms A general outline: Input : Parameters of the algorithm Output : Optimal solution set t 0 P(0) CreateInitialPopulation while TerminationCriterion not satisfied do t t + 1 P (t) SelectMechanism (P(t 1)) Ch(t) Mutate(Combine(P (t))) P(t) Pick sizeof (P(t)) from (Ch(t) P(t)) end while return OptimalSolutionSet(P) Antun Maldini GA-based EMFI 14 / 30
22 Our algorithm Two phases: GA and local search Antun Maldini GA-based EMFI 15 / 30
23 Our algorithm Two phases: GA and local search GA 20 generations of 50 units each roulette-wheel selection non-standard crossover elitism (with 1 elite individual) Antun Maldini GA-based EMFI 15 / 30
24 Our algorithm Two phases: GA and local search GA 20 generations of 50 units each roulette-wheel selection non-standard crossover elitism (with 1 elite individual) LS run after the GA is done further exploit the area around the SUCCESSful points found Antun Maldini GA-based EMFI 15 / 30
25 Selection 3-tournament resulted in overly fast convergence roulette-wheel is slower, especially with large population keeping the best individual useful when good points are rare Antun Maldini GA-based EMFI 16 / 30
26 Crossover Standard crossover for each parameter p do child.p random choice(parent 1.p, parent 2.p) end for Our crossover for each parameter p do child.p random value in range [parent 1.p, parent 2.p] end for Antun Maldini GA-based EMFI 17 / 30
27 Crossover Illustrated on a 3-cube Image by Colin Burnett, CC BY-SA 3.0 Antun Maldini GA-based EMFI 18 / 30
28 Fitness function NORMAL 2 RESET 5 SUCCESS 10 CHANGING??? Antun Maldini GA-based EMFI 19 / 30
29 Fitness function NORMAL 2 RESET 5 SUCCESS 10 CHANGING we look at the 5 measurements of a point fitness CHANGING = N NORMAL N RESET N SUCCESS Antun Maldini GA-based EMFI 19 / 30
30 Local search When we re done exploring... for each SUCCESSful point P do for i from 1 to 10 do neighbour random point from neighbourhood(p) scan neighbour end for end for Neighbourhood: cube centered on P, edge length 0.02 Antun Maldini GA-based EMFI 20 / 30
31 Results all statistics are averages over 5 runs average run length of points TL;DR Random GA improvement faulty msmts. 1.3% 58.8% 42.5 times distinct faulty msmts. 1.0% 19.9% 20.5 times... as % of all individual measurements Antun Maldini GA-based EMFI 21 / 30
32 Results details whole run first 500 points Random GA Random GA NORMAL (90.7%) (18.9%) (90.5%) (63.0%) RESET 65.0 (2.0%) (15.0%) 9.8 (2.0%) 73.4 (14.7%) CHANGING (7.0%) (11.4%) 36.0 (7.2%) 79.0 (15.8%) SUCCESS 8.8 (0.3%) (54.7%) 1.6 (0.3%) 32.4 (6.5%) #faulty m (1.3%) (58.8%) 33.4 (1.3%) (10.4%) #distinct m (1.0%) (19.9%) 22.6 (0.9%) (6.3%) Antun Maldini GA-based EMFI 22 / 30
33 Exploiting faults? Antun Maldini GA-based EMFI 23 / 30
34 Exploiting faults? Can we actually use the faulty outputs we have? Antun Maldini GA-based EMFI 23 / 30
35 Exploiting faults? Can we actually use the faulty outputs we have? How? Antun Maldini GA-based EMFI 23 / 30
36 Exploiting faults? Can we actually use the faulty outputs we have? How? Is it practical? Antun Maldini GA-based EMFI 23 / 30
37 Exploiting faults? Can we actually use the faulty outputs we have? How? Is it practical? Yes. Antun Maldini GA-based EMFI 23 / 30
38 Exploiting faults? Can we actually use the faulty outputs we have? Yes. How? Use DFA or AFA. Is it practical? Antun Maldini GA-based EMFI 23 / 30
39 Exploiting faults? Can we actually use the faulty outputs we have? Yes. How? Use DFA or AFA. Is it practical? Mostly. Antun Maldini GA-based EMFI 23 / 30
40 Algebraic Fault Analysis Luo & alii, (for SHA-3) Idea: let a SAT solver do the hard work 1 represent internal state by boolean vars 2 formulate algorithm & fault model as boolean statements (this provides the propagation constraints) 3 obtain a (correct, faulty) output pair (these provide concrete constraints) enough implicit information to deduce part of state Antun Maldini GA-based EMFI 24 / 30
41 Algebraic Fault Analysis Recovering the state load into SAT solver: (correct, faulty) while more solutions exist do solution SAT.get solution() SAT.add constraint( solution) end while Solver eventually runs out of satisfiable solutions. Bits which are same in all solutions are recoverable. Antun Maldini GA-based EMFI 25 / 30
42 Algebraic Fault Analysis, specifics Luo & al. provide 3 fault models (8-bit, 16-bit, 32-bit) In n-bit fault model, faults are n-bit aligned also, three methods: single-fault, two-fault, two-fault with partially recovered state at χ 23 i we use Method III (the last one) Antun Maldini GA-based EMFI 26 / 30
43 Results GA 106 exploitable faults out of distinct faults (0.71%) out of measurements (0.141%) Random 110 exploitable faults out of 947 distinct faults (11.61%) out of measurements (0.113%) A bit more efficient 24.6%. Antun Maldini GA-based EMFI 27 / 30
44 Why the loss? the GA phase is blind (no exploitability knowledge) the LS phase searches around all SUCCESS points equally To do: Integrate exploitability checks in fitness function Antun Maldini GA-based EMFI 28 / 30
45 Local search neighbourhood? The share of unique faults looks lower than baseline (34% vs 70%) Not a fair comparison! Still, can we improve? To do: Figure out a better range & number of points to scan in neighbourhood Antun Maldini GA-based EMFI 29 / 30
46 Questions? Antun Maldini GA-based EMFI 30 / 30
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