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binary_pattern_test_LSPUF.py
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binary_pattern_test_LSPUF.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Sep 2 11:28:47 2024
@author: Mieszko Ferens
Script to run an experiment for modelling a Lightweight Secure PUF that uses
shifted binary patterns as CRPs (BSP CRPs) during authentication with the
server.
"""
import argparse
import pandas as pd
from pathlib import Path
import numpy as np
from pypuf.simulation import LightweightSecurePUF
import pypuf.attack
class ChallengeResponseSet():
def __init__(self, n, challenges, responses):
self.challenge_length = n
self.challenges = challenges
self.responses = np.expand_dims(
np.expand_dims(responses,axis=1),axis=1)
def create_binary_code_challenges(n, N, pattern_len):
assert ((2**(pattern_len))*(n-pattern_len+1) > N), (
"Pattern length is too low for the number of patterns")
max_bits = int(np.ceil(np.log2(N)))
bits = min(max_bits, pattern_len)
lsb = np.arange(2**bits, dtype=np.uint8).reshape(-1,1)
extra = 0
if(bits % 8):
extra = 1
msb = []
for i in range(1, int(bits/8) + extra):
msb.append(lsb.copy())
for j in range(2**8):
msb[i-1][(2**(8*(i+1)))*j:(2**(8*(i+1)))*(j+1)].sort(axis=0)
lsb = np.unpackbits(lsb, axis=1)[:,-8:].copy()
for i in range(len(msb)):
msb[i] = np.unpackbits(msb[i], axis=1)[:,-8:].copy()
msb.insert(0, lsb)
patterns = 2*np.concatenate(msb[::-1], axis=1, dtype=np.int8) - 1
if(bits % 8):
patterns = np.delete(
patterns[:N], slice(8 - (bits % 8)), axis=1)
else:
patterns = patterns[:N]
if(bits < pattern_len):
patterns = np.concatenate(
(-np.ones((N, pattern_len - bits), dtype=np.int8), patterns),
axis=1, dtype=np.int8)
challenges = -np.ones(((n-pattern_len+1)*len(patterns), n), dtype=np.int8)
for i in range(len(patterns)):
for j in range(n-pattern_len+1):
challenges[i*(n-pattern_len+1)+j, j:j+pattern_len] = patterns[i]
_ , idx = np.unique(challenges, return_index=True, axis=0)
challenges = challenges[np.sort(idx)]
assert N <= len(challenges), (
"Not enough unique CRPs exist due to duplicates. " +
"Tip: You might need to increase the number of patterns")
challenges = challenges[:N]
return challenges
def main():
# Set-up logging
import logging
logging.basicConfig(level=logging.DEBUG)
# Parse arguments
parser = argparse.ArgumentParser()
parser.add_argument("--outdir", type=str, default="./Results/")
parser.add_argument("--seed", type=int, default=0, help="Random seed.")
parser.add_argument("--n-bits", type=int, default=64,
help="Challenge length in bits.")
parser.add_argument("--k", type=int, default=2,
help="The number of parallel APUF in the LSPUF.")
parser.add_argument("--n-CRPs", type=int, default=100000,
help="Minimum number of CRPs to be created.")
parser.add_argument("--pattern-len", type=int, default=25,
help="Binary coded pattern length in bits.")
parser.add_argument("--train-data", type=int, default=50000,
help="Number of training data samples for the model.")
parser.add_argument("--test-data", type=int, default=10000,
help="Number of testing data samples for the model.")
args = parser.parse_args()
# Check if enough CRPs are available to train and test the model
assert args.train_data + args.test_data <= args.n_CRPs, (
"Not enough CRPs. Tip: The number of CRPs must be greater or equal " +
"to the training and testing data")
# Generate the PUF
puf = LightweightSecurePUF(args.n_bits, args.k, args.seed)
# Generate the challenge
challenges = create_binary_code_challenges(
n=args.n_bits, N=args.n_CRPs, pattern_len=args.pattern_len)
# Get responses
responses = puf.eval(challenges)
# Split the data into training and testing
train = args.train_data
test = train + args.test_data
# Prepare the data for training and testing
train_crps = ChallengeResponseSet(
args.n_bits, np.array(challenges[:train], dtype=np.int8),
np.array(responses[:train], dtype=np.float64))
test_x = challenges[train:test]
test_y = np.expand_dims(0.5 - 0.5*responses[train:test], axis=1)
# Use MLP as a predictor
if(args.k <= 4): # If the XOR PUF is small don't reduce the NN too much
network = [8, 16, 8]
else: # As defined in the literature: [2^(k-1), 2^k, 2^(k-1)]
network = [2**(args.k-1), 2**args.k, 2**(args.k-1)]
model = pypuf.attack.MLPAttack2021(
train_crps, seed=args.seed, net=network, epochs=30, lr=.001,
bs=1000, early_stop=.08)
# Train the model
model.fit()
# Test the model
pred_y = model._model.eval(test_x)
pred_y = pred_y.reshape(len(pred_y), 1)
# Calculate accuracy
accuracy = np.count_nonzero(((pred_y<0.5) + test_y)-1)/len(test_y)
print("---\n" +
"Accuracy in the testing data: " + str(accuracy*100) + "%")
# Log data into csv format
data = pd.DataFrame({"seed": [args.seed],
"n_bits": [args.n_bits],
"k": [args.k],
"n_CRPs": [args.n_CRPs],
"pattern_len": [args.pattern_len],
"train_data": [args.train_data],
"test_data": [args.test_data],
"ML_algorithm": ["MLP"],
"accuracy": [accuracy]})
filepath = Path(args.outdir + "out_binary_pattern_" + str(args.k) +
"LS.csv")
if(filepath.is_file()):
data.to_csv(filepath, header=False, index=False, mode='a')
else:
filepath.parent.mkdir(parents=True, exist_ok=True)
data.to_csv(filepath, header=True, index=False, mode='a')
if(__name__ == "__main__"):
main()