UNTANGLE -- Unlocking Routing and Logic Obfuscation Using Graph Neural Networks-based Link Prediction
Lilas Alrahis, Satwik Patnaik, Muhammad Abdullah Hanif, Muhammad Shafique, and Ozgur Sinanoglu
Contact
Lilas Alrahis ([email protected])
UNTANGLE is a link prediction-based attack on logic locking. This repository contains the python implementation of UNTANGLE attack in addition to the implementation of random MUX locking and InterLock.
$ git clone https://github.com/muhanzhang/pytorch_DGCNN
$ cd pytorch_DGCNN/lib
$ make -j4
$ cd ../..
- Install PyTorch
- Install numpy, scipy, networkx, tqdm, sklearn, gensim
1) Lock a design
- First: Modify line 8 in
./prepare_datasets/perl_scripts/MUX_random_lock.pl
and place the full path totheCircuit.pm
(This is done only once) - Example, lock the c7552 ISCAS benchmark with key size of 256
$ cd ./prepare_datasets/perl_scripts/
$ perl MUX_random_lock.pl -k 256 -i ../test_c7552/ > log.txt
-
MUX_random_lock.pl
is a Perl script that reads a circuit in Bench format and locks it using 2-input MUXes. It will convert the design into a graph. It assigns unique numerical IDs (0 to N-1) to the nodes (gates). N represents the total number of nodes (gates) in the design. -
-k
flag specifies the desired key size. -
It will generate a directory
../../data/c7552_MUX_K256
which includes:- The extracted features will be dumped in
feat.txt
. The ith line in feat.txt represent the feature vector of the node ID = the ith line incount.txt
- The existence of an edge i between two vertices u and v is represented by the entry of ith line in
links_train.txt
- The
links_test.txt
andlink_test_n.txt
are created to identify the edges exclusive to the testing set.links_test.txt
includes all the true MUX connections whilelink_test_n.txt
includes all the false MUX connections - The
cell.txt
file includes the mapping between node IDs and gate instances - The
locked_MUX_2_K_256_c7552.bench
file represents the locked circuit
- The extracted features will be dumped in
2) Train UNTANGLE
$ cd ../../
$ python Main.py --file-name c7552_MUX_K256 --train-name links_train.txt --test-name links_test.txt --testneg-name link_test_n.txt --hop 2 --save-model > Log_train_c7552_MUX_K256.txt
3) Get the predictions
$ python Main.py --file-name c7552_MUX_K256 --train-name links_train.txt --test-name links_test.txt --hop 2 --only-predict > Log_pos_predict_c7552_MUX_K256.txt
$ python Main.py --file-name c7552_MUX_K256 --train-name links_train.txt --test-name link_test_n.txt --hop 2 --only-predict > Log_neg_predict_c7552_MUX_K256.txt
- The likelihoods for the links will be dumped in
links_test_2__pred.txt
andlink_test_n_2__pred.txt
. Where2
represents the hop size
4) Parse the predictions
$perl break_MUX.pl c7552_MUX_K256
If you find the code useful, please cite our paper:
- ICCAD 2021:
@INPROCEEDINGS{untangle,
author={Alrahis, Lilas and Patnaik, Satwik and Hanif, Muhammad Abdullah and Shafique, Muhammad and Sinanoglu, Ozgur},
booktitle={2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)},
title={UNTANGLE: Unlocking Routing and Logic Obfuscation Using Graph Neural Networks-based Link Prediction},
year={2021},
volume={},
number={},
pages={1-9},
doi={10.1109/ICCAD51958.2021.9643476}}
We owe many thanks to Muhan Zhang for making his SEAL code available.