Professor Nuzzo’s research groupanalyzes different types of hardwaresecurity attacks and defense techniqueswith the objective to enable systematicdesign of robust and secure IPs.Analysis of the existing approacheshelp reveal the current weaknesses andis key to the development of improveddefense techniques.
My next step is to finish my senioryear and then apply to the electricalengineering program at USC. Myadvice for any future studentparticipating in this program is toask as many questions as possible.Know what your objective is andmake sure everything is as clear aspossible.
I would thank Professor PierluigiNuzzo and my Ph.D. MentorSubhajit Dutta Chowdhury foranswering all my questions andgiving me this opportunity.
In modern times, security has become amajor concern for any computing systemsdue to the globalization of the integratedcircuit (IC) supply chain.
Hardware security protects Intellectualproperties (IPs) from attacks throughvarious defense techniques like logiclocking, gate camouflaging.
Logic locking secures a circuit by addingextra logic gates called key gates, whichhide the true functionality of the design.
Existing logic locking techniques are oftennot secure and in this work we explorehow Machine learning can be used toattack logic locking.
We have used a Multi-layer Perceptron(MLP) as the neural network. The MLPanalyzes the sub-circuit surrounding akey gate to directly predict its key value(0 or 1).
NN Architecture:input features: 3953 hidden layers: 1000, 512, 256 neurons
NN training: 100,000 locality vectors,extracted from 10 different benchmarkcircuits locked with RLL, were used fortraining.
NN testing: Each locked benchmark wasused for testing
Random Logic Locking (RLL):In RLL, the key gates (XOR/XNOR) areinserted at random locations in the IP.
We have developed a machine learningbased attack on Random Logic Locking.
For each key gate inserted in the circuit, acorresponding locality vector (LV) isextracted from the netlist.
LV extraction is based on the Breadth Firsttraversal algorithm for graphs.
Introduction Machine Learning Based AttackLogic Locking
Next Steps; Advice for Future SHINE
Students
Acknowledgements
Objective & Impact of Professor’s Research
Exploring Machine Learning Attacks on Logic LockingNic Hornung, [email protected]
Mountain View High School, Class of 2022USC Viterbi Department of Electrical and Computer Engineering, SHINE 2021
I have never coded a machinelearning program previously butalways wanted to try it. Thisprogram will greatly help with anycoding classes I take in the future.
Unlocked original
circuit
Original circuit locked with 2 keys
Dataset Creation
Key gate sub circuit
Extracted LV for Key
gate(KG)
Benchmark Accuracy
c432 55%
c499 59%
c880 52%
c1355 54%
c3540 51%
c7552 52%
The resultshows thatML basedapproachcan performbetter thanrandomguess.
The current dataset is small. The predictionaccuracy can be further improved byincreasing the dataset size.
Skills Learned
-Boolean algebra and logic-Basic digital circuit design-Python coding-Researching a new and unfamiliartopic-Basics of neural networks (MLP)and their implementation in pytorch
How This Relates to Your STEM Coursework
