ALMOST: Adversarial Learning to Mitigate Oracle-less ML Attacks via Synthesis Tuning
Oracle-less machine learning (ML) attacks have broken various logic locking schemes. Regular synthesis, which is tailored for area-power-delay optimization, yields netlists where key-gate localities are vulnerable to learning. Thus, we call for security-aware logic synthesis. We propose ALMOST, a framework for adversarial learning to mitigate oracle-less ML attacks via synthesis tuning. ALMOST uses a simulated-annealing-based synthesis recipe generator, employing adversarially trained models that can predict state-of-the-art attacks' accuracies over wide ranges of recipes and key-gate localities. Experiments on ISCAS benchmarks confirm the attacks' accuracies drops to around 50\% for ALMOST-synthesized circuits, all while not undermining design optimization.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
XSS Adversarial Attacks Based on Deep Reinforcement Learning: A Replication and Extension Study
Cross-site scripting (XSS) poses a significant threat to web application security. While Deep Learning (DL) has shown remarkable success in detecting XSS attacks, it remains vulnerable to adversarial attacks due to the d…
Adversarial AttackDeep Reinforcement LearningModel Extraction and Adversarial Attacks on Neural Networks using Switching Power Information
Artificial neural networks (ANNs) have gained significant popularity in the last decade for solving narrow AI problems in domains such as healthcare, transportation, and defense. As ANNs become more ubiquitous, it is imp…
Model extractionStateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attacks
Recent work has proposed stateful defense models (SDMs) as a compelling strategy to defend against a black-box attacker who only has query access to the model, as is common for online machine learning platforms. Such sta…
Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors
We study the problem of generating adversarial examples in a black-box setting in which only loss-oracle access to a model is available. We introduce a framework that conceptually unifies much of the existing work on bla…
Adversarial Attacks Against Deep Learning-Based Radio Frequency Fingerprint Identification
Radio frequency fingerprint identification (RFFI) is an emerging technique for the lightweight authentication of wireless Internet of things (IoT) devices. RFFI exploits deep learning models to extract hardware impairmen…
Adversarial Attack