ASCENT: Amplifying Power Side-Channel Resilience via Learning & Monte-Carlo Tree Search
<|reference_start|>ASCENT: Amplifying Power Side-Channel Resilience via Learning & Monte-Carlo Tree Search: Power side-channel (PSC) analysis is pivotal for securing cryptographic hardware. Prior art focused on securing gate-level netlists obtained as-is from chip design automation, neglecting all the complexities and potential side-effects for security arising from the design automation process. That is, automation traditionally prioritizes power, performance, and area (PPA), sidelining security. We propose a "security-first" approach, refining the logic synthesis stage to enhance the overall resilience of PSC countermeasures. We introduce ASCENT, a learning-and-search-based framework that (i) drastically reduces the time for post-design PSC evaluation and (ii) explores the security-vs-PPA design space. Thus, ASCENT enables an efficient exploration of a large number of candidate netlists, leading to an improvement in PSC resilience compared to regular PPA-optimized netlists. ASCENT is up to 120x faster than traditional PSC analysis and yields a 3.11x improvement for PSC resilience of state-of-the-art PSC countermeasures<|reference_end|>
@article{bhandari2024ascent:,
    title={ASCENT: Amplifying Power Side-Channel Resilience via Learning &
  Monte-Carlo Tree Search},
    author={Jitendra Bhandari, Animesh Basak Chowdhury, Mohammed Nabeel, Ozgur
  Sinanoglu, Siddharth Garg, Ramesh Karri, Johann Knechtel},
    journal={arXiv preprint arXiv:2406.19549},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19549},
    primaryClass={cs.CR cs.LG}
}