SQ-PUF: پروتکل احراز هویت مبتنی برPUF مقاوم در برابر حملات یادگیری ماشین
محورهای موضوعی : مهندسی برق و کامپیوتر
سید ابوالفضل سجادی هزاوه
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بیژن علیزاده
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1 - دانشكده مهندسی برق و كامپیوتر، دانشکدگان فنی، دانشگاه تهران
2 - دانشكده مهندسی برق و كامپیوتر، دانشکدگان فنی، دانشگاه تهران
کلید واژه: اینترنت اشیا, یادگیری ماشین, احراز هویت, امنیت شبکه, توابع غیرهمسان فیزیکی,
چکیده مقاله :
توابع غیرهمسان فیزیکی (PUF) سخت‌افزاری را برای تولید الگویی منحصربه‌فرد از چالش- پاسخ با اهداف احراز هویت و رمزگذاری ارائه می‌دهند. یکی از ویژگی‌های مهم در این مدارها غیرقابل پیش‌بینی‌بودن است؛ به این معنی که یک مهاجم نمی‌تواند پاسخ‌های آینده را از مشاهدات قبلی پیش‌بینی کند. با این حال نشان داده شده که الگوریتم‌های یادگیری ماشین، تهدیدی قابل توجه برای PUF ها هستند؛ زیرا آنها قادر به مدل‌سازی دقیق رفتار PUF می‌باشند. در این مقاله، ما تهدیدات امنیتیPUF را تحلیل و یک روش احراز هویت مبتنی بر PUF به نام SQ-PUF را ارائه می‌کنیم که می‌تواند در برابر حملات یادگیری ماشین مقاومت خوبی از خود نشان دهد. توانایی شبیه‌سازی یا پیش‌بینی آن را با مبهم‌سازی همبستگی بین جفت‌های چالش- پاسخ‌ها دشوار کردیم. نتایج تجربی نشان می‌دهند که برخلاف PUFهای موجود، حتی با مجموعه‌ای از داده‌های بزرگ هم نمی‌توان به مدل SQ-PUF حمله موفقی داشت و بیشترین دقت پیش‌بینی %۵۳ است که نشان‌دهنده غیرقابل پیش‌بینی‌بودن این مدل می‌باشد. علاوه بر این، یکنواختی و یکتایی در این مدل تقریباً با مقدار ایده‌آل در A-PUF یکسان باقی مانده است.
Physically unclonable functions (PUFs) provide hardware to generate a unique challenge-response pattern for authentication and encryption purposes. An essential feature of these circuits is their unpredictability, meaning that an adversary cannot sufficiently predict future responses from previous observations. However, machine learning algorithms have been demonstrated to be a severe threat to PUFs since they are capable of accurately modeling their behavior. In this work, we analyze PUF security threats and propose a PUF-based authentication mechanism called SQ-PUF, which can provide good resistance to machine learning attacks. In order to make it harder to simulate or predict, we obfuscated the correlation between challenge-response pairs. Experimental results show that, unlike existing PUFs, even with a large data set, the SQ-PUF model cannot be successfully attacked with a maximum prediction accuracy of 53%, indicating that this model is unpredictable. In addition, the uniformity in this model remains almost the same as the ideal value in A-PUF.
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