Физически неклонируемые функции в аналоговых интегральных схемах

Цели. Целью работы является комплексный обзор аналоговых и пассивных физически неклонируемых функций (ФНФ), анализ уязвимостей к атакам на основе машинного обучения и разбор практических сценариев применения в современных интегральных схемах и устройствах интернета вещей.

Методы. Использованы методы количественной оценки различий реализаций ФНФ и признаков их формального описания, включая вычислимость, уникальность, реализуемость, сложность создания клонов, защиту от несанкционированного доступа.

Результаты. Показано, что аналоговые ФНФ относятся к классу «сильных» ФНФ, но требуют специальных мер для подавления влияния факторов внешней среды и старения. Приведены примеры, демонстрирующие близкую к идеальной уникальность (inter-HD1 ≈ 50%) при высокой стабильности (intra-HD2 < 1%) и рекордные энергетические показатели (единицы – десятки фДж/бит). Пассивные ФНФ характеризуются высокой стабильностью, но относятся к «слабым» ФНФ. Рассмотрены атаки на основе машинного обучения, показано, что конволюционные нейронные сети и многослойные перцептроны превосходят классические подходы. Средства защиты на уровне протокола, ограничивающие объем доступной злоумышленнику информации, позволяют избежать модификации архитектуры ФНФ.

Выводы. Аналоговые и пассивные ФНФ расширяют спектр средств аппаратной аутентификации и защиты
от подделок, особенно для маломощных и ресурсно-ограниченных устройств интернета вещей. Наиболее перспективны архитектуры с внутренней калибровкой и малыми накладными расходами по площади/потреблению, а также пассивные решения для задач однократной идентификации и контроля вмешательства. Остаются открытыми задачи стандартизации процедур чтения/оцифровки, повышения устойчивости к изменениям внешней среды и различным атакам, а также совмещения с коррекцией ошибок и постобработкой на кристалле. Для выбора архитектур ФНФ необходимо тщательное моделирование угроз и применение стратегий глубокой защиты с учетом будущих достижений машинного обучения.

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