Dataplant: Enhancing System Security with Low-Cost In-DRAM Value Generation Primitives

Abstract: DRAM manufacturers have been prioritizing memory capacity, yield, and bandwidth for years, while trying to keep the design complexity as simple as possible. DRAM chips do not carry out any computation or other important functions, such as security. Processors implement most of the existing security mechanisms that protect the system against security threats, because 1) executing security mechanisms usually require non-trivial computational capabilities (e.g., encryption), and 2) commodity DRAM chips are not designed to perform computations or tasks other than data storage. In this work, we advocate for DRAM as a key component for providing security mechanisms to the system. To this end, we propose Dataplant, a new class of low-cost, high-performance, and reliable security primitives that can be integrated in commodity DRAM chips with minimal changes. The main idea of Dataplant is to slightly modify the internal DRAM timing signals to expose the inherent process variation found in all DRAM chips for generating unpredictable but reproducible values (e.g., keys) within DRAM. We use Dataplant to build two new security mechanisms. First, a new Dataplant-based physical unclonable function (PUF) with non-destructive read-out, low evaluation latency, robust responses, resiliency to temperature changes, and data-independent responses. Second, a new cold boot attack prevention mechanism that automatically destroys all data within DRAM on every power cycle with zero run-time energy and latency overheads. Using a combination of detailed simulations and experiments with 136 real commodity DRAM chips, we show that our Dataplant-based PUF has 1.8x higher throughput than the best state-of-the-art DRAM PUFs. We also demonstrate that our Dataplant-based cold boot attack protection mechanism is 19.5x faster and consumes 2.54x less energy when compared to existing mechanisms.