Redundancy AES Masking Basis for Attack Mitigation (RAMBAM)
In this work, we present RAMBAM, a novel concept of designing countermeasures against side-channel attacks and the Statistical Ineffective Fault Attack (specifically SIFA-1) on AES that employs redundant representations of finite field elements. From this concept, we derive a family of protected hardware implementations of AES. A fundamental property of RAMBAM is a security parameter d that along with other attributes of the scheme allows for making trade-offs between gate count, maximal frequency, performance, level of robustness to the first and higher-order side-channel attacks, and protection against SIFA-1. We present an analytical model that explains how the scheme reduces the leakage and how the design choices affect it. Furthermore, we demonstrate experimentally how different design choices achieve the required goals. In particular, the compact version exhibits a gate count as low as 12.075 kGE, while maintaining adequate protection. The performance-oriented version provides latency as low as one round per cycle, thus combining protection against SCA and SIFA-1 with high performance which is one of the original design goals of AES. Finally, we assess the leakage of the scheme for the first and the second (bivariate) orders using TVLA methodology on an FPGA implementation and observe resilience to at least 348M traces with 16 Sboxes.
DANA Universal Dataflow Analysis for Gate-Level Netlist Reverse Engineering 📺
Reverse engineering of integrated circuits, i.e., understanding the internals of Integrated Circuits (ICs), is required for many benign and malicious applications. Examples of the former are detection of patent infringements, hardware Trojans or Intellectual Property (IP)-theft, as well as interface recovery and defect analysis, while malicious applications include IP-theft and finding insertion points for hardware Trojans. However, regardless of the application, the reverse engineer initially starts with a large unstructured netlist, forming an incomprehensible sea of gates.This work presents DANA, a generic, technology-agnostic, and fully automated dataflow analysis methodology for flattened gate-level netlists. By analyzing the flow of data between individual Flip Flops (FFs), DANA recovers high-level registers. The key idea behind DANA is to combine independent metrics based on structural and control information with a powerful automated architecture. Notably, DANA works without any thresholds, scenario-dependent parameters, or other “magic” values that the user must choose. We evaluate DANA on nine modern hardware designs, ranging from cryptographic co-processors, over CPUs, to the OpenTitan, a stateof- the-art System-on-Chip (SoC), which is maintained by the lowRISC initiative with supporting industry partners like Google and Western Digital. Our results demonstrate almost perfect recovery of registers for all case studies, regardless whether they were synthesized as FPGA or ASIC netlists. Furthermore, we explore two applications for dataflow analysis: we show that the raw output of DANA often already allows to identify crucial components and high-level architecture features and also demonstrate its applicability for detecting simple hardware Trojans.Hence, DANA can be applied universally as the first step when investigating unknown netlists and provides major guidance for human analysts by structuring and condensing the otherwise incomprehensible sea of gates. Our implementation of DANA and all synthesized netlists are available as open source on GitHub.