Adaptive Row Activation and Refresh a Hybrid Approach to Mitigate RowHammer

Abstract

RowHammer attack is an attack where an exploit is used to trigger a vulnerability in Dynamic Random Access Memory (DRAM). The attack is executed by repeatedly accessing a number of aggressor rows whereby this repeated access can induce bit flips in adjacent rows, leading to potential security breaches such as kernel access and privilege escalation. As the memory sizes increase, modern DRAM cells are getting increasingly dense over time. This phenomenon poses a significant threat to data integrity and system security. Although there are mitigation methods in existence, no method assures high security and high performance at the same time. This paper discusses two of the most widely used mitigation techniques: Target Row Refresh (TRR) and Probabilistic Adjacent Row Activation (PARA). Moreover, this paper proposes a hybrid mitigation approach, Adaptive Row Activation and Refresh (ARAR), which leverages the strengths of various existing mitigation methods. A comparative analysis in each section is performed to identify which approach delivers better performance and better security, while also identifying potential improvements for ARAR.