Deep Learning Based Diagnostics for Rowhammer Protection of DRAM Chips

Abstract

Modern day DRAM chips have been shown to have a reliability issue which can lead to erratic bit flips, a phenomenon which is called Rowhammer. Although current DRAM modules come with in-built countermeasures, recent attacks have shown they are still vulnerable. The Rowhammer vulnerability has been used in conjunction with other side-channels to lead to devastating attacks. In this work, we take a novel approach by training a deep learning model based on several successful and unsuccessful attempts to conduct Rowhammer. The objective of the model is to analyze the access patterns of the DRAM by reverse engineering the physical address to pinpoint exact DRAM location and in turn use them for early prediction of a potential Rowhammer flip. We showed that our approach could detect a probable Rowhammer attempt with considerably high accuracy and even before the completion of the attack. In a more general context, this work shows that suitable combinations of deep learning and reverse engineering of physical address space can help to enhance both the reliability and security of systems.