A Built-In Integrated Rowhammer, Rowpress, and Leakage Detection Sensor for DRAM

The increasing density of DRAM chips has led to heightened susceptibility of memory cells to bit-flips. Rowhammer and Rowpress attacks on DRAMs have obtained significant attention due to their effectiveness in compromising system security and data integrity. A substantial portion of the previously proposed techniques for detecting rowhammer attacks focus on counting the number of row activations. However, these methods suffer from several limitations, including high overheads, lack of consideration for environmental conditions during DRAM operation, and limited tunability to detect rowpress attacks. To address these challenges, this work introduces a novel low-overhead built-in DRAM sensor designed specifically for detecting and locating rowhammer and rowpress attacks. The core idea behind the sensor is to utilize an additional non-operational DRAM sensor cell for each row. This auxiliary cell experiences the same potential rowhammer attacks as the other cells within that row. The sensor operates by controlling an extra precharged match-line based on the charge stored in the sensor cells. This sensor not only detects rowhammer attacks but also other environmental conditions that may impact DRAM cells retention time, such as temperature elevation or changes in operating voltage. Simulation results demonstrate that the proposed sensor detects all of the rowhammer attacks in the presence of process variation with a variance of up to 7.5% in circuit parameters. Furthermore, the area overhead introduced by the sensor remains about 1%, making it a promising solution for enhancing DRAM security with the minimum memory structure change.

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Published
2024-10-27
DOI
https://doi.org/10.1145/3676536.3676813
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

A Built-In Integrated Rowhammer, Rowpress, and Leakage Detection Sensor for DRAM

Hamid Sarbazi‐Azad, Nezam Rohbani, Osman Ünsal, Adrián Cristal et al.
Advanced Malware Detection Techniques
article

A Built-In Integrated Rowhammer, Rowpress, and Leakage Detection Sensor for DRAM

Hamid Sarbazi‐Azad, Nezam Rohbani, Osman Ünsal, Adrián Cristal, Mohammad Arman Soleimani, Rouzbeh Pirayadi
article en

Abstract

The increasing density of DRAM chips has led to heightened susceptibility of memory cells to bit-flips. Rowhammer and Rowpress attacks on DRAMs have obtained significant attention due to their effectiveness in compromising system security and data integrity. A substantial portion of the previously proposed techniques for detecting rowhammer attacks focus on counting the number of row activations. However, these methods suffer from several limitations, including high overheads, lack of consideration for environmental conditions during DRAM operation, and limited tunability to detect rowpress attacks. To address these challenges, this work introduces a novel low-overhead built-in DRAM sensor designed specifically for detecting and locating rowhammer and rowpress attacks. The core idea behind the sensor is to utilize an additional non-operational DRAM sensor cell for each row. This auxiliary cell experiences the same potential rowhammer attacks as the other cells within that row. The sensor operates by controlling an extra precharged match-line based on the charge stored in the sensor cells. This sensor not only detects rowhammer attacks but also other environmental conditions that may impact DRAM cells retention time, such as temperature elevation or changes in operating voltage. Simulation results demonstrate that the proposed sensor detects all of the rowhammer attacks in the presence of process variation with a variance of up to 7.5% in circuit parameters. Furthermore, the area overhead introduced by the sensor remains about 1%, making it a promising solution for enhancing DRAM security with the minimum memory structure change.

Sharif University of Technology (IR), Barcelona Supercomputing Center (ES), Institute for Research in Fundamental Sciences (IR), Universitat Politècnica de Catalunya (ES)
European Commission
Affordable and clean energy
Openalex Percentile: Top 67%
Advanced Malware Detection Techniques
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