A Secure Integrity Checking System for Nanoelectronic Resistive RAM

Majumder, Md. Badruddoja; Hasan, Sakib; Uddin, Mesbah; Rose, Garrett S.

doi:10.1109/tvlsi.2018.2876693

Cited by 4 publications

(8 citation statements)

References 35 publications

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“…Table I presents the results for this tag generation method. This result is slightly different from the one presented in [13] as it is regenerated for the memristor type and crossbar used in this work.…”

Section: Bit-aliasingcontrasting

confidence: 63%

“…Sneak path currents in a crossbar memristive array are read using multiple columns and converted to digital bits in this tag generation method. Details about this system can be found in [13]. This scheme does not require an additional hash function or message authentication code (MAC) since the memory itself is used as the tag generator.…”

Section: F Sneak-path Based Tag Generation For Data Integrity Verific...mentioning

confidence: 99%

“…The produced tag is calculated from the sneak path currents of the crossbar of memristors [13], i.e. this is an analog in-memory computation.…”

Section: Readout or Alteration Of The Puf Challenge Or Secure Tagmentioning

confidence: 99%

“…After evaluating the security of the PUF, now we are interested in evaluating the security of the tag generation method that we have used in this work. This is a sneakpath based tag generation, specific to RRAM or memristive memory and the details can be found in [13]. Three metrics to evaluate any tag generation or hashing method are: uniformity (different than PUF's uniformity), diffusion, and avalanche effect.…”

Section: Bit-aliasingmentioning

confidence: 99%

“…To verify that the stored data has not been altered due to a data error or maliciously by an adversary, we have also used an integrity checking protocol during system wake-up, thereby providing a second layer of security. The integrity checking protocol considered here has in-memory tag generation capability leveraging sneak path currents in a memristive (or any resistive) memory [13]. After performing a backup of the program state and other necessary data, a tag is generated from the memory in order to verify data integrity when the system wakes up.…”

Section: Introductionmentioning

confidence: 99%

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A Secure Back-up and Restore for Resource-Constrained IoT based on Nanotechnology

Uddin¹,

Majumder²,

Sakib³

et al. 2020

Preprint

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With the emergence of IoT (Internet of things), huge amounts of sensitive data are being processed and transmitted everyday in edge devices with little to no security. Due to their aggressive power management schemes, it is a common and necessary technique to make a back-up of their program states and other necessary data in a non-volatile memory (NVM) before going to sleep or low power mode. However, this memory is often left unprotected as adding robust security measures tends to be expensive for these resource constrained systems. In this paper, we propose a lightweight security system for NVM during low power mode. This security architecture uses the memristor, an emerging nanoscale device which is used to build hardware security primitives like PUF (physical unclonable function) based encryption-decryption, true random number generators (TRNG), and memory integrity checking. A reliability enhancement technique for this PUF is also proposed which shows how this system would work even with less-than-100% reliable PUF responses. Together, with all these techniques, we have established a dual layer security protocol (data encryption+integrity check) which provides reasonable security to an embedded processor while being very lightweight in terms of area, power, and computation time. A complete system design is demonstrated with 65nm CMOS and emerging memristive technology. With this, we have provided a detailed and accurate estimation of resource overhead. Analysis of the security of the whole system is also provided.

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Section: Bit-aliasingcontrasting

confidence: 63%

Section: F Sneak-path Based Tag Generation For Data Integrity Verific...mentioning

confidence: 99%

“…The produced tag is calculated from the sneak path currents of the crossbar of memristors [13], i.e. this is an analog in-memory computation.…”

Section: Readout or Alteration Of The Puf Challenge Or Secure Tagmentioning

confidence: 99%

Section: Bit-aliasingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Secure Back-up and Restore for Resource-Constrained IoT based on Nanotechnology

Uddin¹,

Majumder²,

Sakib³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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Unidirectional Neuromorphic Resistive Memory Integrated with Piezoelectric Nanogenerator for Self‐Power Electronics

Khan

Abbas

Rezeq

et al. 2023

Adv Funct Materials

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This study presents a method to enhance data processing by integrating a unidirectional analogue artificial neuromorphic memristor device with a piezoelectric nanogenerator, taking inspiration from biological information processing. A self‐powered unidirectional neuromorphic resistive memory device is proposed, comprising an ITO/ZnO/Yb2O3/Au structure combined with a high‐sensitivity piezoelectric nanogenerator (PENG) ITO/ZnO/Al. The memristor device is operated at a voltage sweep of ±4 V with a low operating current in a range of 1.4 µA. The filament formation is studied using a conductive mode atomic force microscope. The integration enables the creation of a self‐powered artificial sensing system that converts mechanical stimuli from the PENG into electrical signals, which are subsequently processed by analogue unidirectional neuromorphic device to mimic the functionality of a neuron without requiring additional circuitry. This emulation encompasses crucial functions such as potentiation, depression, and synaptic plasticity. Furthermore, this study highlights the potential for hardware implementations of neural networks with a weight change of memristor device with nonlinearity (NL) of potentiation and depression of 1.94 and 0.89, respectively, with an accuracy of 93%. The outcomes of this research contribute to the progress of next‐generation low‐power, self‐powered unidirectional neuromorphic perception networks with correlated learning and trainable memory capabilities.

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