Performance Impact of Magnetic and Thermal Attack on STTRAM and Low-Overhead Mitigation Techniques

Jang, Jae-Won; Ghosh, Swaroop

doi:10.1145/2934583.2934614

Cited by 8 publications

(10 citation statements)

References 16 publications

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“…Note that magnetic field-based toggling is the foundation of magnetic RAM. The attacker can exploit this extra knob to corrupt the free layer data [16,17]. Both permanent magnet as well as electromagnet could be used for tampering by the adversary.…”

Section: Threat Model (Magnetic Attack)mentioning

confidence: 99%

“…Both permanent magnet as well as electromagnet could be used for tampering by the adversary. It has been noted in [16,17] that the attacks on STTRAM could be launched either through static (DC) magnetic field or alternating (AC) magnetic field. The DC attack is less detrimental as it can only create unipolar failures.…”

Section: Threat Model (Magnetic Attack)mentioning

confidence: 99%

“…However, eFlash suffers from high latency (~μs) and poor endurance (~100,000 Program/Erase cycles). Spin-transfer torque RAM (STTRAM) offers much better latency (~ns), negligible leakage energy (~fJ/bit), and high endurance (~10 16 cycles), which makes it a promising viable candidate to replace the eFlash-based memory on IoT devices. STTRAM technology is already under commercialization by companies such as Everspin [8] and Avalanche Technology [9], for standalone and embedded memory applications.…”

Section: Introductionmentioning

confidence: 99%

“…Although exciting at first sight, STTRAM is susceptible to data security and data privacy attacks [16][17][18][19][20]. These attacks exploit the fact that STTRAM is fundamentally susceptible to ambient parameters such as magnetic field and temperature.…”

Section: Introductionmentioning

confidence: 99%

“…We assume attack sensors such as STTRAM with low free layer volume and weak write [16] to detect the attack. The activities are disabled during the attack and the STTRAM contents are discarded after attack.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Replacing eFlash with STTRAM in IoTs: Security Challenges and Solutions

Khan

Park

et al. 2017

J Hardw Syst Secur

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Spin-transfer torque RAM (STTRAM) is an emerging non-volatile memory (NVM) technology that provides better endurance, write energy and performance than traditional NVM technologies such as Flash. In embedded application such as microcontroller SoC of Internet of Things (IoTs), embedded Flash (eFlash) is widely employed. However, eFlash is also associated with cost, poor reliability and high write energy consumption. Therefore, replacing eFlash with emerging NVMs is desirable in IoTs and have been investigated in literature. Although promising, STTRAM brings several new security and privacy challenges such as magnetic attacks that pose a significant threat to sensitive program stored in memory. In this paper, we investigate these challenges and propose a novel memory architecture for attack resilient IoT network. The information redundancy present in a homogeneous peer-to-peer connected IoT network is exploited to restore the corrupted memory of any IoT node after magnetic attack. Since restoring program memory from other IoT is associated with energy overhead, we propose to reduce the number of corrupted bits by using high retention STTRAM at the cost of one-time write energy overhead. Energy overhead for the recovery process is estimated using commercial IoT boards, showing 19.85 nJ/bit for 100 MB data transfer. Increasing the STTRAM resiliency with a thermal stability factor of 72 reduces the recovery energy overhead to 9.01 nJ/bit (54.61% improvement) for a 100 Oe magnetic attack persisting for 1 s. We further propose to reduce the transfer energy by applying error correction codes (ECC) in STTRAM, which shows 10× (10 5 ×) improvement in energy for 64 (128) bit words with 16-bit ECC.

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Section: Threat Model (Magnetic Attack)mentioning

confidence: 99%

Section: Threat Model (Magnetic Attack)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%