A Lightweight Delay-based Authentication Scheme for DMA Attack Mitigation

Gui, Yutian; Siddiqui, Ali Shuja; Nicholas, Geraldine Shirley; Hughes, Marcus; Saqib, Fareena

doi:10.1109/isqed51717.2021.9424262

Cited by 1 publication

(4 citation statements)

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“…The same year, the Intel Advanced Threat Research team performed a DMA attack over the air by modifying a WiGig dock to compromise a laptop that is connected to the dock wirelessly [20]. The architecture of wireless connection allows the attacker to use the DMA capabilities to dump secrets out of the memory on the victim machine remotely.…”

Section: Dma Attackmentioning

confidence: 99%

“…The ROI is used to find the region in the sub-dataset with the lowest noise. In the raw measurements of the data collected from the device, the variance is high, so the proposed scheme [20] calculates the most reliable ROI in the sub-dataset. One of the algorithms is a difference-based algorithm and the other is a correlation-based algorithm.…”

Section: Region Of Interest (Roi) Selectionmentioning

confidence: 99%

“…The Difference-based ROI (DROI) algorithm, denoted as Algorithm 1 [20], takes pairs of different sub-datasets and calculate the difference between them for the same region, starting with the first measurement and ending with the last one. In this algorithm, there are C (R, 2) possible combinations of different pairs for comparison with (S−L) possible regions, where L represents the length of the DROI.…”

Section: Region Of Interest (Roi) Selectionmentioning

confidence: 99%

“…for k = (j + 1) to R //for all the combinations With the Correlation-based ROI (CROI) algorithm, denoted as Algorithm 2 [20], a correlation coefficient, a statistical index for the dependency of variables, is computed for pairs of sub-datasets. Specifically, the Pearson's correlation coefficient of all the regions in the pairs is computed.…”

Section: Region Of Interest (Roi) Selectionmentioning

confidence: 99%

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A Delay-Based Machine Learning Model for DMA Attack Mitigation

et al. 2021

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Direct Memory Access (DMA) is a state-of-the-art technique to optimize the speed of memory access and to efficiently use processing power during data transfers between the main system and a peripheral device. However, this advanced feature opens security vulnerabilities of access compromise and to manipulate the main memory of the victim host machine. The paper outlines a lightweight process that creates resilience against DMA attacks minimal modification to the configuration of the DMA protocol. The proposed scheme performs device identification of the trusted PCIe devices that have DMA capabilities and constructs a database of profiling time to authenticate the trusted devices before they can access the system. The results show that the proposed scheme generates a unique identifier for trusted devices and authenticates the devices. Furthermore, a machine learning–based real-time authentication scheme is proposed that enables runtime authentication and share the results of the time required for training and respective accuracy.

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Section: Dma Attackmentioning

confidence: 99%

Section: Region Of Interest (Roi) Selectionmentioning

confidence: 99%

Section: Region Of Interest (Roi) Selectionmentioning

confidence: 99%

Section: Region Of Interest (Roi) Selectionmentioning

confidence: 99%

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