Small animal image-guided radiotherapy: status, considerations and potential for translational impact.

Radiation biology is being transformed by the implementation of small animal image-guided precision radiotherapy into pre-clinical research programmes worldwide. We report on the current status and developments of the small animal radiotherapy field, suggest criteria for the design and execution of effective studies and contend that this powerful emerging technology, used in combination with relevant small animal models, holds much promise for translational impact in radiation oncology.

Ultrasound entropy may be a new non-invasive measure of pre-clinical vascular damage in young hypertensive patients

Background: The identification of pre-clinical microvascular damage in hypertension by non-invasive techniques has proved frustrating for clinicians. This proof of concept study investigated whether entropy, a novel summary measure for characterizing blood velocity waveforms, is altered in participants with hypertension and may therefore be useful in risk stratification.

Methods: Doppler ultrasound waveforms were obtained from the carotid and retrobulbar circulation in 42 participants with uncomplicated grade 1 hypertension (mean systolic/diastolic blood pressure (BP) 142/92 mmHg), and 26 healthy controls (mean systolic/diastolic BP 116/69 mmHg). Mean wavelet entropy was derived from flow-velocity data and compared with traditional haemodynamic measures of microvascular function, namely the resistive and pulsatility indices.

Results: Entropy, was significantly higher in control participants in the central retinal artery (CRA) (differential mean 0.11 (standard error 0.05 cms(-1)), CI 0.009 to 0.219, p 0.017) and ophthalmic artery (0.12 (0.05), CI 0.004 to 0.215, p 0.04). In comparison, the resistive index (0.12 (0.05), CI 0.005 to 0.226, p 0.029) and pulsatility index (0.96 (0.38), CI 0.19 to 1.72, p 0.015) showed significant differences between groups in the CRA alone. Regression analysis indicated that entropy was significantly influenced by age and systolic blood pressure (r values 0.4-0.6). None of the measures were significantly altered in the larger conduit vessel.

Conclusion: This is the first application of entropy to human blood velocity waveform analysis and shows that this new technique has the ability to discriminate health from early hypertensive disease, thereby promoting the early identification of cardiovascular disease in a young hypertensive population.

Pre-Processing Power Traces to Defeat Random Clocking Countermeasures

We describe a pre-processing correlation attack on an FPGA implementation of AES, protected with a random clocking countermeasure that exhibits complex variations in both the location and amplitude of the power consumption patterns of the AES rounds. It is demonstrated that the merged round patterns can be pre-processed to identify and extract the individual round amplitudes, enabling a successful power analysis attack. We show that the requirement of the random clocking countermeasure to provide a varying execution time between processing rounds can be exploited to select a sub-set of data where sufficient current decay has occurred, further improving the attack. In comparison with the countermeasure's estimated security of 3 million traces from an integration attack, we show that through application of our proposed techniques that the countermeasure can now be broken with as few as 13k traces.

Neural Network Based Attack on a Masked Implementation of AES

Masked implementations of cryptographic algorithms are often used in commercial embedded cryptographic devices to increase their resistance to side channel attacks. In this work we show how neural networks can be used to both identify the mask value, and to subsequently identify the secret key value with a single attack trace with high probability. We propose the use of a pre-processing step using principal component analysis (PCA) to significantly increase the success of the attack. We have developed a classifier that can correctly identify the mask for each trace, hence removing the security provided by that mask and reducing the attack to being equivalent to an attack against an unprotected implementation. The attack is performed on the freely available differential power analysis (DPA) contest data set to allow our work to be easily reproducible. We show that neural networks allow for a robust and efficient classification in the context of side-channel attacks.

Power Spectral Density Side-Channel Attack Overlapping Window Method

Cryptographic algorithms have been designed to be computationally secure, however it has been shown that when they are implemented in hardware, that these devices leak side channel information that can be used to mount an attack that recovers the secret encryption key. In this paper an overlapping window power spectral density (PSD) side channel attack, targeting an FPGA device running the Advanced Encryption Standard is proposed. This improves upon previous research into PSD attacks by reducing the amount of pre-processing (effort) required. It is shown that the proposed overlapping window method requires less processing effort than that of using a sliding window approach, whilst overcoming the issues of sampling boundaries. The method is shown to be effective for both aligned and misaligned data sets and is therefore recommended as an improved approach in comparison with existing time domain based correlation attacks.

[4 phot. de monuments musulmans en France, 3 du cimetière musulman au Père-Lachaise à Paris, 1 d'une gravure représentant la mosquée Mahomet à Buzancy (Ardennes), le tout dû à la Photogravure parisienne de monuments historiques, 30, boulevard Saint-Germain. Des collections du prince R. Bonaparte]

Donateur : Bonaparte, Roland (1858-1924)