As the popularity of graphics processing units (GPUs) grows rapidly in recent years, it becomes very critical to study and understand the security implications imposed by them. In this paper, we show that modern GPUs can “broadcast” sensitive information over the air to make a number of attacks practical. Specifically, we present a new electromagnetic (EM) side-channel vulnerability that we have discovered in many GPUs of both NVIDIA and AMD. We show that this vulnerability can be exploited to mount realistic attacks through two case studies, which are website fingerprinting and keystroke timing inference attacks. Our investigation recognizes the commonly used dynamic voltage and frequency scaling (DVFS) feature in GPU as the root cause of this vulnerability. Nevertheless, we also show that simply disabling DVFS may not be an effective countermeasure since it will introduce another highly exploitable EM side-channel vulnerability. To the best of our knowledge, this is the first work that studies realistic physical side-channel attacks on non-shared GPUs at a distance.

RAPL (Running Average Power Limit) is a hardware feature introduced by Intel to facilitate power management. Even though RAPL and its supporting software interfaces can benefit power management significantly, they are unfortunately designed without taking certain security issues into careful consideration. In this paper, we demonstrate that information leaked through RAPL-induced side channels can be exploited to mount realistic attacks. Specifically, we have constructed a new RAPL-based covert channel using a single AVX instruction, which can exfiltrate data across different boundaries (e.g., those established by containers in software or even CPUs in hardware); and, we have investigated the first RAPL-based website fingerprinting technique that can identify visited webpages with a high accuracy (up to 99% in the case of the regular network using a browser like Chrome or Safari, and up to 81% in the case of the anonymity network using Tor). These two studies form a preliminary examination into RAPL-imposed security implications. In addition, we discuss some possible countermeasures.

Automating pneumonia diagnosis from X-ray images could significantly improve patient diagnosing outcomes. A major challenge is that disease information (features) must be extracted directly from the image backgrounds. Motivated by recent advances in Convolutional Neural Network (CNN), we propose a hierarchical weighting deep learning model, ChestWNet, that combines DenseNet and transfer learning to detect and localize thoracic diseases from chest x-rays. Hierarchical weighting networks are designed to assign scores reflecting the importance of specific pixels (regions), and learning weights at pixel-, region-, and image-levels, jointly learning these hierarchical weighting networks and the image classification network in an end-to-end manner. Chest X-ray datasets are customized to solve the unbalancing label problem in these datasets. Extensive experiments show that ChestWNet significantly outperforms other established prediction methods, and can also be applied to similar scenarios with fixed point-of-interest regions in images.

Precisely forecasting wind speed is essential for wind power producers and grid operators. However, this task is challenging due to the stochasticity of wind speed. To accurately predict short-term wind speed under uncertainties, this paper proposed a multi-variable stacked LSTMs model (MSLSTM). The proposed method utilizes multiple historical meteorological variables, such as wind speed, temperature, humidity, pressure, dew point and solar radiation to accurately predict wind speeds. The prediction performance is extensively assessed using real data collected in West Texas, USA. The experimental results show that the proposed MSLSTM can preferably capture and learn uncertainties while output competitive performance.