The employment of high-performance servers and GPU accelerators for training deep neural network models have greatly accelerated recent advances in deep learning (DL). DL frameworks, such as TensorFlow, MXNet, and Caffe2, have emerged to assist DL researchers to train their models in a distributed manner. Although current DL frameworks scale well for image classification models, there remain opportunities for scalable distributed training on natural language processing (NLP) models. We found that current frameworks show relatively low scalability on training NLP models due to the lack of consideration to the difference in sparsity of model parameters. In this paper, we propose Parallax, a framework that optimizes data parallel training by utilizing the sparsity of model parameters. Parallax introduces a hybrid approach that combines Parameter Server and AllReduce architectures to optimize the amount of data transfer according to the sparsity. Experiments show that Parallax built atop Tensor-Flow achieves scalable training throughput on both dense and sparse models while requiring little effort from its users. Parallax achieves up to 2.8x, 6.02x speedup for NLP models than TensorFlow and Horovod with 48 GPUs, respectively. The training speed for the image classification models is equal to Horovod and 1.53x faster than TensorFlow.

To protect customers' sensitive information, many mobile financial applications include steps to probe the runtime environment and abort their execution if the environment is deemed to have been tampered with. This paper investigates the security of such self-defense mechanisms used in 76 popular financial Android apps in the Republic of Korea. Our investigation found that existing tools fail to analyze these Android apps effectively because of their highly obfuscated code and complex, non-traditional control flows. We overcome this challenge by extracting a call graph with a self-defense mechanism, from a detailed runtime trace record of a target app's execution. To generate the call graph, we identify the causality between the system APIs (Android APIs and system calls) used to check device rooting and app integrity, and those used to stop an app's execution. Our analysis of 76 apps shows that we can pinpoint methods to bypass a self-defense mechanism using a causality graph in most cases. We successfully bypassed self-defense mechanisms in 67 out of 73 apps that check device rooting and 39 out of 44 apps that check app integrity. While analyzing the self-defense mechanisms, we found that many apps rely on third-party security libraries for their self-defense mechanisms. Thus we present in-depth studies of the top five security libraries. Our results demonstrate the necessity of a platform-level solution for integrity checks.