首页> 外文期刊>Future generation computer systems >A comparative analysis of emerging approaches for securing Java software with Intel SGX
【24h】

A comparative analysis of emerging approaches for securing Java software with Intel SGX

机译:用英特尔SGX保护Java软件的新出现方法的比较分析

获取原文
获取原文并翻译 | 示例

摘要

Intel SGX enables developers to protect security critical parts of their application code and data even from privileged software. This type of protection is needed in all cases where applications run on untrusted infrastructures, including public clouds. Since a significant fraction of current applications is written in Java, the research strand on how to fully unleash the potential of SGX in Java is flourishing, and multiple techniques have been proposed. In this paper, we review such techniques, and select the most promising ones - namely SCONE, SGX-LKL, and SGX-JNI Bridge - for an experimental comparison with respect to effort, security, and performance. We use a benchmark application from a real-world case study based on microservices - possibly the most prominent software architecture for current applications - and built on the widely adopted Vert.x development framework. We focus on specific microservices characterized by three different profiles in terms of resource usage - I/O-, CPU-, and Memory-intensive - and assess the trade-offs of the three aforementioned techniques for SGX integration. The results of the analysis can be used as a reference by practitioners willing to identify the best approach for integrating SGX in their Java applications, based on priorities of their particular context. (C) 2019 Elsevier B.V. All rights reserved.
机译:Intel SGX使开发人员能够保护其应用程序代码和数据的安全关键部分即使是特权软件。在所有情况下都需要这种类型的保护,其中应用程序在不受信任的基础架构上运行,包括公共云。由于在Java中编写了大量的当前应用程序,因此如何完全释放在Java中的SGX潜力的研究股线是繁荣的,并且已经提出了多种技术。在本文中,我们审查了这些技术,并选择最有前途的技术 - 即烤饼,SGX-LKL和SGX-JNI桥 - 用于努力,安全性和性能的实验比较。我们使用基于微服务的真实案例研究的基准应用程序 - 可能是当前应用程序最突出的软件架构 - 并构建在广泛采用的Vert.x开发框架上。我们专注于特定的微服务,以资源使用量 - I / O-,CPU和内存密集,并评估三个上述技术用于SGX集成技术的权衡。分析的结果可以作为愿意确定在其Java应用程序中集成SGX的最佳方法的从业者的参考,基于其特定上下文的优先级。 (c)2019 Elsevier B.v.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号