EAGER-TC: Limiting Effect of RAM-Scraping Attacks in DBMSs

本提案探讨了在数据管理工作负载的背景下RAM抓取攻击的有效解决方案。最近的研究发现，RAM抓取攻击是企业信息系统中数据盗窃的主要原因之一，攻击者安装恶意软件来窃取内存驻留数据。拟议的研究探索RAM抓取引入的新漏洞，并开发实用的解决方案，提供针对此类攻击的正确安全组合，同时将性能下降（由于安全方案的成本/开销）保持在可接受的范围内。研究团队将探索现实的攻击场景，模拟典型dbms的对抗能力和约束（例如，持续时间短的重复攻击，缓冲和数据生命周期的影响，并发查询的存在，带宽限制等），风险感知查询优化的设计标准，可以解释由ram抓取恶意软件产生的各种披露风险；并开发优化技术，同时优化性能和减少披露风险。拟议的研究还探讨了新兴的基于云的数据处理框架（如MapReduce）的ram抓取攻击的威胁。智力优势：该研究代表了对一个新的研究方向的第一次尝试，即减轻数据库管理系统工作负载背景下ram抓取攻击的影响，这是数据库研究社区尚未解决的问题。所提出的重新设计查询优化器以探索性能和披露风险之间的权衡的方法代表了数据库管理领域查询处理和内存管理方面的重大创新。此外，在MapReduce风格的基于云的数据处理框架中探索RAM抓取攻击代表了云安全的新创新。更广泛的影响：这项EAGER资助的结果将有助于在数据库安全社区内启动一条新的研究路线。即，风险感知数据库管理，平衡RAM抓取攻击带来的数据暴露风险和各种敌对设置下的性能下降。在新兴的云计算框架中，所提出的研究也可能对DBMS供应商和数据服务提供商产生重大影响。欲了解更多信息，请参阅项目网站的URL:http://www.ics.uci.edu/~projects/privacygroup/projects.html

This proposal explores effective solutions to RAM scraping attacks in the context of data management workloads. Recent studies identify RAM scraping attacks, wherein an adversary installs malware to stealmemory resident data, as one of the major causes of data thefts inenterprise information systems. Proposed research explores new vulnerabilities introduced by RAM scraping and developspractical solutions that offer the right blend of security against suchattacks while keeping performance degradation (due to cost/overhead ofsecurity scheme) within acceptable limits. The research team will explore realistic attack scenarios, model adversarial capabilities and constraints for typical DBMSs (e.g., small duration repeated attacks, effects of buffering and data lifetime, presence of concurrent queries, bandwidth limits, etc.), design criteria of a risk-aware query optimization that can accountfor a variety of disclosure risks emanating from RAM-scraping malwares;and develop optimization techniques that simultaneously optimize performance and reduce disclosure risks. Proposed research also explores the threat ofRAM-scraping attacks for the emerging cloud-based data processing frameworks like MapReduce.Intellectual Merit: The proposed research represents the first attempttowards a new direction of research , viz. mitigating impact of RAMscraping attacks in the context of DBMS workloads, that has not beenaddressed by the database research community at large. The proposedapproach of redesigning a query optimizer to explore tradeoffs betweenperformance and disclosure risks represents a significant innovation inquery processing and memory management in the database management area.Furthermore, exploration of RAM scraping attacks in MapReduce stylecloud-based data processing frameworks represents new innovation in cloudsecurity.Broader Impact: The results of this EAGER grant will help launch a new lineof research within database security community ? viz., risk aware databasemanagement that balances data exposure risks from RAM scraping attacks withperformance degradation in a variety of adversarial settings. The proposedresearch could also make a significant impact on the DBMS vendors and dataservice providers in the emerging cloud computing framework.For further information see the project web site at the URL:http://www.ics.uci.edu/~projects/privacygroup/projects.html