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

该提案探讨了在数据管理工作负载环境中针对RAM抓取攻击的有效解决方案。最近的研究表明， RAM抓取攻击，其中对手安装恶意软件以窃取内存驻留数据，这是企业信息系统中数据盗窃的主要原因之一。拟议的研究探讨了RAM抓取引入的新漏洞，并开发了实际解决方案，这些解决方案提供了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，模型对抗能力和典型DBMS的约束（例如，持续时间短的重复攻击、缓冲和数据寿命的影响、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RAM抓取恶意软件产生的各种泄露风险;并开发优化技术，同时优化性能并降低泄露风险。建议的研究还探讨了RAM抓取攻击的威胁，为新兴的基于云的数据处理框架，如MapReduce.Intellectual优点：建议的研究代表了第一次尝试走向一个新的研究方向，即减轻影响的RAM抓取攻击的背景下，DBMS的工作负载，这还没有被广泛的数据库研究社区。所提出的重新设计查询优化器以探索性能和泄露风险之间的权衡的方法代表了数据库管理领域中查询处理和内存管理方面的重大创新。此外，在基于MapReduce风格的数据处理框架中探索RAM抓取攻击代表了云安全方面的新创新。EAGER的资助结果将有助于在数据库安全社区内开展一系列新的研究。即，具有风险意识的数据库管理系统，可在各种对抗性设置中平衡RAM抓取攻击带来的数据暴露风险和性能下降。这项研究还将对新兴云计算框架中的DBMS供应商和云服务提供商产生重大影响。欲了解更多信息，请访问项目网站：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