EAGER: Detecting and Avoiding Side-Channel Attacks with Security Conscious Prediction

EAGER：通过安全意识预测检测和避免侧通道攻击

• 批准号: 1938064
• 负责人: Daniel Jimenez
• 金额: $ 22万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1938064&HistoricalAwards=false
• 关键词: EAGER; Detecting; Avoiding; Side; Channel

For many years, computers have been using a technique called "speculation" to achieve good performance. Computers also implement security policies that prevent private data from being revealed to unauthorized entities. Recently, researchers have learned that speculation can unintentionally allow private information to be leaked. An attacker can manipulate speculation to communicate data through a "side-channel," defeating security policies. This project will explore ways to use machine learning to detect whether a computer is under a side-channel attack and trigger defenses that will keep private data from being leaked. The work will enable secure computing while maintaining the benefits of speculation.A predictor will be trained to detect whether the system is under attack, providing a level of confidence in the prediction. Input to the predictor will be features such as counts of microarchitectural events. The predictor will be trained offline and implemented in hardware to be used during execution. Using measurements from real and simulated systems, features correlated with malicious behavior will be explored. Predictors based on neural learning will be trained with those features. The predictor will be prototyped and evaluated in a microarchitectural and circuit simulator. Mitigations based on the predictor confidence will also be prototyped.Side-channel attacks threaten the continued use of speculation to provide high performance. It is expected that this work will enable the continued use of speculation with high confidence in the security of private user data while continuing the much needed level of performance demanded by today's mobile, server, and embedded applications. Students from under-represented groups will be encouraged to participate in the research. The research will be featured in classroom teaching at Texas A&M University.The project code and data will be made available for at least two years following the completion of the project. The products of this project including technical papers, code archive, and datasets will be made available at http://taco.cse.tamu.edu/secure/.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

多年来，计算机一直使用一种称为“推测”的技术来获得良好的性能。计算机还实施安全策略，防止私人数据泄露给未经授权的实体。最近，研究人员了解到，猜测可能会无意中泄露私人信息。攻击者可以操纵推测，通过“旁道”传输数据，从而破坏安全策略。该项目将探索使用机器学习来检测计算机是否受到旁道攻击并触发防御措施以防止私人数据泄露的方法。这项工作将实现安全计算，同时保持推测的好处。将训练预测器来检测系统是否受到攻击，从而为预测提供一定程度的置信度。预测器的输入将是微架构事件计数等特征。预测器将进行离线训练并在硬件中实现以在执行期间使用。使用真实和模拟系统的测量结果，将探索与恶意行为相关的特征。基于神经学习的预测器将使用这些特征进行训练。该预测器将在微架构和电路模拟器中进行原型设计和评估。基于预测器置信度的缓解措施也将原型化。侧通道攻击威胁到继续使用推测来提供高性能。预计这项工作将使人们能够对私人用户数据的安全性充满信心地继续使用推测，同时继续满足当今移动、服务器和嵌入式应用程序所要求的急需的性能水平。来自代表性不足群体的学生将被鼓励参与研究。该研究将在德克萨斯农工大学的课堂教学中进行展示。项目代码和数据将在项目完成后至少两年内提供。该项目的产品，包括技术论文、代码档案和数据集，将在 http://taco.cse.tamu.edu/secure/ 上提供。该奖项反映了 NSF 的法定使命，并通过评估被认为值得支持基金会的智力价值和更广泛的影响审查标准。

项目成果

Thermometer: profile-guided btb replacement for data center applications

温度计：数据中心应用的轮廓引导 BTB 替代品

• DOI: 10.1145/3470496.3527430
• 发表时间: 2022-06
• 期刊: International Symposium on Computer Architecture (ISCA
• 作者: Song, Shixin;Khan, Tanvir Ahmed;Shahri, Sara Mahdizadeh;Sriraman, Akshitha;Soundararajan, Niranjan K;Subramoney, Sreenivas;Jiménez, Daniel A.;Litz, Heiner;Kasikci, Baris
• 通讯作者: Kasikci, Baris