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“猜测”的技术来实现良好的性能。计算机还实施了防止私人数据向未经授权实体揭示的安全策略。最近，研究人员了解到，猜测可以无意间允许私人信息泄漏。攻击者可以操纵猜测，通过“侧渠道”击败安全政策来传达数据。该项目将探索使用机器学习的方法来检测计算机是否处于侧通道攻击下并触发防御措施，以防止私人数据被泄漏。这项工作将在维持投机的好处的同时实现安全的计算。将培训一个预测因子，以检测系统是否受到攻击，从而对预测提供了一定的信心。预测变量的输入将是诸如微构造事件计数之类的特征。预测变量将在离线训练，并在硬件中实现，以在执行过程中使用。使用来自真实和模拟系统的测量结果，将探讨与恶意行为相关的功能。基于神经学习的预测因素将接受这些功能的培训。预测变量将在微构造和电路模拟器中进行原型和评估。基于预测器置信度的缓解也将是原型的。Side通道攻击威胁着继续使用猜测来提供高性能。可以预期，这项工作将使人们能够继续对私人用户数据的安全性充满信心地继续使用投机，同时继续当今的移动，服务器和嵌入式应用程序所需的急需的性能水平。将鼓励来自代表性不足的小组的学生参加研究。该研究将在德克萨斯A＆M大学的课堂教学中进行介绍。项目守则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/.This奖中提供，反映了NSF的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响来通过评估来获得支持。

项目成果

Thermometer: profile-guided btb replacement for data center applications

• DOI: 10.1145/3470496.3527430
• 发表时间: 2022-06
• 期刊: Proceedings of the 49th Annual International Symposium on Computer Architecture
• 作者: Shixin Song;Tanvir Ahmed Khan;Sara Mahdizadeh-Shahri;Akshitha Sriraman;N. Soundararajan;S. Subramoney;Daniel A. Jiménez;Heiner Litz;Baris Kasikci