Collaborative Research: NSF-BSF: SaTC: CORE: Small: Detecting malware with machine learning models efficiently and reliably

协作研究：NSF-BSF：SaTC：核心：小型：利用机器学习模型高效可靠地检测恶意软件

• 批准号: 2338301
• 负责人: Ljudevit Bauer
• 金额: $ 32.04万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338301&HistoricalAwards=false
• 关键词: Collaborative; Research; NSF; BSF; SaTC

Detecting malicious software (a.k.a. malware) is a common security challenge that machine learning (ML) is increasingly used to perform. While many types of malware detectors exist, this project focuses on improving those that identify malware without actually running it. In these types of detectors, an ML model is trained on many examples of malware and of benign software to learn how to classify new examples based on patterns in the computer instructions they contain. A specific threat to the use of such ML models is that attackers might use their understanding of a model's workings to develop malware variants that are particularly hard for the model to detect. This project seeks to develop ML models for malware detection that are reliable despite attackers' attempts to fool them, as well developing models that are more time- and space-efficient.The technical innovations of this project lie in basic research on techniques to quantifiably improve the resistance of malware detectors to being fooled, with greater efficiency than has been possible to date. These techniques include new approaches to combining multiple classifiers into ensembles; novel model architectures; leveraging methods that attribute classification outcomes to specific properties of the input; and improvements to ML training methods for the malware domain. This project will develop principles underlying the application of these techniques to improve ML models and empirically demonstrate the improvements they yield, using datasets of malware and benign executables. Where doing so is responsible (i.e., does not risk facilitating new attacks on operational systems), the advances of this project will be open-sourced for others to leverage in follow-on research; in other cases, the implementations will be distributed only by request to parties who are verifiable researchers or developers of anti-malware tools.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.

检测恶意软件（又称恶意软件）是一个常见的安全挑战，越来越多地使用机器学习 (ML) 来执行。虽然存在多种类型的恶意软件检测器，但该项目的重点是改进那些无需实际运行即可识别恶意软件的检测器。 在这些类型的检测器中，机器学习模型接受了许多恶意软件和良性软件示例的训练，以学习如何根据新示例所包含的计算机指令中的模式对新示例进行分类。使用此类 ML 模型的一个具体威胁是，攻击者可能会利用他们对模型工作原理的理解来开发模型特别难以检测的恶意软件变体。 该项目旨在开发用于恶意软件检测的机器学习模型，尽管攻击者试图欺骗他们，但该模型仍然可靠，并开发时间和空间效率更高的模型。该项目的技术创新在于可量化改进技术的基础研究恶意软件检测器不会被愚弄，其效率比迄今为止更高。 这些技术包括将多个分类器组合成整体的新方法；新颖的模型架构；利用将分类结果归因于输入的特定属性的方法；以及针对恶意软件领域的机器学习训练方法的改进。 该项目将开发应用这些技术的原理来改进机器学习模型，并使用恶意软件和良性可执行文件的数据集凭经验证明它们产生的改进。 如果这样做是负责任的（即，不会冒促进对操作系统的新攻击的风险），该项目的进展将开源，供其他人在后续研究中利用；在其他情况下，实施方案将仅根据要求分发给可验证的反恶意软件工具研究人员或开发人员。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。