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

检测恶意软件(也称为恶意软件)是机器学习(ML)越来越多地用于执行的常见安全挑战。虽然存在许多类型的恶意软件检测器，但该项目专注于改进那些在不实际运行恶意软件的情况下识别恶意软件的检测器。在这些类型的检测器中，ML模型针对许多恶意软件和良性软件的示例进行训练，以学习如何根据新示例包含的计算机指令中的模式对新示例进行分类。使用这种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了解来开发恶意软件变体，而这些变体对模型来说尤其难以检测。该项目旨在开发尽管攻击者试图愚弄恶意软件但仍然可靠的ML模型，以及开发更具时间和空间效率的模型。该项目的技术创新在于对定量提高恶意软件检测器抵抗上当的技术的基础研究，效率比迄今可能的更高。这些技术包括将多个分类器组合成集成的新方法；新的模型体系结构；利用将分类结果归因于输入的特定属性的方法；以及对恶意软件领域的ML训练方法的改进。该项目将开发应用这些技术的基本原理，以改进ML模型，并使用恶意软件和良性可执行程序的数据集，经验地演示它们产生的改进。如果这样做是有责任的(即，不会有促进对操作系统的新攻击的风险)，该项目的进展将是开源的，供其他人在后续研究中利用；在其他情况下，实施将仅根据请求分发给作为可验证的研究人员或反恶意软件工具开发商的各方。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.