SaTC: CORE: Medium: Understanding and Fortifying Machine Learning Based Security Analytics

初步研究表明，通过控制输入数据或计算过程，攻击者可以使基于机器学习的安全分析无效。网络安全的历史表明，这类攻击很快就会在现实世界中变得更加普遍。该项目面临的挑战是开发一个系统的、基础的和实用的框架，以了解攻击、量化漏洞并加强基于机器学习的安全分析。该框架的有效性在现实的、面向用户的环境中使用真实的恶意软件数据集进行了评估和演示。该项目旨在从根本上改变基于机器学习的系统在安全和恶意软件分析、更广泛的网络安全以及科学、教育和技术中的许多其他应用领域的设计、开发和部署方式，因为机器学习的使用无处不在。这一项目结合了多种新的思想，组织成了四个相互关联的研究方向：(1)基于机器教学和主动学习的机器学习理论框架，用于理解攻击、量化漏洞、测量对手的能力和模型的稳健性；(2)用于机器学习的算法技术，用于自适应地对抗对手的特征和样本操纵策略；(3)使用真实和变异的恶意软件数据集在现有安全系统上对识别的攻击和防御策略进行广泛评估，并证明新的、强化的机器学习系统提高了攻击弹性；(4)现实世界面向用户的安全分析环境中的系统级对策。

Preliminary research has demonstrated that by gaining control of the input data or the computation procedures, attackers can render machine learning based security analysis ineffective. The history of cybersecurity suggests that such attacks will become more prevalent in the real-world soon. This project undertakes the challenge of developing a systematic, foundational, and practical framework to understand attacks, quantify vulnerabilities, and fortify machine learning based security analytics. The framework's effectiveness is evaluated and demonstrated in realistic, user-facing environments, using real malware datasets. This project aims to fundamentally change how machine learning based systems will be designed, developed and deployed for security and malware analytics, cybersecurity more broadly, and numerous other application areas in science, education, and technology, as the use of machine learning is ubiquitous. The findings can lead to new breeds of adaptive defense systems that are highly resilient to current and future security attacks, helping protect the nation and its citizens from cyber harm.This project combines multiple novel ideas synergistically, organized into four inter-related research thrusts: (1) machine learning theoretical framework, based on machine teaching and active learning, for understanding attacks, quantifying vulnerabilities, and measuring the capabilities of adversaries and model robustness; (2) algorithmic techniques for machine learning resilience, to adaptively counter adversaries' feature and sample manipulation strategies; (3) extensive evaluation of the identified attack and defense strategies with real and mutated malware datasets, on existing security systems, and demonstrate the improved attack resilience of the new, fortified machine learning system; (4) system-level countermeasures in real-world user-facing security analysis environments.