Collaborative Research: SaTC: CORE: Small: Towards Label Enrichment and Refinement to Harden Learning-based Security Defenses

协作研究：SaTC：核心：小型：走向标签丰富和细化以强化基于学习的安全防御

• 批准号: 2055320
• 负责人: Xinyu Xing
• 金额: $ 24.79万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2055320&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; CORE; Small

This project aims to harden machine learning based security defenses by improving their ability to handle dynamic changes. From data breaches to ransomware infections, the increasingly sophisticated attacks are posing a serious threat to Internet-enabled systems and their users. While machine learning has shown great promise to build the next generation of defense, these defense systems are vulnerable to the dynamic changes (or concept drift) in the data caused by attacker evolvement and behavior changes of benign players. Traditionally, detecting and mitigating the impact of concept drift requires significant efforts to label new data, which is challenging to scale up. In this project, the team of researchers will design novel schemes to improve the adaptability and resilience of learning-based defenses that require minimal labeling capacity. The core idea is to use self-supervised learning models, utilizing unlabeled data and obtaining supervision from the data itself. If successful, the project will provide the much-needed tools to measure, detect, and mitigate concept drift for security applications, including malware analysis, network intrusion detection, and bot detection.The team of researchers will first focus on measuring concept drift over longitudinal data. With a focus on real-world malware samples, the team will develop measurement tools to extract and characterize different types of concept drift to understand their patterns. In the next stage, the team will develop reactive methods to detect drifting samples via contrastive learning (a form of self-supervision), and methods to select drifting samples to facilitate efficient labeling. Finally, the team will move from reactive defense to proactive approaches. The plan is to use adversarial generative models (another form of self-supervision) to synthesize richer data and labels that mimic future mutations of attackers, which will be used to harden the defenses at the training stage. The proposed techniques are expected to reduce the data labeling costs for learning-based defenses and improve their long-term sustainability to protect users, organizations, and critical infrastructures. The team will also leverage this project to recruit and mentor underrepresented students, develop new course materials, and perform technology transfer.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.

该项目旨在通过提高处理动态变化的能力来强化基于机器学习的安全防御。从数据泄露到勒索软件感染，日益复杂的攻击对互联网系统及其用户构成了严重威胁。虽然机器学习在构建下一代防御方面显示出了巨大的希望，但这些防御系统很容易受到攻击者进化和良性参与者行为变化引起的数据动态变化（或概念漂移）的影响。传统上，检测和减轻概念漂移的影响需要付出巨大努力来标记新数据，而这在扩大规模方面具有挑战性。在该项目中，研究人员团队将设计新颖的方案，以提高需要最小标记能力的基于学习的防御的适应性和弹性。其核心思想是采用自监督学习模型，利用未标记的数据并从数据本身获得监督。如果成功，该项目将提供急需的工具来测量、检测和减轻安全应用程序的概念漂移，包括恶意软件分析、网络入侵检测和机器人检测。研究人员团队将首先专注于测量纵向概念漂移数据。该团队将重点关注现实世界的恶意软件样本，开发测量工具来提取和表征不同类型的概念漂移，以了解其模式。在下一阶段，该团队将开发通过对比学习（一种自我监督形式）检测漂移样本的反应方法，以及选择漂移样本以促进高效标记的方法。最后，团队将从被动防御转向主动防御。该计划是使用对抗性生成模型（另一种形式的自我监督）来合成更丰富的数据和标签，模仿攻击者未来的突变，这将用于在训练阶段强化防御。所提出的技术预计将降低基于学习的防御的数据标记成本，并提高其长期可持续性，以保护用户、组织和关键基础设施。该团队还将利用该项目来招募和指导代表性不足的学生、开发新的课程材料并进行技术转让。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。