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

该项目旨在通过提高机器学习处理动态变化的能力来加强基于机器学习的安全防御。从数据泄露到勒索软件感染，日益复杂的攻击正在对支持互联网的系统及其用户构成严重威胁。虽然机器学习在构建下一代防御系统方面表现出了巨大的潜力，但这些防御系统很容易受到攻击者进化和良性玩家行为变化导致的数据动态变化(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的努力来标记新数据，这对扩大规模具有挑战性。在这个项目中，研究团队将设计新的方案，以提高基于学习的防御系统的适应性和弹性，这些防御系统需要最低限度的标记能力。其核心思想是使用自我监督的学习模型，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、检测和缓解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。研究团队将首先专注于测量纵向数据上的概念漂移。该团队将把重点放在真实世界的恶意软件样本上，开发测量工具来提取和表征不同类型的概念漂移，以了解它们的模式。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性方法，以及选择漂移样本的方法，以促进有效的标记。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，以模仿攻击者未来的突变，这些将被用来在训练阶段加强防御。拟议的技术有望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未被充分代表的学生，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。

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.