SAGE：面向对抗攻击的鲁棒入侵检测的样本感知防护引擎
SAGE: Sample-Aware Guarding Engine for Robust Intrusion Detection Against Adversarial Attacks
AI总结：
针对ML-IDS易受对抗攻击且缺乏防御选择系统方法的问题，提出SAGE，集成主动学习与数据缩减，提升F1分数201%，逼近Oracle性能，计算开销降低29倍。
摘要：
物联网（IoT）的快速普及持续暴露关键安全漏洞，因此亟需开发高效且鲁棒的入侵检测系统（IDS）。基于机器学习的入侵检测系统（ML-IDS）显著提升了威胁检测能力，然而它们仍然极易受到对抗攻击的影响。尽管已提出众多防御机制以增强ML-IDS的韧性，但针对特定对抗攻击选择最有效防御的系统性方法仍然缺失。为应对这一挑战，我们先前提出了DYNAMITE，一种动态防御选择方法，通过机器学习驱动的选择机制来识别针对对抗攻击的最合适防御。在此基础之上，我们提出了SAGE（样本感知防护引擎），一种大幅改进的防御算法，它集成了主动学习与目标数据缩减。SAGE采用主动学习机制，选择性地识别最具信息量的输入样本及其对应的最优防御标签，然后利用这些样本训练一个二级学习器，负责选择最有效的防御。这种目标采样提高了计算效率，使模型在训练期间接触多样化的对抗策略，并增强了鲁棒性、稳定性和泛化能力。因此，SAGE在多个入侵检测数据集上展现出强大的预测性能，相较于最先进的防御方法，平均F1分数提升了201%。值得注意的是，SAGE将性能差距缩小至与Oracle仅差3.8%，同时将计算开销降低了高达29倍。
英文摘要：
The rapid proliferation of the Internet of Things (IoT) continues to expose critical security vulnerabilities, necessitating the development of efficient and robust intrusion detection systems (IDS). Machine learning-based intrusion detection systems (ML-IDS) have significantly improved threat detection capabilities; however, they remain highly susceptible to adversarial attacks. While numerous defense mechanisms have been proposed to enhance ML-IDS resilience, a systematic approach for selecting the most effective defense against a specific adversarial attack remains absent. To address this challenge, we previously proposed DYNAMITE, a dynamic defense selection approach that identifies the most suitable defense against adversarial attacks through an ML-driven selection mechanism. Building on this foundation, we propose SAGE (Sample-Aware Guarding Engine), a substantially improved defense algorithm that integrates active learning with targeted data reduction. It employs an active learning mechanism to selectively identify the most informative input samples and their corresponding optimal defense labels, which are then used to train a second-level learner responsible for selecting the most effective defense. This targeted sampling improves computational efficiency, exposes the model to diverse adversarial strategies during training, and enhances robustness, stability, and generalizability. As a result, SAGE demonstrates strong predictive performance across multiple intrusion detection datasets, achieving an average F1-score improvement of 201% over the state-of-the-art defenses. Notably, SAGE narrows the performance gap to the Oracle to just 3.8%, while reducing computational overhead by up to 29x.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
