研究评估在线 AutoML 管线中 IoT 网络投毒攻击对抗训练防御效果
Online AutoML: Evaluating Poisoning Attacks on Adversarial Training Defense Strategy in IoT Networks
给做 IoT 安全的人：论文实测了 5 种流式学习器被投毒时的表现，AT-SRP 防标签翻转 F1 到 0.904，方法可复现。
该研究针对 IoT 网络流式场景，用在线 AutoML 管线评估对抗训练（AT）对标签翻转和噪声注入两类投毒攻击的防御效果。攻击施加在 5 个流式学习器上：Hoeffding Tree、Leveraging Bagging、Adaptive Random Forest、Hoeffding Adaptive Tree 和 Streaming Random Patches。在最强投毒率 PR = 1.0 下，AT-SRP 对标签翻转攻击取得最高 F1 分数 0.904，AT-LB 对噪声注入攻击取得最高 F1 分数 0.933。研究还结合多种漂移检测方法进行滚动准确率和预评估。
Online AutoML: Evaluating Poisoning Attacks on Adversarial Training Defense Strategy in IoT Networks
Machine learning (ML)-powered poisoning attack vectors are adversarial maneuvers whereby an attacker intentionally inserts, corrupts, or alters training data to distort an ML model's learning process. The objective is to diminish model efficacy, instill biases, induce misclassifications, or include concealed backdoors that may be attacked during implementation. In streaming contexts, poisoning attacks pose significant risks since models perpetually update based on incoming streams of data. An assailant may incrementally introduce harmful samples into this data stream, leading the model to assimilate erroneous features over time without timely identification. Therefore, this study is aimed at evaluating the efficacy of the adversarial training (AT) defense approach against poisoning attacks (label flip and noise injection) using an online AutoML pipeline for Internet of Things (IoT) networks. Specifically, poisoning attacks (label flip and noise injection) were applied to streaming-capable AutoML learners (Hoeffding Tree (HT), Leveraging Bagging (LB), Adaptive Random Forest (ARF), Hoeffding Adaptive Tree (HAT), and Streaming Random Patches (SRP)). Under the strongest poisoning rate (PR = 1.0), AT-SRP achieved the highest F1-score against label flip poisoning (0.904), while AT-LB achieved the highest F1-score against noise-injection poisoning (0.933). Finally, several drift detection methods were used for rolling accuracy and prequential evaluation.
