TrojanTime：针对时间序列分类的后门攻击
TrojanTime: Backdoor Attacks on Time Series Classification
AI总结：
提出TrojanTime两步训练算法，在无法访问训练数据下通过伪数据集生成与logits对齐实现时间序列分类后门攻击，并设计防御性遗忘策略降低攻击成功率。
摘要：
时间序列分类（TSC）极易受到后门攻击，构成了严重的安全威胁。现有方法主要集中于训练阶段的数据投毒，通过设计复杂的触发器来提高隐蔽性和攻击成功率（ASR）。然而，在实际场景中，攻击者往往面临无法访问训练数据的限制。此外，在无法获取数据的情况下，模型既要保持对干净测试数据的泛化能力，又要对投毒输入保持脆弱性，这是一大挑战。为了解决这些挑战，我们提出了TrojanTime，一种新颖的两步训练算法。在第一阶段，我们利用外部任意数据集，通过目标对抗攻击生成一个伪数据集。随后，干净模型在该伪数据集及其投毒版本上进行持续训练。为确保泛化能力，第二阶段采用了一种精心设计的训练策略，结合了logits对齐和batch norm冻结。我们在来自不同领域的UCR基准数据集中，使用五种类型的触发器在四种TSC架构上对TrojanTime进行了评估。结果表明，TrojanTime在执行后门攻击的同时能有效保持干净准确率。最后，为了缓解这一威胁，我们提出了一种防御性遗忘策略，该策略在保持干净准确率的同时有效降低了ASR。
英文摘要：
Time Series Classification (TSC) is highly vulnerable to backdoor attacks, posing significant security threats. Existing methods primarily focus on data poisoning during the training phase, designing sophisticated triggers to improve stealthiness and attack success rate (ASR). However, in practical scenarios, attackers often face restrictions in accessing training data. Moreover, it is a challenge for the model to maintain generalization ability on clean test data while remaining vulnerable to poisoned inputs when data is inaccessible. To address these challenges, we propose TrojanTime, a novel two-step training algorithm. In the first stage, we generate a pseudo-dataset using an external arbitrary dataset through target adversarial attacks. The clean model is then continually trained on this pseudo-dataset and its poisoned version. To ensure generalization ability, the second stage employs a carefully designed training strategy, combining logits alignment and batch norm freezing. We evaluate TrojanTime using five types of triggers across four TSC architectures in UCR benchmark datasets from diverse domains. The results demonstrate the effectiveness of TrojanTime in executing backdoor attacks while maintaining clean accuracy. Finally, to mitigate this threat, we propose a defensive unlearning strategy that effectively reduces the ASR while preserving clean accuracy.
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。
