提出两种新攻击方法，可让金融时间序列预测的走势翻倍
Targeted Manipulation: Slope-Based Attacks on Financial Time-Series Data
- 基于斜率设计攻击，直接操纵模型预测趋势
- 使预测斜率翻倍，且能绕过检测机制
- 适合关注模型安全与全链路防护的研究者
针对深度学习模型的对抗攻击在图像领域已深入研究，但在时间序列尤其是金融数据预测领域仍较少。本文在N-HiTS模型基础上，提出两种新的斜率攻击方法——通用斜率攻击与最小二乘斜率攻击，可使预测趋势斜率翻倍。相比正常预测，这两种方法能有效绕过4层CNN构成的判别器，使其特异性降至28%、准确率降至57%。此外，该攻击被集成至GAN架构中，用于生成逼真的合成数据并欺骗模型。最后，本文还设计了一种样本恶意软件，可注入模型推理库实施攻击，表明机器学习安全需兼顾模型与整个系统管道。
原文摘要 · Abstract (English)
A common method of attacking deep learning models is through adversarial attacks, which occur when an attacker specifically modifies the input of a model to produce an incorrect result. Adversarial attacks have been deeply investigated in the image domain; however, there is less research in the time-series domain and very little for forecasting financial data. To address these concerns, this study aims to build upon previous research on adversarial attacks for time-series data by introducing two new slope-based methods aimed to alter the trends of the predicted stock forecast generated by an N-HiTS model. Compared to the normal N-HiTS predictions, the two new slope-based methods, the General Slope Attack and Least-Squares Slope Attack, can manipulate N-HiTS predictions by doubling the slope. These new slope attacks can bypass standard security mechanisms, such as a discriminator that filters real and perturbed inputs, reducing a 4-layered CNN's specificity to 28% and accuracy to 57%. Furthermore, the slope based methods were incorporated into a GAN architecture as a means of generating realistic synthetic data, while simultaneously fooling the model. Finally, this paper also proposes a sample malware designed to inject an adversarial attack in the model inference library, proving that ML-security research should not only focus on making the model safe, but also securing the entire pipeline.
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