Investigating machine learning attacks on financial time series models

Machine learning and Artificial Intelligence (AI) already support human decision-making and complement professional roles, and are expected in the future to be sufficiently trusted to make autonomous decisions. To trust AI systems with such tasks, a high degree of confidence in their behaviour is needed. However, such systems can make drastically different decisions if the input data is modified, in a way that would be imperceptible to humans. The field of Adversarial Machine Learning studies how this feature could be exploited by an attacker and the countermeasures to defend against them. This work examines the Fast Gradient Signed Method (FGSM) attack, a novel Single Value attack and the Label Flip attack on a trending architecture, namely a 1-Dimensional Convolutional Neural Network model used for time series classifi-cation. The results show that the architecture was susceptible to these attacks and that, in their face, the classifier accuracy was significantly impacted.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Automated summary

Machine learning and Artificial Intelligence (AI) are already assisting human decision-making and have the potential to make autonomous decisions in the future. However, adversarial machine learning attacks can significantly impact the accuracy of these systems due to their sensitivity to modified input data.