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Investigating Machine Learning Attacks on Financial Time Series Models

Gallagher, Michael; Pitropakis, Nikolaos; Chrysoulas, Christos; Papadopoulos, Pavlos; Mylonas, Alexios; Katsikas, Sokratis


Michael Gallagher

Alexios Mylonas

Sokratis Katsikas


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 classification. The results show that the architecture was susceptible to these attacks and that, in their face, the classifier accuracy was significantly impacted.


Gallagher, M., Pitropakis, N., Chrysoulas, C., Papadopoulos, P., Mylonas, A., & Katsikas, S. (2022). Investigating Machine Learning Attacks on Financial Time Series Models. Computers and Security, 123,

Journal Article Type Article
Acceptance Date Sep 23, 2022
Online Publication Date Sep 28, 2022
Publication Date 2022-12
Deposit Date Sep 28, 2022
Publicly Available Date Nov 1, 2022
Journal Computers & Security
Print ISSN 0167-4048
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 123
Keywords Adversarial Machine Learning, Neural Networks, Financial Time-Series Models
Public URL


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