Investigating Machine Learning Attacks on Financial Time Series Models

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

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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.
Original languageEnglish
Article number102933
Number of pages17
JournalComputers and Security
Early online date28 Sept 2022
Publication statusPublished - 31 Dec 2022


  • Adversarial machine learning
  • financial time-series models
  • neural networks


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