From 41e0878006ba32a48bc78ae9e0c7fc5985da1595 Mon Sep 17 00:00:00 2001 From: Yael Mathov Date: Tue, 29 Jun 2021 15:16:34 +0300 Subject: [PATCH] More info to the readme file --- README.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 03dda42..492bfea 100644 --- a/README.md +++ b/README.md @@ -18,9 +18,15 @@

- The code for the paper "Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders" + Crafting a targeted universal adversarial perturbation against the alpha model of an algorithmic trading system.

+## About the Project +This repository contains the code for the ECML PKDD 2021 manuscript [Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders](https://arxiv.org/abs/2010.09246). Our study presents a realistic scenario in which an attacker influences algorithmic trading systems using adversarial learning techniques to manipulate the input data stream in real-time. +The attacker creates a targeted universal adversarial perturbation (TUAP) that is agnostic to the target model and time of use, which remains imperceptible when added to the input stream. + +Our code reads and processes the data from the [S&P 500 Intraday](https://www.kaggle.com/nickdl/snp-500-intraday-data) dataset and divides it into a set for training the alpha models, a set for crafting TUAPs, and six test sets to evaluate the attack. The training set is used to train three alpha models. Then, we use the TUAP set to craft a universal adversarial perturbation that can fool the target alpha models and evaluate the perturbations' performance. Finally, we also explore various mitigation methods. Additional information is available in the paper. + ## Citation ``` @article{nehemya2020bots,