On Poisoning Attacks and Defenses for LSTM Time Series Prediction Models: Speed Prediction as an Example

Yi Yu Chen*, Hui Nien Hung, Shun Ren Yang, Chia Cheng Yen, Phone Lin

*此作品的通信作者

研究成果: Conference contribution同行評審

摘要

The Long Short-Term Memory (LSTM) model has significantly improved time series prediction accuracy, but also brought forth concerns regarding reliability and security with its widespread adoption, particularly in the context of poisoning attacks. While there is substantial research on attacks and defenses for LSTM models, there's limited focus on LSTM time series prediction models. In this paper, we propose an arithmetic-based poisoning attack methodology for a demonstrative LSTM time series speed prediction model. Furthermore, we employ the 'red team/blue team exercises' commonly used in network security to develop defense strategies using support vector machine and linear regression analysis methods. Through the system-level simulation experiments, we verify the effectiveness of our proposed methodology. Our experiment results indicate that, regarding attacks, our methodology can identify the optimal attacks for the representative road segments. As for defenses, we demonstrate that the defended model's performance is close to the real model's performance.

原文English
主出版物標題20th International Wireless Communications and Mobile Computing Conference, IWCMC 2024
發行者Institute of Electrical and Electronics Engineers Inc.
頁面610-615
頁數6
ISBN(電子)9798350361261
DOIs
出版狀態Published - 2024
事件20th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2024 - Hybrid, Ayia Napa, 塞浦路斯
持續時間: 27 5月 202431 5月 2024

出版系列

名字20th International Wireless Communications and Mobile Computing Conference, IWCMC 2024

Conference

Conference20th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2024
國家/地區塞浦路斯
城市Hybrid, Ayia Napa
期間27/05/2431/05/24

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