DCWM-LSTM: A Novel Attack Detection Framework for Robotic Arms

Abstract

Robotic systems have become integrated components across various industries, offering improved operational efficiency and automation. However, this wide usage area makes robotic systems susceptible to cyberattacks and security vulnerabilities. Replay and subscriber flood attacks pose significant challenges to the integrity and security of robotic arms. In response to these challenges, this study proposes a new approach that leverages deep learning techniques for attack detection. We present a new framework, LSTM-based Dynamic Compound Weight Mechanism (DCWM), designed to identify cyberattacks targeting robotic arms effectively. In addition, we introduce a new feature reduction method in the preprocessing module. Our methodology is evaluated through ACC and MSE measurements. Compared to simple LSTM models, our proposed framework achieves a significant enhancement in accuracy, with performance gains observed across both replay and subscriber flood attack scenarios. Specifically, the DCWM-LSTM model exhibits an increase in accuracy from 88% to 92% for replay attacks and from 93% to 97% for subscriber flood attacks. These findings underscore the effectiveness and robustness of the DCWM-LSTM framework as a practical solution for detecting and mitigating cyber threats against robotic systems. By enhancing the security posture of robotic arms, our approach contributes to safeguarding critical infrastructure and ensuring the continued reliability and safety of automated processes in diverse industrial settings.