  Robot systems are increasingly integrating into numerous avenues of modern
life. From cleaning houses to providing guidance and emotional support, robots
now work directly with humans. Due to their far-reaching applications and
progressively complex architecture, they are being targeted by adversarial
attacks such as sensor-actuator attacks, data spoofing, malware, and network
intrusion. Therefore, security for robotic systems has become crucial. In this
paper, we address the underserved area of malware detection in robotic
software. Since robots work in close proximity to humans, often with direct
interactions, malware could have life-threatening impacts. Hence, we propose
the RoboMal framework of static malware detection on binary executables to
detect malware before it gets a chance to execute. Additionally, we address the
great paucity of data in this space by providing the RoboMal dataset comprising
controller executables of a small-scale autonomous car. The performance of the
framework is compared against widely used supervised learning models: GRU, CNN,
and ANN. Notably, the LSTM-based RoboMal model outperforms the other models
with an accuracy of 85% and precision of 87% in 10-fold cross-validation, hence
proving the effectiveness of the proposed framework.
