A Real-Time Intrusion Detection System for Enhancing Cybersecurity in Robotic Systems

Background: The increasing integration of robotic systems across various sectors has highlighted the critical need for robust cybersecurity measures to safeguard these systems against cyber threats. Objective: This research presents a novel Real-Time Intrusion Detection System (IDS) framework specifically designed to enhance the cybersecurity of robotic systems. Methods: The proposed IDS framework monitors network traffic and continuously identifies potential threats in real time. A testbed is set up using an AlphaBot robotic device and a server machine to perform experiments under both normal and attack conditions. Network traffic data is captured in real-time using tools like Wireshark, generating raw datasets from actual data exchanges between the robotic device and the server. The dataset undergoes preprocessing, including feature extraction, data cleaning, and normalization. This processed dataset is then used to train machine learning algorithms, such as Decision Trees, K-Nearest Neighbors, and Random Forest, designed to identify patterns distinguishing between normal and malicious activities. Results: The IDS framework is tested on the AlphaBot robotic device and server machine, demonstrating effective results in real-world conditions. The system achieved an accuracy rate of 96.61% in distinguishing between normal and attack traffic, highlighting its robustness and practicality. Conclusion: The proposed real-time IDS framework shows promise in enhancing the cybersecurity of robotic systems by effectively identifying potential threats in real time.