INTELLIGENT ATTACK DETECTION IN ROS-BASED SYSTEMS

Abstract

The rapid adoption of robotic systems in domains such as autonomous vehicles, industrial automation, healthcare, and smart environments has increased the reliance on the Robot Operating System (ROS) as a flexible middleware framework. While ROS provides modularity and ease of integration, it also introduces significant security vulnerabilities due to its open communication architecture, lack of built-in authentication, and susceptibility to network-based attacks. These weaknesses expose ROS-based systems to various cyber threats, including message spoofing, denial-of-service (DoS) attacks, node hijacking, and data tampering, which can compromise system reliability and safety. Therefore, there is a critical need for intelligent and adaptive security mechanisms capable of detecting and mitigating such attacks in real time. This project proposes an Intelligent Attack Detection System for ROS-based Environments that leverages advanced machine learning and deep learning techniques to identify anomalous behaviors and potential cyberattacks. The system monitors ROS communication patterns, including topic messages, node interactions, and network traffic, to extract relevant features for analysis. Algorithms such as Random Forest, Support Vector Machines (SVM), and Deep Neural Networks (DNNs) are employed to classify normal and malicious activities. Additionally, the system incorporates real-time data streaming and anomaly detection mechanisms to ensure timely identification of threats. Feature engineering and data preprocessing techniques are applied to improve detection accuracy and reduce false positives. Experimental evaluation demonstrates that the proposed system achieves high detection accuracy and robustness against various attack scenarios. The intelligent model effectively identifies both known and unknown attack patterns, enhancing the overall security of ROS-based systems. The solution is scalable and adaptable to different robotic applications, making it suitable for real-world deployment. In conclusion, this research presents a proactive and intelligent approach to securing ROS environments, contributing to the development of safer and more resilient robotic systems. Future work may focus on integrating blockchain-based security and reinforcement learning for adaptive threat response.