Cybersecurity Threats and Mitigation Strategies in Robotic Systems: A Frame work for Securing Autonomous Technologies

Abstract

In a world where robots and intelligent systems are becoming more and more integrated into critical infra- structure, ensuring cybersecurity has become a key challenge. In the Software Defined Networking (SDN) environment, this thesis proposes an inclusive cybersecurity architecture for robotic systems. In this re- search, we aim to develop a real time anomaly based detection system which aims to protect a robotic sys- tem from Distributed Denial of Service (DDoS) attack which is based on two machine learning techniques. The research opened with a thorough review of the existing scenario of cybersecurity in robotics. It has iden- tified the strength in technological advancement as well as real-time protection gap. After interpreting these results, the main focus of the research was to develop a very strong and scalable detection model based on ensemble learning using Random Forests and deep learning using Long Short-Term Memory (LSTM). This hybrid model has been trained and tested on a real-world SDN-based DDoS dataset from Kaggle which real- istically generates attack scenarios like TCP SYN flood, UDP flood, and Smurf. The pipeline was created and executed entirely using the modular Jupyter Notebook framework, with all data pre-processing, selection, normalisation and model integration. The dual-path plan helped in gathering static traffic features and dynamic temporal characteristics. These characteristics allow the system to recognize modern-day attacks. The performance of the model was empirically evaluated with the results showing a detection accuracy of 99.03% and precision of 99.27%. Also, AUC-ROC was 0.9994. The findings highlight the model's reliability, flexibility, and real deployment readiness in robotic environments, where safety, latencies and trust receive utmost priority. To ensure transparency and reproducibility, we also include performance metrics, confusion matrix analysis, and attack detection scenario visualizations. This research shows how important modular design is. It also shows how important cross-domain application is and that learning needs to be continuous to deal with evolv-ing threats. The framework is aligned with global cybersecurity standards and paves the way for future research in federated learning, logging on blockchains and secure-by-design protocols for robotic infra-structures. In the end, this work allows the gap between the theory and operation of robotic cyber-security to close with a contribution that is practically useful, well-documented, as well as reproducible.