“Deep learning for anomaly detection in cyber-physical systems” addresses the challenges of detecting abnormalities in cyber-physical systems (CPS). CPS, which integrate computational and physical processes, are increasingly vulnerable to cyber threats. Traditional intrusion detection systems (IDS) struggle to protect these systems due to their complexity and the evolving nature of threats.
Deep learning techniques have shown promise in improving IDS for CPS. They can learn from large amounts of data, identify complex patterns, and adapt to new situations. However, there are limitations. Deep learning models can be difficult to interpret, and they require substantial amounts of data and computing resources.
The paper proposes a new approach to anomaly detection in CPS using a combination of deep learning and other machine learning techniques. This hybrid approach aims to overcome the limitations of deep learning models and improve the effectiveness of IDS. The proposed system is tested on a real-world CPS, demonstrating its potential to enhance security in these critical systems.
While this work represents a significant step forward in CPS security, further research is needed to refine the approach and broaden its applicability. Future work should focus on improving the interpretability of deep learning models and reducing their resource requirements. The development of more sophisticated IDS that can adapt to the evolving threat landscape is also a priority.
Go to source article: https://arxiv.org/abs/2402.04615