Neurosymbolic approach for attack detection in satellite communication systems

In the context of increasing cyber threats, the pressing task becomes the implementation of new protection systems for satellite communications. The proposed article presents an innovative neurosymbolic method for attack detection that integrates the capabilities of artificial intelligence and neural networks for effective countermeasures against threats in the domain of satellite communication. The foundation of the development is the synthesis of the strengths of symbolic artificial intelligence and deep learning, enabling highly accurate recognition and neutralization of complex attacks. The architecture of the proposed system is thoroughly detailed, including its key components, mechanisms of operation, and implementation process. Analyzing data from satellite and terrestrial networks, the system's effectiveness is evaluated using machine learning methods, demonstrating significant improvements in intrusion detection compared to existing approaches. Special attention is given to the model's ability to adapt to new types of attacks, ensuring its longterm relevance and efficiency. The architecture of the chosen multilayer neural network includes a symbolic layer, designed for analyzing network input data for vulnerabilities or attacks based on a knowledge base. Experiments on datasets of attacks and vulnerabilities such as CTU-13 and STIN allow for the testing and confirmation of the high efficiency of the proposed method. Thus, this research paves the way for improving cybersecurity systems in the field of satellite communication, contributing to the creation of a mor e secure space environment.