AI Techniques for Malware Detection in Drone Communication and Security

Abstract

Malware detection in drone communication and security is a critical area of research given the growing use of drones in civilian and military applications. In the past few years, driven by data artificial intelligence techniques, such as Machine Learning (ML) and Deep Learning (DL) approaches, have shown promise in detecting malware by leveraging its behaviour in terms of API calls. It is anticipated that drones will play a significant part in the future linked smart cities. They will be responsible for smart city security and monitoring, transporting commodities and commerce, and acting as mobile hot points for broadband wireless access. This article includes an in-depth examination of the several ML algorithms used in malware analysis and proposed an hybrid model for the improve the performance in terms of accuracy and F1-scores. The "Hybrid With FS" model achieves the highest accuracy, nearing 80%, indicating the significant benefit of feature selection in optimizing performance.