Artificial Intelligence Applications in Intrusion Detection Systems for Unmanned Aerial Vehicles

Abstract

This master thesis focuses on the cutting-edge application of AI in developing intrusion detection systems (IDS) for unmanned aerial vehicles (UAVs) in smart cities. The objective is to address the escalating problem of UAV intrusions, which pose a significant risk to the safety and security of citizens and critical infrastructure. The thesis explores the current state of the art and provides a comprehensive understanding of recent advancements in the field, encompassing both physical and network attacks.

The literature review examines various techniques and approaches employed in the development of AI-based IDS. This includes the utilization of machine learning algorithms, computer vision technologies, and edge computing. A proposed solution leveraging computer vision technologies is presented to detect and identify intruding UAVs in the sky effectively. The system employs machine learning algorithms to analyze video feeds from city-installed cameras, enabling real-time identification of potential intrusions. The proposed approach encompasses the detection of unauthorized drones, dangerous UAVs, and UAVs carrying suspicious payloads.

Moreover, the thesis introduces a Cycle GAN network for image denoising that can translate noisy images to clean images without the need for paired training data. This approach employs two generators and two discriminators, incorporating a cycle consistency loss that ensures the generated images align with their corresponding input images.

Furthermore, a distributed architecture is proposed for processing collected images using an edge-offloading approach within the UAV network. This architecture allows flying and ground cameras to leverage the computational capabilities of their IoT peers to process captured images. A hybrid neural network is developed to predict, based on input tasks, the potential edge computers capable of real-time processing. The edge-offloading approach reduces the computational burden on the centralized system and facilitates real-time analysis of network traffic, offering an efficient solution.

In conclusion, the research outcomes of this thesis provide valuable insights into the development of secure and efficient IDS for UAVs in smart cities. The proposed solution contributes to the advancement of the UAV industry and enhances the safety and security of citizens and critical infrastructure within smart cities.