# A Lightweight AI-Based Approach for Drone Jamming Detection
## 2 Related Works  
Detection of jamming attacks in wireless networks has been a topic of interest for the research community for many years. A wide variety of approaches have been proposed for different types of networks, with most of the work focusing on the detection of jamming attacks in Vehicular Ad-Hoc Networks (VANETs) [5]. This section will provide an overview of some studies on the integration of terrestrial 5G/6G cellular networks with non terrestrial network (NTN), e.g., UAV/drone networks, with particular focus on the significance of AI-native solutions. Afterwards, various approaches for the detection of jamming attacks in wireless networks will be discussed. Paper [6] discusses UAV wireless infrastructure to support a flying base station or data collector in 6G integrated terrestrial and non terrestrial networks (T/NTNs). The authors provide a review of the main regulatory and standardization initiatives for integrating UAVs into the cellular network and describe several testbeds and UAV prototypes. Moreover, the paper (i) categorizes several potential advantages, such as increased capacity and coverage, on-demand networking, machine type communications, 3D mobility support and users tracking, (ii) addresses the issue of flexible use spectrum policy, and (iii) provides an accurate analysis of security concerns like GPS jamming attacks. Survey [7] highlights the limits of traditional ML models in handling Big Data related to the massive number of connected devices that rely on Cloud-based servers to deliver smart services and applications. The paper identifies federated learning (FL) as a promising approach for distributing computation to the edge of the network. In particular, after analyzing the fundamentals of the FL principles and architecture, the review shows the use of multi-access edge computing (MEC) as a suitable environment for FL algorithms. MEC enables low-latency processing for real time applications in beyond 5G/6G networks as well as in other FL enabled technologies, such as BlockChain and Network Slicing. Furthermore, the state of art for AI-empowered UAV is reviewed, with a particular focus on FL applications including (i) preserving the privacy of sensed data in UAV-enabled mobile crowd-sensing, (ii) exploiting and predicting the data on air quality acquired by an UAV swarm, integrating data collected by different agencies, (iii) reducing E2E latency and overhead at the edge, thus improving the battery life of UAV, (iv) reducing the communication cost among multiple UAVs and a ground data-fusion centre in the context of image classification. The authors of [8] provide a review of the current trends and state of the art of Unmanned Autonomous Intelligent Systems (UAISs) in the context of 5G/6G NTN communications. Integration of NTN with 5G/6G systems trough the use of UAVs and other low-altitude aerial platforms is addressed, emphasizing the importance of artificial intelligence (AI) as UAIS becomes a part of the future integrated T/NTN networks. The use of deep learning and reinforcement learning algorithms combined with MEC and distributed computation offloading to offer low latency paths is analyzed. These technologies enable practical applications, such as object detection and recognition, decision-making, and autonomous navigation. Shifting our focus to jamming attacks, the authors of [9] analyze a wide range of supervised Machine Learning approaches for the detection of Jamming attacks, including Random Forest, Support Vector Machines and Neural Networks. The authors show that Random Forests are able to achieve a high detection rate against jamming attacks. The authors of [10] on the other hand, propose the use of an unsupervised approach in the context of VANETs and test its performance in a variety of real-world scenarios. The authors of [11] compare the use of Multi-Layer Perceptrons (MLPs) and Decision Trees for the detection of jamming attacks in drone networks, showing that both methods are able to achieve good detection performance.