# A Lightweight AI-Based Approach for Drone Jamming Detection
## 1 Introduction  
The shift from 4G to 5G has been a generational leap and has revolutionized connectivity. Despite 5G still being in its early stages of adoption at the time of writing [1], the research community is already working on the next generation of wireless communication technologies, namely 6G. Sixth-generation (6G) technology is expected to enable a wide variety of new use cases, thanks to massive coverage as well as improvements in both data rates and latency, especially for IoT domains [2]. All of these advancements will in turn bring forth new security challenges specific to those applications.  
Beginning with Release 17, 3 GPP introduced the wireless infrastructure UAV (WI UAV) that, thanks to its controlled mobility, would provide the flexibility required by a dynamic and massive-access network for ubiquitous connectivity. Release 18, known as \"6G and beyond drones\", builds upon this vision by introducing AI as a means to achieve fully connected sky and marine drone communications for a global Internet of Drones (IoD).  
One of the main differences between 5G and 6G technology will be a much deeper integration with artificial intelligence (AI). While Software-Defined Networks (SDN) have played a key role in improving the efficiency and security of 5G networks, 6G is expected to take this a step further, with the networks being designed to be AI native from the ground up. This is what the authors of [3] define as the shift from _Softwarization_ to _Intelligization_.  
AI integration in 6G networks will greatly strengthen the security of the network against potential threats. By leveraging diagnostic analytics, a collection of insights into the status of the network, security teams will be able to train specific AI models to detect and respond to security threats in real-time.  
In this paper, we focus on a specific aspect of the security of 6G networks, namely we provide a lightweight edge-based machine learning approach for the detection of jamming attacks in networks of drones. We conceptualize a scenario where a drone is subject to constant and periodic jamming and has to rely on an internal AI model to detect the attack and apply the appropriate countermeasures.  
In the future, IoT devices are expected to come equipped with AI-specific chips that will allow them to run AI models directly on the edge, without the need to offload computation to a central server. This approach, which aligns with the context-aware autonomic computing and communications paradigm [4], is especially useful in the case of jamming attacks, where the communication between the device and the server is disrupted. Our solution is designed to run on a resource-constrained IoT device like a drone and to be lightweight, both in terms of memory usage and computational power. Despite the low resource requirements, the proposed approach is able to detect both constant and periodic jamming attacks with a high degree of accuracy, as well as correctly classify normal traffic. The paper is organized in five sections:  
1. **Topic overview:** General overview of the benefits of drone integration in 6G networks, analysis of jamming attacks: types, mitigation and detection. Discussion of the advantages provided by an edge AI approach for jamming detection.
2. **System Model:** Description of the analyzed scenario, along with the dataset, algorithms, and evaluation metrics chosen for the tests.
3. **Model Performance:** Explanation of the testing methodology, including model tuning, testing phases, and detailed description of the performance of the model.
4. **Discussion:** Analysis of the results from the previous section and the advantages of the proposed approach.
5. **Conclusions:** Overview of how the proposed solution applies to real-world scenarios, with a discussion on potential future research directions.