# A Lightweight AI-Based Approach for Drone Jamming Detection
## 7 Conclusions  
In this article, we analyzed the use of the Isolation Forest unsupervised machine learning algorithm for the detection of jamming attacks. We compared the performance of the base IF model against a version specifically tuned to detect jamming attacks. During the tuning phase, we paid particular attention to the computational impact of each parameter and tried to find the right balance between performance and efficiency. We then proposed the integration of a Majority Rule module to reduce the number of false positives caused by the inherent noise of the dataset. The MR module was tuned to provide the best performance and proved effective in mitigating the problem of false positives, while still  
Figure 18: Classification results of the proposed solution. The model is able to correctly classify periodic jamming and normal traffic with high accuracy. maintaining a linear complexity in classification time. We tested the model against a dataset that included normal traffic, constant jamming, and periodic jamming attacks, with an unbalanced number of normal traffic samples and jamming attack samples to better reflect a real-world scenario. We compared the performance of the proposed solution against the solution proposed by [12] and found that our solution outperformed the existing solution in both the detection of periodic jamming and the classification of normal traffic, while retaining the same performance against constant jamming attacks. In a real world scenario, a 6G drone integrating the proposed solution would periodically run the tuning phase of the algorithm, selecting the best hyperparameters for the current environment. This could happen periodically or whenever there is a change in the environment caused by the drone moving to a different location. The dynamic tuning of the model would ensure that the parameters of the model are always optimized for the current environment, providing the best possible performance. After the tuning phase, the drone would periodically sample incoming signals and classify them using the MR-integrated IF. If the model detects a jamming attack, the drone could activate built-in countermeasures like Frequency Hopping or Direct Sequence Spread Spectrum to mitigate the effects of the attack. Future research could explore the model's performance against a broader range of jamming attacks types, including smart, reactive and random jamming, and evaluate the impact of real world noise on its accuracy. Deploying the model on a drone would provide opportunity for real-world testing and valuable insights for further refinement. As discussed in Section 3.5, devices integrated into 6G networks are expected to have a higher degree of sensing capabilities compared to current devices [3]. Thanks to the ability of the Isolation Forest to work on high dimensional data, the model could easily be expanded to include more classification features, which would improve its situational awareness and performance. This could play a crucial role in addressing interference or signal shadowing patterns. Furthermore, future work could include the investigation of the impact of adversarial settings, as this could offer insights into real-world performance as well as demonstrate adaptability to emerging 6G technologies. Conceptualization, F.C. and S.C.; methodology, S.C.; software, S.C.; validation, F.C. and L.P.; writing--original draft preparation, S.C.; writing--review and editing, F.C. and L.P.; visualization, S.C.; supervision, F.C. and L.P. All authors have read and agreed to the published version of the manuscript. This work was partially supported by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, in partnership with the project \"Telecommunications of the Future\" (PE00000001--program \"RESTART\"). Additionally, this article publication is based upon work from COST Action CA22168 for \"Physical Layer Security for Trustworthy and Resilient 6G Systems (6G-PHYSEC)\", supported by COST (European Cooperation in Science and Technology).