Safety and security issues for Critical Infrastructures (CI) are growing as attackers increasingly adopt drones as an attack vector flying in sensitive airspace, such as airports, military bases, city centres, and crowded places. The rapid proliferation of drones for merchandise, shipping recreations activities, and other commercial applications poses severe concerns on the CI operators due to the violations and the invasions of the restricted airspaces. A cost-effective framework is needed to detect, classify and identify the presence of drones in such cases. In this paper, we demonstrate that CI operators can detect, classify and identify timely and efficiently drones (multi-copter and fixed-wings) invading no-drone zones, with an inexpensive RF-based detection framework named URANUS. Our experiments show that by using Random Forest classifier, we achieved a classification accuracy of 93.4% in the classification of one or multiple specific drones. The tracking performance achieves an accuracy with an average of MAE=0.3650, MSE=0.9254 and R2 = 0.7502. Our framework has been released as open-source, to enable the community to verify our findings and use URANUS as a ready-to-use basis for further analysis.

翻译：由于攻击者越来越多地采用无人驾驶飞机作为攻击矢量,在机场、军事基地、城市中心和拥挤地点等敏感空域飞行,攻击者越来越多地采用无人驾驶飞机作为攻击性病媒,因此,关键基础设施的安全和安保问题日益严重; 用于商品、航运娱乐活动和其他商业应用的无人驾驶飞机的迅速扩散,对驾驶员造成了严重的关切,因为侵犯和入侵了限制领空; 需要有一个成本效益高的框架,以探测、分类和识别此类情况下无人驾驶飞机的存在; 本文表明,驾驶员能够及时有效地探测、分类和识别入侵无油区的无人驾驶飞机(多机和固定翼),并采用廉价的RF探测框架,称为URANUS。我们的实验表明,通过使用随机森林分类器,我们在一种或多种特定无人驾驶飞机的分类分类中达到了93.4%的分类准确度。 跟踪工作达到准确度,平均MAE=0.36550、MSE=0.9254和R2=0.7502。 我们的框架已作为公开来源发布,使社区能够核查我们的调查结果,并使用URANUS作为现用分析的基础。