Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptible to security and privacy threats due to hardware and architectural issues. Although small drones hold promise for expansion in both civil and defense sectors, they have safety, security, and privacy threats. Addressing these challenges is crucial to maintaining the security and uninterrupted operations of these drones. In this regard, this study investigates security, and preservation concerning both the drones and Internet of Drones (IoD), emphasizing the significance of creating drone networks that are secure and can robustly withstand interceptions and intrusions. The proposed framework incorporates a weighted voting ensemble model comprising three convolutional neural network (CNN) models to enhance intrusion detection within the network. The employed CNNs are customized 1D models optimized to obtain better performance. The output from these CNNs is voted using a weighted criterion using a 0.4, 0.3, and 0.3 ratio for three CNNs, respectively. Experiments involve using multiple benchmark datasets, achieving an impressive accuracy of up to 99.89% on drone data. The proposed model shows promising results concerning precision, recall, and F1 as indicated by their obtained values of 99.92%, 99.98%, and 99.97%, respectively. Furthermore, cross-validation and performance comparison with existing works is also carried out. Findings indicate that the proposed approach offers a prospective solution for detecting security threats for aerial systems and satellite systems with high accuracy.
针对近红外光下采集的指静脉图像存在局部像素相似性强、单一方向识别效果差的问题,提出模板投票和多方向融合的指静脉识别方法。首先,提出基于模板投票的局部三值模式(template voting local three pattern,TVTP),充分利用局部多邻域点的信息,减少局部像素相似性;其次,指静脉图像中含有丰富的方向特征信息,提出多方向编码(multi-directional coding,MDC),获取图像中具有辨别力的方向特征,加强不同方向特征之间的鲁棒性,解决单一方向识别率差的问题;最后,利用分块直方图统计特征,并使用协同表示(collaborative representation,CR)进行分类,提高识别效率。实验结果证明,所提方法在SDUMLA数据集、USM数据集和THU-FVFDT2数据集上的识别率分别达到99.32%、99.73%和99.75%,与其他经典和新颖算法相比,不仅取得了更好的识别效果,还能同时满足实时性要求,具有应用价值。
