针对现有的DDoS(distributed denial of service)攻击检测模型面临大量数据时,呈现出检测效率低的问题。为适应当前网络环境,通过研究DDoS攻击检测模型、提取流量特征、计算攻击密度,提出一种基于融合稀疏注意力机制的DDoS攻击检测模型G...针对现有的DDoS(distributed denial of service)攻击检测模型面临大量数据时,呈现出检测效率低的问题。为适应当前网络环境,通过研究DDoS攻击检测模型、提取流量特征、计算攻击密度,提出一种基于融合稀疏注意力机制的DDoS攻击检测模型GVBNet(global variable block net),使用攻击密度自适应计算稀疏注意力。利用信息熵以及信息增益分析提取攻击流量的连续字节作为特征向量,通过构建基于GVBNet的网络模型在两种数据集上进行训练。实验结果表明,该方法具有良好的识别效果、检测速度以及抗干扰能力,在不同的环境下具有应用价值。展开更多
This paper focuses on the task of few-shot 3D point cloud semantic segmentation.Despite some progress,this task still encounters many issues due to the insufficient samples given,e.g.,incomplete object segmentation an...This paper focuses on the task of few-shot 3D point cloud semantic segmentation.Despite some progress,this task still encounters many issues due to the insufficient samples given,e.g.,incomplete object segmentation and inaccurate semantic discrimination.To tackle these issues,we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity,which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks(CapsNets)in the embedding network.Concretely,the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature representations,which capture the relationships between object parts and their wholes.Secondly,we designed a multi-prototype enhancement module to enhance the prototype discriminability.Specifically,the single-prototype enhancement mechanism is expanded to the multi-prototype enhancement version for capturing rich semantics.Besides,the shot-correlation within the category is calculated via the interaction of different samples to enhance the intra-category similarity.Ablation studies prove that the involved part-whole relations and proposed multi-prototype enhancement module help to achieve complete object segmentation and improve semantic discrimination.Moreover,under the integration of these two modules,quantitative and qualitative experiments on two public benchmarks,including S3DIS and ScanNet,indicate the superior performance of the proposed framework on the task of 3D point cloud semantic segmentation,compared to some state-of-the-art methods.展开更多
基金This work is supported by the National Natural Science Foundation of China under Grant No.62001341the National Natural Science Foundation of Jiangsu Province under Grant No.BK20221379the Jiangsu Engineering Research Center of Digital Twinning Technology for Key Equipment in Petrochemical Process under Grant No.DTEC202104.
文摘This paper focuses on the task of few-shot 3D point cloud semantic segmentation.Despite some progress,this task still encounters many issues due to the insufficient samples given,e.g.,incomplete object segmentation and inaccurate semantic discrimination.To tackle these issues,we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity,which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks(CapsNets)in the embedding network.Concretely,the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature representations,which capture the relationships between object parts and their wholes.Secondly,we designed a multi-prototype enhancement module to enhance the prototype discriminability.Specifically,the single-prototype enhancement mechanism is expanded to the multi-prototype enhancement version for capturing rich semantics.Besides,the shot-correlation within the category is calculated via the interaction of different samples to enhance the intra-category similarity.Ablation studies prove that the involved part-whole relations and proposed multi-prototype enhancement module help to achieve complete object segmentation and improve semantic discrimination.Moreover,under the integration of these two modules,quantitative and qualitative experiments on two public benchmarks,including S3DIS and ScanNet,indicate the superior performance of the proposed framework on the task of 3D point cloud semantic segmentation,compared to some state-of-the-art methods.
