保密通信作为物联网安全的一项核心技术近些年受到了广泛关注。无人机(UAV)由于其高机动和灵活部署等特性,被认为是提高物联网通信安全的有力手段。针对当前物联网中UAV辅助保密通信频谱利用率问题,研究了UAV和地面设备到设备(Device-to...保密通信作为物联网安全的一项核心技术近些年受到了广泛关注。无人机(UAV)由于其高机动和灵活部署等特性,被认为是提高物联网通信安全的有力手段。针对当前物联网中UAV辅助保密通信频谱利用率问题,研究了UAV和地面设备到设备(Device-to-Device, D2D)通信系统频谱共享的系统保密容量最大化,提出了一种基于块坐标下降(Block Coordinate Descent, BCD)法的频谱共享算法,该算法通过联合优化系统中发送方(UAV和地面设备对)的传输功率和无人机轨迹,在确保地面设备对和UAV能够共存的情况下最大化接收方的保密容量。具体来说,该算法通过差分凸规划(Difference of Two Convex Functions, DC)方法优化固定轨迹下的系统功率,利用连续凸优化方法解决给定发射功率的UAV轨迹优化问题。仿真结果表明,所提方法在多种参数配置下都能有效提高系统的保密容量,并且和其他算法相比有更低的功率消耗。展开更多
With the gradual popularization of 5G communications,the application of multi-antenna broadcasting technology has become widespread.Therefore,this study aims to investigate the wireless covert communication in the two...With the gradual popularization of 5G communications,the application of multi-antenna broadcasting technology has become widespread.Therefore,this study aims to investigate the wireless covert communication in the two-user cooperative multi-antenna broadcast channel.We focus on the issue that the deteriorated reliability and undetectability are mainly affected by the transmission power.To tackle this issue,we design a scheme based on beamforming to increase the reliability and undetectability of wireless covert communication in the multi-antenna broadcast channel.We first modeled and analyzed the cooperative multi-antenna broadcasting system,and put forward the target question.Then we use the SCA(successive convex approximation)algorithm to transform the target problem into a series of convex subproblems.Then the convex problems are solved and the covert channel capacity is calculated.In order to verify the effectiveness of the scheme,we conducted simulation verification.The simulation results show that the proposed beamforming scheme can effectively improve the reliability and undetectability of covert communication in multi-antenna broadcast channels.展开更多
洋葱路由(Tor, the onion route)网络加密流的关联分析是其追踪溯源的核心技术之一;针对当前基于深度学习的流关联方法存在的时间特征不可靠且初级特征表征能力不强的问题,提出了一种基于时频分析和图卷积神经网络的关联分析方法,该方...洋葱路由(Tor, the onion route)网络加密流的关联分析是其追踪溯源的核心技术之一;针对当前基于深度学习的流关联方法存在的时间特征不可靠且初级特征表征能力不强的问题,提出了一种基于时频分析和图卷积神经网络的关联分析方法,该方法使用Tor网络流量的数据包长度信息作为原始特征序列,将数据包的包长度序列通过时频分布函数映射到时频域,并进一步将其嵌入为图结构数据,同时使用图卷积神经网络提取高阶特征,最后将得到的高阶特征输入三元组网络以实现相似流量的关联。实验结果表明误报率为0.1%时,所提方法的关联准确率可达到83.4%,明显优于已有的DeepCorr和Attcorr方法。展开更多
文摘保密通信作为物联网安全的一项核心技术近些年受到了广泛关注。无人机(UAV)由于其高机动和灵活部署等特性,被认为是提高物联网通信安全的有力手段。针对当前物联网中UAV辅助保密通信频谱利用率问题,研究了UAV和地面设备到设备(Device-to-Device, D2D)通信系统频谱共享的系统保密容量最大化,提出了一种基于块坐标下降(Block Coordinate Descent, BCD)法的频谱共享算法,该算法通过联合优化系统中发送方(UAV和地面设备对)的传输功率和无人机轨迹,在确保地面设备对和UAV能够共存的情况下最大化接收方的保密容量。具体来说,该算法通过差分凸规划(Difference of Two Convex Functions, DC)方法优化固定轨迹下的系统功率,利用连续凸优化方法解决给定发射功率的UAV轨迹优化问题。仿真结果表明,所提方法在多种参数配置下都能有效提高系统的保密容量,并且和其他算法相比有更低的功率消耗。
基金supported by the National Natural Science Foundation of China(Grants No.U1836104,61772281,61702235,61801073,61931004,62072250).
文摘With the gradual popularization of 5G communications,the application of multi-antenna broadcasting technology has become widespread.Therefore,this study aims to investigate the wireless covert communication in the two-user cooperative multi-antenna broadcast channel.We focus on the issue that the deteriorated reliability and undetectability are mainly affected by the transmission power.To tackle this issue,we design a scheme based on beamforming to increase the reliability and undetectability of wireless covert communication in the multi-antenna broadcast channel.We first modeled and analyzed the cooperative multi-antenna broadcasting system,and put forward the target question.Then we use the SCA(successive convex approximation)algorithm to transform the target problem into a series of convex subproblems.Then the convex problems are solved and the covert channel capacity is calculated.In order to verify the effectiveness of the scheme,we conducted simulation verification.The simulation results show that the proposed beamforming scheme can effectively improve the reliability and undetectability of covert communication in multi-antenna broadcast channels.
文摘洋葱路由(Tor, the onion route)网络加密流的关联分析是其追踪溯源的核心技术之一;针对当前基于深度学习的流关联方法存在的时间特征不可靠且初级特征表征能力不强的问题,提出了一种基于时频分析和图卷积神经网络的关联分析方法,该方法使用Tor网络流量的数据包长度信息作为原始特征序列,将数据包的包长度序列通过时频分布函数映射到时频域,并进一步将其嵌入为图结构数据,同时使用图卷积神经网络提取高阶特征,最后将得到的高阶特征输入三元组网络以实现相似流量的关联。实验结果表明误报率为0.1%时,所提方法的关联准确率可达到83.4%,明显优于已有的DeepCorr和Attcorr方法。
