随着汽车智能化的发展,汽车内的电子控制单元(Electronic Control Unit,ECU)数量激增,因CAN总线自身的特点,挂载在总线上的ECU缺乏必要的安全保护。为此,提出了基于物理不可克隆函数(Physical Unclonable Functions,PUF)的车载网络ECU...随着汽车智能化的发展,汽车内的电子控制单元(Electronic Control Unit,ECU)数量激增,因CAN总线自身的特点,挂载在总线上的ECU缺乏必要的安全保护。为此,提出了基于物理不可克隆函数(Physical Unclonable Functions,PUF)的车载网络ECU身份认证方案。该方案利用PUF的物理特征动态生成密钥,将身份认证划分为两个关联结合阶段,以满足车载网络实时性和安全性的双重需求。在车辆出厂前的非实时场景下,采用数字证书完成ECU间的注册。在车辆启动的实时场景下,基于认证链建立“一次一密”的快速身份认证机制。采用仿真测试对该方案的性能进行了评估,并与其他认证方案进行对比。实验结果表明,该方案可以同时满足车载网络在实时性和安全性方面的需求。展开更多
为提高毫米波多输入多输出(Multiple-Input Multiple-Output,MIMO)通信系统的安全性,针对窃听者信道状态信息(Channel State Information,CSI)未知的情况,提出了一种可重构智能表面(Reconfigurable Intelligent Surface,RIS)和人工噪声(...为提高毫米波多输入多输出(Multiple-Input Multiple-Output,MIMO)通信系统的安全性,针对窃听者信道状态信息(Channel State Information,CSI)未知的情况,提出了一种可重构智能表面(Reconfigurable Intelligent Surface,RIS)和人工噪声(Artificial Noise,AN)辅助的物理层安全传输方案。为提高系统的保密性能,在基站处最小化信息信号的发射功率,利用剩余功率对AN进行设计。具体而言,先共同设计基站处的传输预编码矩阵和RIS相移矩阵,在合法用户的服务质量(Quality of Service,QoS)约束下优化其功率分配,以获得基站处信息信号的最小发射功率;再利用剩余功率设计AN的发射协方差,并将其对准合法用户信道的零空间,从而避免了AN的有害影响。仿真结果表明,所提算法具有良好的收敛性和有效性,同时揭示了实际保密率与合法用户QoS之间的权衡关系。展开更多
个性化联邦学习因其在应对数据异质性和隐私保护方面的优势而备受关注。现有算法专注于平衡全局信息和个性化信息之间的矛盾,忽视了全局信息中的不同标签信息带来的干扰,尤其在维护单一全局头部的算法中,容易出现标签间特征冲突导致的...个性化联邦学习因其在应对数据异质性和隐私保护方面的优势而备受关注。现有算法专注于平衡全局信息和个性化信息之间的矛盾,忽视了全局信息中的不同标签信息带来的干扰,尤其在维护单一全局头部的算法中,容易出现标签间特征冲突导致的收敛困难。为此,提出一种新的算法——全局多头部联邦学习(federated learning with global multi-head,FedGMH)算法,该算法在服务器创建多个全局头部,每个头部专门处理一种标签信息,而客户端下载与本地标签相关的全局头部,从而避免无关标签信息的干扰。此外,FedGMH引入参数级聚合机制:评估头部参数重要性,并将关键参数更新为全局多头部的加权参数,以加快收敛速度并且提高准确率。在3个视觉数据集上的大量实验表明,FedGMH优于现有的先进算法。展开更多
Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remain...Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.展开更多
文摘随着汽车智能化的发展,汽车内的电子控制单元(Electronic Control Unit,ECU)数量激增,因CAN总线自身的特点,挂载在总线上的ECU缺乏必要的安全保护。为此,提出了基于物理不可克隆函数(Physical Unclonable Functions,PUF)的车载网络ECU身份认证方案。该方案利用PUF的物理特征动态生成密钥,将身份认证划分为两个关联结合阶段,以满足车载网络实时性和安全性的双重需求。在车辆出厂前的非实时场景下,采用数字证书完成ECU间的注册。在车辆启动的实时场景下,基于认证链建立“一次一密”的快速身份认证机制。采用仿真测试对该方案的性能进行了评估,并与其他认证方案进行对比。实验结果表明,该方案可以同时满足车载网络在实时性和安全性方面的需求。
文摘为提高毫米波多输入多输出(Multiple-Input Multiple-Output,MIMO)通信系统的安全性,针对窃听者信道状态信息(Channel State Information,CSI)未知的情况,提出了一种可重构智能表面(Reconfigurable Intelligent Surface,RIS)和人工噪声(Artificial Noise,AN)辅助的物理层安全传输方案。为提高系统的保密性能,在基站处最小化信息信号的发射功率,利用剩余功率对AN进行设计。具体而言,先共同设计基站处的传输预编码矩阵和RIS相移矩阵,在合法用户的服务质量(Quality of Service,QoS)约束下优化其功率分配,以获得基站处信息信号的最小发射功率;再利用剩余功率设计AN的发射协方差,并将其对准合法用户信道的零空间,从而避免了AN的有害影响。仿真结果表明,所提算法具有良好的收敛性和有效性,同时揭示了实际保密率与合法用户QoS之间的权衡关系。
文摘个性化联邦学习因其在应对数据异质性和隐私保护方面的优势而备受关注。现有算法专注于平衡全局信息和个性化信息之间的矛盾,忽视了全局信息中的不同标签信息带来的干扰,尤其在维护单一全局头部的算法中,容易出现标签间特征冲突导致的收敛困难。为此,提出一种新的算法——全局多头部联邦学习(federated learning with global multi-head,FedGMH)算法,该算法在服务器创建多个全局头部,每个头部专门处理一种标签信息,而客户端下载与本地标签相关的全局头部,从而避免无关标签信息的干扰。此外,FedGMH引入参数级聚合机制:评估头部参数重要性,并将关键参数更新为全局多头部的加权参数,以加快收敛速度并且提高准确率。在3个视觉数据集上的大量实验表明,FedGMH优于现有的先进算法。
基金This study is financed by the European Union-NextGenerationEU,through the National Recovery and Resilience Plan of the Republic of Bulgaria,Project No.BG-RRP-2.013-0001.
文摘Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.
