The existing recommendation algorithms have lower robustness in facing of shilling attacks. Considering this problem, we present a robust recommendation algorithm based on kernel principal component analysis and fuzzy...The existing recommendation algorithms have lower robustness in facing of shilling attacks. Considering this problem, we present a robust recommendation algorithm based on kernel principal component analysis and fuzzy c-means clustering. Firstly, we use kernel principal component analysis method to reduce the dimensionality of the original rating matrix, which can extract the effective features of users and items. Then, according to the dimension-reduced rating matrix and the high correlation characteristic between attack profiles, we use fuzzy c-means clustering method to cluster user profiles, which can realize the effective separation of genuine profiles and attack profiles. Finally, we construct an indicator function based on the attack detection results to decrease the influence of attack profiles on the recommendation, and incorporate it into the matrix factorization technology to design the corresponding robust recommendation algorithm. Experiment results indicate that the proposed algorithm is superior to the existing methods in both recommendation accuracy and robustness.展开更多
无线光通信网络的隐蔽窃听攻击具有高度的隐蔽性和复杂性,其中包含的复杂数据模式和特征,加大了无线光通信网络隐蔽窃听攻击检测难度。故提出无线光通信网络隐蔽窃听攻击自适应检测研究。采用图信号处理方法全面监测无线光通信网络,捕...无线光通信网络的隐蔽窃听攻击具有高度的隐蔽性和复杂性,其中包含的复杂数据模式和特征,加大了无线光通信网络隐蔽窃听攻击检测难度。故提出无线光通信网络隐蔽窃听攻击自适应检测研究。采用图信号处理方法全面监测无线光通信网络,捕捉异常信号范围;利用人工智能技术识别隐蔽窃听攻击特征;建立基于混合核最小二乘支持向量机(hybridkernel least-squares support vector machine,HKLSSVM)的窃听攻击检测模型,通过引入混合核函数将数据映射到更高维的特征空间中,识别出的隐蔽窃听攻击特征,并通过鲸鱼提升算法选择最优的惩罚参数和内核参数,实现无线光通信网络隐蔽窃听攻击自适应检测。实验结果表明,所提方法能准确获取异常信号范围和异常信号,在保证计算稳定性的同时,提高攻击检测性能。展开更多
根据电力系统的各种量测信息,智能电网采用状态估计得出电网当前的运行状态,因此精确的状态估计对维持智能电网的合法操作至关重要。虚假数据注入攻击可以篡改由数据采集与监控系统采集到的测量信息,对电网状态估计造成安全威胁。现有...根据电力系统的各种量测信息,智能电网采用状态估计得出电网当前的运行状态,因此精确的状态估计对维持智能电网的合法操作至关重要。虚假数据注入攻击可以篡改由数据采集与监控系统采集到的测量信息,对电网状态估计造成安全威胁。现有多数虚假数据注入攻击是基于已知电网拓扑结构,而在未知电网拓扑结构的情况下构造高性能的虚假数据注入攻击更具有现实意义。提出一种盲在线虚假数据注入攻击方法,采用核主成分分析（kernel principal component analysis,KPCA）在尽可能多的保留数据内部非线性关系的情况下,把测量数据向量通过核函数投影到高维空间,在高维空间对测量数据进行线性变换,因此可求得近似电网拓扑结构矩阵,并且该方法只需要使用少量的测量值便可构造在线攻击。最后在IEEE 14节点和118节点标准测试系统中进行大量仿真实验,并与完美攻击、随机攻击和其他盲攻击进行比较分析,验证了所提攻击方法的有效性。展开更多
基金Supported by the Scientific Research Foundation of Liaoning Provincial Education Department(L2015240)the National Natural Science Foundation of China(61379116,61503169)the Joint Fund of the Science and Technology Department of Liaoning Province(20170540448)
文摘The existing recommendation algorithms have lower robustness in facing of shilling attacks. Considering this problem, we present a robust recommendation algorithm based on kernel principal component analysis and fuzzy c-means clustering. Firstly, we use kernel principal component analysis method to reduce the dimensionality of the original rating matrix, which can extract the effective features of users and items. Then, according to the dimension-reduced rating matrix and the high correlation characteristic between attack profiles, we use fuzzy c-means clustering method to cluster user profiles, which can realize the effective separation of genuine profiles and attack profiles. Finally, we construct an indicator function based on the attack detection results to decrease the influence of attack profiles on the recommendation, and incorporate it into the matrix factorization technology to design the corresponding robust recommendation algorithm. Experiment results indicate that the proposed algorithm is superior to the existing methods in both recommendation accuracy and robustness.
文摘无线光通信网络的隐蔽窃听攻击具有高度的隐蔽性和复杂性,其中包含的复杂数据模式和特征,加大了无线光通信网络隐蔽窃听攻击检测难度。故提出无线光通信网络隐蔽窃听攻击自适应检测研究。采用图信号处理方法全面监测无线光通信网络,捕捉异常信号范围;利用人工智能技术识别隐蔽窃听攻击特征;建立基于混合核最小二乘支持向量机(hybridkernel least-squares support vector machine,HKLSSVM)的窃听攻击检测模型,通过引入混合核函数将数据映射到更高维的特征空间中,识别出的隐蔽窃听攻击特征,并通过鲸鱼提升算法选择最优的惩罚参数和内核参数,实现无线光通信网络隐蔽窃听攻击自适应检测。实验结果表明,所提方法能准确获取异常信号范围和异常信号,在保证计算稳定性的同时,提高攻击检测性能。
文摘根据电力系统的各种量测信息,智能电网采用状态估计得出电网当前的运行状态,因此精确的状态估计对维持智能电网的合法操作至关重要。虚假数据注入攻击可以篡改由数据采集与监控系统采集到的测量信息,对电网状态估计造成安全威胁。现有多数虚假数据注入攻击是基于已知电网拓扑结构,而在未知电网拓扑结构的情况下构造高性能的虚假数据注入攻击更具有现实意义。提出一种盲在线虚假数据注入攻击方法,采用核主成分分析（kernel principal component analysis,KPCA）在尽可能多的保留数据内部非线性关系的情况下,把测量数据向量通过核函数投影到高维空间,在高维空间对测量数据进行线性变换,因此可求得近似电网拓扑结构矩阵,并且该方法只需要使用少量的测量值便可构造在线攻击。最后在IEEE 14节点和118节点标准测试系统中进行大量仿真实验,并与完美攻击、随机攻击和其他盲攻击进行比较分析,验证了所提攻击方法的有效性。
