Abstract Accurate aerodynamic models are the basis of flight simulation and control law design. Mathematically modeling unsteady aerodynamics at high angles of attack bears great difficulties in model structure determ...Abstract Accurate aerodynamic models are the basis of flight simulation and control law design. Mathematically modeling unsteady aerodynamics at high angles of attack bears great difficulties in model structure determination and parameter estimation due to little understanding of the flow mechanism. Support vector machines （SVMs） based on statistical learning theory provide a novel tool for nonlinear system modeling. The work presented here examines the feasibility of applying SVMs to high angle.-of-attack unsteady aerodynamic modeling field. Mainly, after a review of SVMs, several issues associated with unsteady aerodynamic modeling by use of SVMs are discussed in detail, such as sele, ction of input variables, selection of output variables and determination of SVM parameters. The least squares SVM （LS-SVM） models are set up from certain dynamic wind tunnel test data of a delta wing and an aircraft configuration, and then used to predict the aerodynamic responses in other tests. The predictions are in good agreement with the test data, which indicates the satisfving learning and generalization performance of LS-SVMs.展开更多
Mobile Ad hoc NETworks (MANETs), characterized by the free move of mobile nodes are more vulnerable to the trivial Denial-of-Service (DoS) attacks such as replay attacks. A replay attacker performs this attack at anyt...Mobile Ad hoc NETworks (MANETs), characterized by the free move of mobile nodes are more vulnerable to the trivial Denial-of-Service (DoS) attacks such as replay attacks. A replay attacker performs this attack at anytime and anywhere in the network by interception and retransmission of the valid signed messages. Consequently, the MANET performance is severally degraded by the overhead produced by the redundant valid messages. In this paper, we propose an enhancement of timestamp discrepancy used to validate a signed message and consequently limiting the impact of a replay attack. Our proposed timestamp concept estimates approximately the time where the message is received and validated by the received node. This estimation is based on the existing parameters defined at the 802.11 MAC layer.展开更多
电力系统作为实时信息与能源高度融合的电力信息物理融合系统(cyber-physical power system,CPPS),虚假数据注入攻击(false data injection attacks,FDIAs)的准确辨识将有效保证CPPS安全稳定运行。为准确、高效地完成日前负荷预测,首先...电力系统作为实时信息与能源高度融合的电力信息物理融合系统(cyber-physical power system,CPPS),虚假数据注入攻击(false data injection attacks,FDIAs)的准确辨识将有效保证CPPS安全稳定运行。为准确、高效地完成日前负荷预测,首先使用肯德尔相关系数(Kendall's tau-b)量化日期类型的取值,引入加权灰色关联分析选取相似日,再建立基于最小二乘支持向量机(least squares support vector machine,LSSVM)的日前负荷预测模型。将预测负荷通过潮流计算求解的系统节点状态量与无迹卡尔曼滤波(unscented Kalman filter,UKF)动态状态估计得到的状态量进行自适应加权混合,最后基于混合预测值和静态估计值间的偏差变量提出了攻击检测指数(attack detection index,ADI),根据ADI的分布检测FDIAs。若检测到FDIAs,使用混合预测状态量对该时刻的量测量进行修正。使用IEEE-14和IEEE-39节点系统进行仿真,结果验证了所提方法的有效性与可行性。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
跨站脚本XSS(Cross Site Scripting)漏洞,已对大多数网站产生严重威胁。其中存储型XSS漏洞对用户及网站的损害尤为巨大。事先使用漏洞扫描工具对该漏洞进行检测并修补,可以有效预防和减轻该漏洞被利用后导致的一系列危害。分析存储型XS...跨站脚本XSS(Cross Site Scripting)漏洞,已对大多数网站产生严重威胁。其中存储型XSS漏洞对用户及网站的损害尤为巨大。事先使用漏洞扫描工具对该漏洞进行检测并修补,可以有效预防和减轻该漏洞被利用后导致的一系列危害。分析存储型XSS漏洞的攻击原理,提出用巴科斯范式(BNF)自动生成初始攻击向量,对初始攻击向量进行变异处理。使用辅助标记自动检测存储型XSS漏洞的动态检测方法,设计并实现存储型XSS漏洞检测系统。在现实Web应用中测试评估了该系统,实验证明它能有效检测出应用中存在的存储型XSS漏洞。展开更多
文摘Abstract Accurate aerodynamic models are the basis of flight simulation and control law design. Mathematically modeling unsteady aerodynamics at high angles of attack bears great difficulties in model structure determination and parameter estimation due to little understanding of the flow mechanism. Support vector machines （SVMs） based on statistical learning theory provide a novel tool for nonlinear system modeling. The work presented here examines the feasibility of applying SVMs to high angle.-of-attack unsteady aerodynamic modeling field. Mainly, after a review of SVMs, several issues associated with unsteady aerodynamic modeling by use of SVMs are discussed in detail, such as sele, ction of input variables, selection of output variables and determination of SVM parameters. The least squares SVM （LS-SVM） models are set up from certain dynamic wind tunnel test data of a delta wing and an aircraft configuration, and then used to predict the aerodynamic responses in other tests. The predictions are in good agreement with the test data, which indicates the satisfving learning and generalization performance of LS-SVMs.
文摘Mobile Ad hoc NETworks (MANETs), characterized by the free move of mobile nodes are more vulnerable to the trivial Denial-of-Service (DoS) attacks such as replay attacks. A replay attacker performs this attack at anytime and anywhere in the network by interception and retransmission of the valid signed messages. Consequently, the MANET performance is severally degraded by the overhead produced by the redundant valid messages. In this paper, we propose an enhancement of timestamp discrepancy used to validate a signed message and consequently limiting the impact of a replay attack. Our proposed timestamp concept estimates approximately the time where the message is received and validated by the received node. This estimation is based on the existing parameters defined at the 802.11 MAC layer.
文摘电力系统作为实时信息与能源高度融合的电力信息物理融合系统(cyber-physical power system,CPPS),虚假数据注入攻击(false data injection attacks,FDIAs)的准确辨识将有效保证CPPS安全稳定运行。为准确、高效地完成日前负荷预测,首先使用肯德尔相关系数(Kendall's tau-b)量化日期类型的取值,引入加权灰色关联分析选取相似日,再建立基于最小二乘支持向量机(least squares support vector machine,LSSVM)的日前负荷预测模型。将预测负荷通过潮流计算求解的系统节点状态量与无迹卡尔曼滤波(unscented Kalman filter,UKF)动态状态估计得到的状态量进行自适应加权混合,最后基于混合预测值和静态估计值间的偏差变量提出了攻击检测指数(attack detection index,ADI),根据ADI的分布检测FDIAs。若检测到FDIAs,使用混合预测状态量对该时刻的量测量进行修正。使用IEEE-14和IEEE-39节点系统进行仿真,结果验证了所提方法的有效性与可行性。
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。
文摘跨站脚本XSS(Cross Site Scripting)漏洞,已对大多数网站产生严重威胁。其中存储型XSS漏洞对用户及网站的损害尤为巨大。事先使用漏洞扫描工具对该漏洞进行检测并修补,可以有效预防和减轻该漏洞被利用后导致的一系列危害。分析存储型XSS漏洞的攻击原理,提出用巴科斯范式(BNF)自动生成初始攻击向量,对初始攻击向量进行变异处理。使用辅助标记自动检测存储型XSS漏洞的动态检测方法,设计并实现存储型XSS漏洞检测系统。在现实Web应用中测试评估了该系统,实验证明它能有效检测出应用中存在的存储型XSS漏洞。
