We present our results by using a machine learning(ML)approach for the solution of the Riemann problem for the Euler equations of fluid dynamics.The Riemann problem is an initial-value problem with piecewise-constant ...We present our results by using a machine learning(ML)approach for the solution of the Riemann problem for the Euler equations of fluid dynamics.The Riemann problem is an initial-value problem with piecewise-constant initial data and it represents a mathematical model of the shock tube.The solution of the Riemann problem is the building block for many numerical algorithms in computational fluid dynamics,such as finite-volume or discontinuous Galerkin methods.Therefore,a fast and accurate approximation of the solution of the Riemann problem and construction of the associated numerical fluxes is of crucial importance.The exact solution of the shock tube problem is fully described by the intermediate pressure and mathematically reduces to finding a solution of a nonlinear equation.Prior to delving into the complexities of ML for the Riemann problem,we consider a much simpler formulation,yet very informative,problem of learning roots of quadratic equations based on their coefficients.We compare two approaches:(i)Gaussian process(GP)regressions,and(ii)neural network(NN)approximations.Among these approaches,NNs prove to be more robust and efficient,although GP can be appreciably more accurate(about 30\%).We then use our experience with the quadratic equation to apply the GP and NN approaches to learn the exact solution of the Riemann problem from the initial data or coefficients of the gas equation of state(EOS).We compare GP and NN approximations in both regression and classification analysis and discuss the potential benefits and drawbacks of the ML approach.展开更多
采用数据挖掘技术来扩展入侵检测的功能以判别未知攻击是当前的一个研究热点。本文在分析了各种数据挖掘算法的基础上,提出将k-NN分类规则运用于入侵检测,给出了可运用于入侵检测的k-NN分类规则改进算法k-NNfor IDS。最后,我们在KDD99上...采用数据挖掘技术来扩展入侵检测的功能以判别未知攻击是当前的一个研究热点。本文在分析了各种数据挖掘算法的基础上,提出将k-NN分类规则运用于入侵检测,给出了可运用于入侵检测的k-NN分类规则改进算法k-NNfor IDS。最后,我们在KDD99上对-kNN for IDS算法进行试验,验证了算法的有效性。展开更多
入侵检测系统能够有效地检测网络中异常的攻击行为,对网络安全至关重要.目前,许多入侵检测方法对攻击行为Probe(probing),U2R(user to root),R2L(remote to local)的检测率比较低.基于这一问题,提出一种新的混合多层次入侵检测模型,检...入侵检测系统能够有效地检测网络中异常的攻击行为,对网络安全至关重要.目前,许多入侵检测方法对攻击行为Probe(probing),U2R(user to root),R2L(remote to local)的检测率比较低.基于这一问题,提出一种新的混合多层次入侵检测模型,检测正常和异常的网络行为.该模型首先应用KNN(K nearest neighbors)离群点检测算法来检测并删除离群数据,从而得到一个小规模和高质量的训练数据集;接下来,结合网络流量的相似性,提出一种类别检测划分方法,该方法避免了异常行为在检测过程中的相互干扰,尤其是对小流量攻击行为的检测;结合这种划分方法,构建多层次的随机森林模型来检测网络异常行为,提高了网络攻击行为的检测效果.流行的数据集KDD(knowledge discovery and data mining) Cup 1999被用来评估所提出的模型.通过与其他算法进行对比,该方法的准确率和检测率要明显优于其他算法,并且能有效地检测Probe,U2R,R2L这3种攻击类型.展开更多
基金This work was performed under the auspices of the National Nuclear Security Administration of the US Department of Energy at Los Alamos National Laboratory under Contract No.DE-AC52-06NA25396The authors gratefully acknowledge the support of the US Department of Energy National Nuclear Security Administration Advanced Simulation and Computing Program.The Los Alamos unlimited release number is LA-UR-19-32257.
文摘We present our results by using a machine learning(ML)approach for the solution of the Riemann problem for the Euler equations of fluid dynamics.The Riemann problem is an initial-value problem with piecewise-constant initial data and it represents a mathematical model of the shock tube.The solution of the Riemann problem is the building block for many numerical algorithms in computational fluid dynamics,such as finite-volume or discontinuous Galerkin methods.Therefore,a fast and accurate approximation of the solution of the Riemann problem and construction of the associated numerical fluxes is of crucial importance.The exact solution of the shock tube problem is fully described by the intermediate pressure and mathematically reduces to finding a solution of a nonlinear equation.Prior to delving into the complexities of ML for the Riemann problem,we consider a much simpler formulation,yet very informative,problem of learning roots of quadratic equations based on their coefficients.We compare two approaches:(i)Gaussian process(GP)regressions,and(ii)neural network(NN)approximations.Among these approaches,NNs prove to be more robust and efficient,although GP can be appreciably more accurate(about 30\%).We then use our experience with the quadratic equation to apply the GP and NN approaches to learn the exact solution of the Riemann problem from the initial data or coefficients of the gas equation of state(EOS).We compare GP and NN approximations in both regression and classification analysis and discuss the potential benefits and drawbacks of the ML approach.
文摘采用数据挖掘技术来扩展入侵检测的功能以判别未知攻击是当前的一个研究热点。本文在分析了各种数据挖掘算法的基础上,提出将k-NN分类规则运用于入侵检测,给出了可运用于入侵检测的k-NN分类规则改进算法k-NNfor IDS。最后,我们在KDD99上对-kNN for IDS算法进行试验,验证了算法的有效性。
文摘入侵检测系统能够有效地检测网络中异常的攻击行为,对网络安全至关重要.目前,许多入侵检测方法对攻击行为Probe(probing),U2R(user to root),R2L(remote to local)的检测率比较低.基于这一问题,提出一种新的混合多层次入侵检测模型,检测正常和异常的网络行为.该模型首先应用KNN(K nearest neighbors)离群点检测算法来检测并删除离群数据,从而得到一个小规模和高质量的训练数据集;接下来,结合网络流量的相似性,提出一种类别检测划分方法,该方法避免了异常行为在检测过程中的相互干扰,尤其是对小流量攻击行为的检测;结合这种划分方法,构建多层次的随机森林模型来检测网络异常行为,提高了网络攻击行为的检测效果.流行的数据集KDD(knowledge discovery and data mining) Cup 1999被用来评估所提出的模型.通过与其他算法进行对比,该方法的准确率和检测率要明显优于其他算法,并且能有效地检测Probe,U2R,R2L这3种攻击类型.
