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
In current research, a series of triaxial tests, which were employed to simulate three typical mining lay-outs (i.e., top-coal caving, non-pillar mining and protected coal seam mining), were conducted on coal by using...In current research, a series of triaxial tests, which were employed to simulate three typical mining lay-outs (i.e., top-coal caving, non-pillar mining and protected coal seam mining), were conducted on coal by using MTS815 Flex Test GT rock mechanics test system, and the fracture networks in the broken coal samples were qualitatively and quantitatively investigated by employing CT scanning and 3D reconstruc-tion techniques. This work aimed at providing a detail description on the micro-structure and fracture-connectivity characteristics of rupture coal samples under different mining layouts. The results show that: (i) for protected coal seam mining layout, the coal specimens failure is in a compression-shear manner and oppositely, (ii) the tension-shear failure phenomenon is observed for top-coal caving and non-pillar mining layouts. By investigating the connectivity features of the generated fractures in the direction of r1 under different mining layouts, it is found that the connectivity level of the fractures of the samples corresponding to non-pillar mining layout was the highest.展开更多
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.
基金financially supported by the Major State Fundamental Research Project of China(Nos.2011CB201201and2010CB226802)the National Natural Science Foundation of China(No.51204113)the Youth Science and Technology Fund of Sichuan Province(No.2012JQ0031)
文摘In current research, a series of triaxial tests, which were employed to simulate three typical mining lay-outs (i.e., top-coal caving, non-pillar mining and protected coal seam mining), were conducted on coal by using MTS815 Flex Test GT rock mechanics test system, and the fracture networks in the broken coal samples were qualitatively and quantitatively investigated by employing CT scanning and 3D reconstruc-tion techniques. This work aimed at providing a detail description on the micro-structure and fracture-connectivity characteristics of rupture coal samples under different mining layouts. The results show that: (i) for protected coal seam mining layout, the coal specimens failure is in a compression-shear manner and oppositely, (ii) the tension-shear failure phenomenon is observed for top-coal caving and non-pillar mining layouts. By investigating the connectivity features of the generated fractures in the direction of r1 under different mining layouts, it is found that the connectivity level of the fractures of the samples corresponding to non-pillar mining layout was the highest.
