Penetration testing offers strong advantages in the discovery of hidden vulnerabilities in a network and assessing network security.However,it can be carried out by only security analysts,which costs considerable time...Penetration testing offers strong advantages in the discovery of hidden vulnerabilities in a network and assessing network security.However,it can be carried out by only security analysts,which costs considerable time and money.The natural way to deal with the above problem is automated penetration testing,the essential part of which is automated attack planning.Although previous studies have explored various ways to discover attack paths,all of them require perfect network information beforehand,which is contradictory to realistic penetration testing scenarios.To vividly mimic intruders to find all possible attack paths hidden in a network from the perspective of hackers,we propose a network information gain based automated attack planning(NIG-AP)algorithm to achieve autonomous attack path discovery.The algorithm formalizes penetration testing as a Markov decision process and uses network information to obtain the reward,which guides an agent to choose the best response actions to discover hidden attack paths from the intruder’s perspective.Experimental results reveal that the proposed algorithm demonstrates substantial improvement in training time and effectiveness when mining attack paths.展开更多
I consider a system whose deterioration follows a discrete-time and discrete-state Markov chain with an absorbing state. When the system is put into practice, I may select operation (wait), imperfect repair, or replac...I consider a system whose deterioration follows a discrete-time and discrete-state Markov chain with an absorbing state. When the system is put into practice, I may select operation (wait), imperfect repair, or replacement at each discrete-time point. The true state of the system is not known when it is operated. Instead, the system is monitored after operation and some incomplete information concerned with the deterioration is obtained for decision making. Since there are multiple imperfect repairs, I can select one option from them when the imperfect repair is preferable to operation and replacement. To express this situation, I propose a POMDP model and theoretically investigate the structure of an optimal maintenance policy minimizing a total expected discounted cost for an unbounded horizon. Then two stochastic orders are used for the analysis of our problem.展开更多
基金the National Natural Science Foundation of China(No.61502528)。
文摘Penetration testing offers strong advantages in the discovery of hidden vulnerabilities in a network and assessing network security.However,it can be carried out by only security analysts,which costs considerable time and money.The natural way to deal with the above problem is automated penetration testing,the essential part of which is automated attack planning.Although previous studies have explored various ways to discover attack paths,all of them require perfect network information beforehand,which is contradictory to realistic penetration testing scenarios.To vividly mimic intruders to find all possible attack paths hidden in a network from the perspective of hackers,we propose a network information gain based automated attack planning(NIG-AP)algorithm to achieve autonomous attack path discovery.The algorithm formalizes penetration testing as a Markov decision process and uses network information to obtain the reward,which guides an agent to choose the best response actions to discover hidden attack paths from the intruder’s perspective.Experimental results reveal that the proposed algorithm demonstrates substantial improvement in training time and effectiveness when mining attack paths.
文摘本文基于随机有限集的高斯混合多目标滤波器(Gaussian Mixture Multi-Target Filter,GM-MTF)提出几种传感器控制策略.首先,基于容积卡尔曼高斯混合多目标非线性滤波器,借助两个高斯分布之间的巴氏距离,推导GM-MTF的整体信息增益,并以此为基础提出相应的传感器控制策略.另外,设计高斯粒子的联合采样方法对多目标滤波器的预测高斯分量进行采样,用一组带权值的粒子去近似多目标统计特性,利用理想量测集对粒子的权值进行更新,继而研究利用Rényi散度作为评价函数,提出一种适应性更好的传感器控制策略.最后,给出基于目标势的后验期望(Posterior Expected Number of Targets,PENT)评价的高斯混合实现过程.仿真实验验证了提出算法的有效性.
文摘I consider a system whose deterioration follows a discrete-time and discrete-state Markov chain with an absorbing state. When the system is put into practice, I may select operation (wait), imperfect repair, or replacement at each discrete-time point. The true state of the system is not known when it is operated. Instead, the system is monitored after operation and some incomplete information concerned with the deterioration is obtained for decision making. Since there are multiple imperfect repairs, I can select one option from them when the imperfect repair is preferable to operation and replacement. To express this situation, I propose a POMDP model and theoretically investigate the structure of an optimal maintenance policy minimizing a total expected discounted cost for an unbounded horizon. Then two stochastic orders are used for the analysis of our problem.
