The object of this study is to propose a statistical model for predicting the Expected Path Length (expected number of steps the attacker will take, starting from the initial state to compromise the security goal—EPL...The object of this study is to propose a statistical model for predicting the Expected Path Length (expected number of steps the attacker will take, starting from the initial state to compromise the security goal—EPL) in a cyber-attack. The model we developed is based on utilizing vulnerability information along with having host centric attack graph. Utilizing the developed model, one can identify the interaction among the vulnerabilities and individual variables (risk factors) that drive the Expected Path Length. Gaining a better understanding of the relationship between vulnerabilities and their interactions can provide security administrators a better view and an understanding of their security status. In addition, we have also ranked the attributable variables and their contribution in estimating the subject length. Thus, one can utilize the ranking process to take precautions and actions to minimize Expected Path Length.展开更多
Any computer system with known vulnerabilities can be presented using attack graphs. An attacker generally has a mission to reach a goal state that he expects to achieve. Expected Path Length (EPL) [1] in the context ...Any computer system with known vulnerabilities can be presented using attack graphs. An attacker generally has a mission to reach a goal state that he expects to achieve. Expected Path Length (EPL) [1] in the context of an attack graph describes the length or number of steps that the attacker has to take in achieving the goal state. However, EPL varies and it is based on the “state of vulnerabilities” [2] [3] in a given computer system. Any vulnerability throughout its life cycle passes through several stages that we identify as “states of the vulnerability life cycle” [2] [3]. In our previous studies we have developed mathematical models using Markovian theory to estimate the probability of a given vulnerability being in a particular state of its life cycle. There, we have considered a typical model of a computer network system with two computers subject to three vulnerabilities, and developed a method driven by an algorithm to estimate the EPL of this network system as a function of time. This approach is important because it allows us to monitor a computer system during the process of being exploited. Proposed non-homogeneous model in this study estimates the behavior of the EPL as a function of time and therefore act as an index of the risk associated with the network system getting exploited.展开更多
文摘The object of this study is to propose a statistical model for predicting the Expected Path Length (expected number of steps the attacker will take, starting from the initial state to compromise the security goal—EPL) in a cyber-attack. The model we developed is based on utilizing vulnerability information along with having host centric attack graph. Utilizing the developed model, one can identify the interaction among the vulnerabilities and individual variables (risk factors) that drive the Expected Path Length. Gaining a better understanding of the relationship between vulnerabilities and their interactions can provide security administrators a better view and an understanding of their security status. In addition, we have also ranked the attributable variables and their contribution in estimating the subject length. Thus, one can utilize the ranking process to take precautions and actions to minimize Expected Path Length.
文摘Any computer system with known vulnerabilities can be presented using attack graphs. An attacker generally has a mission to reach a goal state that he expects to achieve. Expected Path Length (EPL) [1] in the context of an attack graph describes the length or number of steps that the attacker has to take in achieving the goal state. However, EPL varies and it is based on the “state of vulnerabilities” [2] [3] in a given computer system. Any vulnerability throughout its life cycle passes through several stages that we identify as “states of the vulnerability life cycle” [2] [3]. In our previous studies we have developed mathematical models using Markovian theory to estimate the probability of a given vulnerability being in a particular state of its life cycle. There, we have considered a typical model of a computer network system with two computers subject to three vulnerabilities, and developed a method driven by an algorithm to estimate the EPL of this network system as a function of time. This approach is important because it allows us to monitor a computer system during the process of being exploited. Proposed non-homogeneous model in this study estimates the behavior of the EPL as a function of time and therefore act as an index of the risk associated with the network system getting exploited.
