The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Gregory Levitin - One of the best experts on this subject based on the ideXlab platform.
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preventive strike vs false targets and protection in Defense Strategy
Reliability Engineering & System Safety, 2011Co-Authors: Gregory Levitin, Kjell HauskenAbstract:A defender allocates its resource between defending an object passively and striking preventively against an attacker seeking to destroy the object. With no preventive strike the defender distributes its entire resource between deploying false targets, which the attacker cannot distinguish from the genuine object, and protecting the object. If the defender strikes preventively, the attacker's vulnerability depends on its protection and on the defender's resource allocated to the strike. If the attacker survives, the object's vulnerability depends on the attacker's revenge attack resource allocated to the attacked object. The optimal Defense resource distribution between striking preventively, deploying the false targets and protecting the object is analyzed. Two cases of the attacker Strategy are considered: when the attacker attacks all of the targets and when it chooses a number of targets to attack. An optimization model is presented for making a decision about the efficiency of the preventive strike based on the estimated attack probability, dependent on a variety of model parameters.
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false targets efficiency in Defense Strategy
European Journal of Operational Research, 2009Co-Authors: Gregory Levitin, Kjell HauskenAbstract:The paper analyzes the efficiency of deploying false targets as part of a Defense Strategy. It is assumed that the defender has a single object that can be destroyed by the attacker. The defender distributes its resource between deploying false targets and protecting the object from outside attacks. The attacker cannot distinguish the false targets from the defended object (genuine target). Therefore the attacker has no preferences for attacking one target rather than another target. The defender decides how many false targets to deploy whereas the attacker decides how many targets to attack. The article assumes that both the defender and attacker have complete information and full rationality. The optimal number of false targets and the attacked targets are obtained for the case of fixed and variable resources of the defender and the attacker as solutions of a non-cooperative game between the two agents.
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minmax Defense Strategy for complex multi state systems
Reliability Engineering & System Safety, 2009Co-Authors: Kjell Hausken, Gregory LevitinAbstract:Abstract This paper presents a general optimization methodology that merges game theory and multi-state system survivability theory. The defender has multiple alternatives of Defense Strategy that presumes separation and protection of system elements. The attacker also has multiple alternatives of its attack Strategy based on a combination of different possible attack actions against different groups of system elements. The defender minimizes, and the attacker maximizes, the expected damage caused by the attack (taking into account the unreliability of system elements and the multi-state nature of complex series–parallel systems). The problem is defined as a two-period minmax non-cooperative game between the defender who moves first and the attacker who moves second. An exhaustive minmax optimization algorithm is presented based on a double-loop genetic algorithm for determining the solution. A universal generating function technique is applied for evaluating the losses caused by system performance reduction. Illustrative examples with solutions are presented.
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optimal Defense Strategy against intentional attacks
IEEE Transactions on Reliability, 2007Co-Authors: Gregory LevitinAbstract:This paper presents a generalized model of damage caused to a complex multi-state series-parallel system by intentional attack. The model takes into account the Defense Strategy that presumes separation and protection of system elements. The Defense Strategy optimization methodology is suggested, based on the assumption that the attacker tries to maximize the expected damage of an attack. An optimization algorithm is presented that uses a universal generating function technique for evaluating the losses caused by system performance reduction, and a genetic algorithm for determining the optimal Defense Strategy. Illustrative examples of Defense Strategy optimization are presented
Kjell Hausken - One of the best experts on this subject based on the ideXlab platform.
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preventive strike vs false targets and protection in Defense Strategy
Reliability Engineering & System Safety, 2011Co-Authors: Gregory Levitin, Kjell HauskenAbstract:A defender allocates its resource between defending an object passively and striking preventively against an attacker seeking to destroy the object. With no preventive strike the defender distributes its entire resource between deploying false targets, which the attacker cannot distinguish from the genuine object, and protecting the object. If the defender strikes preventively, the attacker's vulnerability depends on its protection and on the defender's resource allocated to the strike. If the attacker survives, the object's vulnerability depends on the attacker's revenge attack resource allocated to the attacked object. The optimal Defense resource distribution between striking preventively, deploying the false targets and protecting the object is analyzed. Two cases of the attacker Strategy are considered: when the attacker attacks all of the targets and when it chooses a number of targets to attack. An optimization model is presented for making a decision about the efficiency of the preventive strike based on the estimated attack probability, dependent on a variety of model parameters.
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false targets efficiency in Defense Strategy
European Journal of Operational Research, 2009Co-Authors: Gregory Levitin, Kjell HauskenAbstract:The paper analyzes the efficiency of deploying false targets as part of a Defense Strategy. It is assumed that the defender has a single object that can be destroyed by the attacker. The defender distributes its resource between deploying false targets and protecting the object from outside attacks. The attacker cannot distinguish the false targets from the defended object (genuine target). Therefore the attacker has no preferences for attacking one target rather than another target. The defender decides how many false targets to deploy whereas the attacker decides how many targets to attack. The article assumes that both the defender and attacker have complete information and full rationality. The optimal number of false targets and the attacked targets are obtained for the case of fixed and variable resources of the defender and the attacker as solutions of a non-cooperative game between the two agents.
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minmax Defense Strategy for complex multi state systems
Reliability Engineering & System Safety, 2009Co-Authors: Kjell Hausken, Gregory LevitinAbstract:Abstract This paper presents a general optimization methodology that merges game theory and multi-state system survivability theory. The defender has multiple alternatives of Defense Strategy that presumes separation and protection of system elements. The attacker also has multiple alternatives of its attack Strategy based on a combination of different possible attack actions against different groups of system elements. The defender minimizes, and the attacker maximizes, the expected damage caused by the attack (taking into account the unreliability of system elements and the multi-state nature of complex series–parallel systems). The problem is defined as a two-period minmax non-cooperative game between the defender who moves first and the attacker who moves second. An exhaustive minmax optimization algorithm is presented based on a double-loop genetic algorithm for determining the solution. A universal generating function technique is applied for evaluating the losses caused by system performance reduction. Illustrative examples with solutions are presented.
Anurag K Srivastava - One of the best experts on this subject based on the ideXlab platform.
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a diversity based substation cyber Defense Strategy utilizing coloring games
IEEE Transactions on Smart Grid, 2019Co-Authors: Adam Hahn, Anurag K SrivastavaAbstract:Growing cybersecurity risks in the power grid require that utilities implement a variety of security mechanisms (SMs), including VPNs, firewalls, authentication, and access control mechanisms. While these mechanisms provide some level of Defense, they also may contain software vulnerabilities which allow an attack to bypass their protection. Because the same SM type is often used to protect a large number of substations, a single vulnerability could enable a coordinated attack by simultaneously targeting many substations. To protect against such an attack, utilities can adopt a Strategy to use a diverse set of SMs, such that the impact from a vulnerability in any SM is minimized. This paper introduces a game-theoretic graph coloring technique to determine the optimal allocation of SM diversity that minimizes the impact of security vulnerabilities to the grid. This paper demonstrates that the proposed approach provides a Nash equilibrium solution. Furthermore, the technique is demonstrated against cyber-physical models for both IEEE-14 and IEEE-118 bus systems, and compared with other non-strategic diversity allocation methods to demonstrate its effectiveness.
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a diversity based substation cyber Defense Strategy utilizing coloring games
arXiv: Cryptography and Security, 2018Co-Authors: Adam Hahn, Anurag K SrivastavaAbstract:Growing cybersecurity risks in the power grid require that utilities implement a variety of security mechanism (SM) composed mostly of VPNs, firewalls, or other custom security components. While they provide some protection, they might contain software vulnerabilities which can lead to a cyber-attack. In this paper, the severity of a cyber-attack has been decreased by employing a diverse set of SM that reduce repetition of a single vulnerability. This paper focuses on the allocation of diverse SM and tries to increase the security of the cyber assets located within the electronic security perimeter(ESP) of a substation. We have used a graph-based coloring game in a distributed manner to allocate diverse SM for protecting the cyber assets. The vulnerability assessment for power grid network is also analyzed using this game theoretic method. An improved, diversified SMs for worst-case scenario has been demonstrated by reaching the Nash equilibrium of graph coloring game. As a case study, we analyze the IEEE-14 and IEEE-118 bus system, observe the different distributed coloring algorithm for allocating diverse SM and calculating the overall network criticality.
Jin-dong Wang - One of the best experts on this subject based on the ideXlab platform.
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attack Defense differential game model for network Defense Strategy selection
IEEE Access, 2019Co-Authors: Heng-wei Zhang, Jin-dong Wang, Lv Jiang, Shirui Huang, Yuchen ZhangAbstract:The existing game-theoretic approaches for network security problems mostly use the static game or the multi-stage dynamic game. However, these researches can not meet the timeliness requirment to analyze the network attack and Defense. It is better to regard the attack and Defense as a dynamic and real-time process, in which way the rapidity and continuity of network confrontation can be described more precisely. Referring to the epidemic model SIR, we formulated the novel model NIRM to analyze the evolution of network security states. Based on the mentioned above, the attack-Defense differential game model was constructed by introducing the differential game theory. Then we figured out the solution of saddle-point strategies in the game. By analyzing the game equilibrium, the algorithm of optimal Defense strategies selection in the real-time confrontation was designed, which is more targeted and has greater timeliness. Finally by simulation experiments, we demonstrated the validity of the model and method proposed in this paper, and drew some instructive conclusions on network Defense deployment.
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Markov Evolutionary Games for Network Defense Strategy Selection
IEEE Access, 2017Co-Authors: Jian-ming Huang, Heng-wei Zhang, Jin-dong WangAbstract:Since the characteristics of opposite objectives, non-cooperation relationship, and dependent strategies of network attack and Defense are highly consistent with game theory, researching the decision-making methods of network Defense and applying the game models to analyze the network attack-Defense behaviors has been of concern in recent years. However, most of the research achievements regarding to the game models are based on the hypothesis that both the two sides' players are completely rational, which is hard to meet. Therefore, we combined the evolutionary game theory and Markov decisionmaking process to construct a multi-stage Markov evolutionary game model for network attack-Defense analysis, in view of the bounded rationality constraint. The model, based on the non-cooperative evolutionary game theory, could accomplish dynamic analysis and deduction for the multi-stage and multi-state network attack-Defense process. In addition, an objective function with discounted total payoffs was designed by analyzing payoff characteristics of the multi-stage evolutionary game, which is more consistent with the reality of network attack and Defense. Besides, the solving method for multi-stage game equilibrium was proposed on the basis of calculating the single-stage evolutionary game equilibrium. In addition, an algorithm for optimal Defense Strategy of the multi-stage evolutionary games was given. Finally, the experiments showed the high effectiveness and validity of the model and method that has a guiding significance for the network attack and Defense.
Liqiong Chen - One of the best experts on this subject based on the ideXlab platform.
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a game theoretic method to model and evaluate attack Defense Strategy in cloud computing
IEEE International Conference on Services Computing, 2013Co-Authors: Huiqun Yu, Liqiong ChenAbstract:Cloud computing has attracted much interest recently from both industry and academic. However, it is difficult to construct perfectly secure mechanisms, in face of complex and various attack behaviors in cloud computing. In this paper, a stochastic game model (SGM) is proposed to describe the attack-Defense behavior in cloud computing, the physical machine, attack-Defense behavior and their attributes are modeled by using SGM, thus forming the attack-Defense game model of cloud computing. On this basis, the Nash equilibrium of attack-Defense process of physical machine is computed in order to get the best Defense Strategy. The related theories of Petri net are used to verify the correctness of proposed method. The computation formula and the actual meaning of performance index are given. The enforcement algorithm is also proposed. Both case study and simulation results show that the proposed method can adapt quickly to the changes in cloud application, thus improving the security of cloud computing.
