CN109861997B - Dynamic game access control reward and punishment incentive constraint method - Google Patents
Dynamic game access control reward and punishment incentive constraint method Download PDFInfo
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Abstract
The invention discloses a dynamic game access control reward and punishment incentive constraint method, which is used for establishing a trust-based dynamic access control game model aiming at the dishonest cooperation of network interaction entities in an open network environment, prompting the interaction entities to rationally select a strategy expected by a system (designer) under the drive of own benefits through a designed reward and punishment incentive constraint mechanism, and realizing the overall equilibrium state among the entities according with the target (access control strategy) requirements.
Description
Technical Field
The invention belongs to the technical field of mechanism design, and particularly relates to a reward punishment incentive constraint method for dynamic game access control.
Background
The mechanistic design theory, which is the fastest developing branch of research in the scientific field in the last 20 years, originated from the 2007 nobel economic prize winner-professor rio helvetz of university of minnesota economics in usa in 1960 and 1972 pioneering work. For any given (economic, social or systematic) achievement goal, under the conditions of freely-selected, rational and voluntary, incompletely-information and other scattered decision-making conditions, the individual interests and the target goals of the behavior participants are made consistent through a mechanism design.
At present, an incentive constraint mechanism of behaviors among network interaction entities under different network environments is solved, but no rational selection strategy of the entity behaviors is involved, and the effect on restraining the dishonest entity behaviors is not ideal.
Disclosure of Invention
On the basis of existing research on relevant problems of the access control and incentive mechanisms, aiming at the dishonest cooperation of the network interaction entity in the open network environment, a trust-based dynamic access control game model is established, and the interaction entity is prompted to rationally select a strategy expected by a system (designer) under the drive of own benefits through a designed punishment and punishment incentive constraint mechanism, so that the overall equilibrium state between entities meeting the requirements of a target (access control strategy) is realized.
In order to achieve the purpose, the invention adopts the following technical scheme:
a dynamic game access control reward and punishment incentive constraint method comprises the following steps:
step 1, in an initialization stage, a network interaction entity is in an interaction waiting state, meanwhile, an entity node is also in an interaction mixed monitoring state, initialization parameters (N, S, p, …, T) are obtained from a game model mechanism, and the integrity category (host, disonest) and the integrity degree of the behavior of a judgment node are evaluated;
step 2, when the interactive entity has honest access, triggering a reward incentive mechanism; judging the condition satisfied by the related income, and setting a proper reward factor by the system
The motivation subject consciously selects honest access and turns to step 4;
step 3, triggering a punishment incentive constraint mechanism when the interactive entity is accessed in a dishonest way; judging the integrity degree of the entity evaluating the dishonest behavior and deciding the punishment degree; when T isO→S＜θ||k＞＝NsetWhen the punishment reaches the maximum, directly refusing the access;
and 4, evaluating the interaction result and feeding back related parameters.
Drawings
FIG. 1 is a schematic diagram of a trust-based access control bet;
FIG. 2 is a flow chart of the present invention.
Detailed Description
Trust-based access control gaming model
And under the incomplete information, the visiting object utilizes the experience of the visiting object to perform trust judgment on the subject, and the behavior accords with the Bayes dynamic game model of the incomplete information. Therefore, a 'trust-based access control game model' G (N, θ, S, P, U) is established, and a game tree form is obtained according to the hasani (Harsanyi) conversion basic idea as shown in fig. 1, and the specific meanings of the parameters are as follows:
(1) n is the set of players participating in the interactive game, namely the Subject Object and the Object of the visit participating in the visit interaction;
(2)θ＝θs×θoθ is the game type space set;
(3)S＝Ss×Soand S is a set of game action strategies under game type theta, wherein
Is a set of action policies that access the subject S,
is an action policy set for accessing object O;
(4) p: θ → [0, 1], P ∈ P, P being an inference about the trustworthiness of the access principal, P ═ { P, 1-P }, P representing the probability that the access principal is trustworthy, 1-P representing the probability that the access principal is untrustworthy;
(5)U＝{us，uo}，usθ × S → R represents the payment function of the accessing principal, R is the profit value; u. ofoθ × S → R, represents a payment function for the access object. u. ofs，uoThe payment matrix is shown in table 1.
Table 1 access control game payout matrix based on trust
(1) According to the access request of the access subject, under the condition that the whole access subject (the subject and each condition attribute) is evaluated to be credible, the object selects an authorization strategy and obtains the credible access of the subject, and at the moment, the income of the subject is (S.ta.u.inco, O.ta.u.inco);
(2) according to the access request of the access subject, under the condition that the whole access subject (the subject and each condition attribute) is evaluated to be credible, the object selects an authorization strategy and obtains the dishonest access of the subject, and at the moment, the subject and the object have the benefits of (Sn.ta.u.inco.loss);
(3) according to the access request of the access subject, under the condition that the whole access subject (the subject and each condition attribute) is evaluated to be credible, the subject selects an authorization rejection strategy, and at the moment, the subject and the subject have the income of (0, O.tra.u.loss);
(4) according to an access request of an access subject, under the condition that the whole access subject (the subject and each condition attribute) is evaluated to be not credible, an object selects an authorization strategy and obtains the credible access of the subject, and at the moment, the income of the subject and the object is (Sn.nya.u.inco.o.nta.u.loss);
(5) according to the access request of the access subject, under the condition that the whole access subject (the subject and each condition attribute) is evaluated to be not credible, the object is authorized and obtains the dishonest access of the subject, and at the moment, the subject and the object have the benefits of (Sn.
(6) According to the access request of the access subject, the subject selects the authorization denial policy under the condition that the evaluation of the whole access subject (the subject and each condition attribute) is not credible, and at the moment, the subject and the subject have the profit of (0, 0).
Game theory-based access control reward and punishment incentive constraint mechanism
At any time T in the open network, the access subject in the network interaction can have two policy choices for the access object according to the established access control game model, namely, honest access (host) or dishonest access (dishonest). The access subject in the interactive process can not be isolated from other network entities in an open network environment, and the access control mechanism must select a rational game strategy: in order to achieve the aim of stimulating honest cooperation among entities, the generation of the dishonest behavior is effectively inhibited by reducing the gain of the dishonest nodes and even punishing; and rewarding the return income of the integrity node by taking the benefits as a driving force, and motivating and restricting the interactive entity to actively adopt integrity access behaviors.
1. Conditional assumptions
When a network interaction behavior occurs, because the access object is an authorizer of both game parties, for an access control request provided by the access subject, a policy selection for authorizing or rejecting the access subject needs to be made according to prior history information of the access subject and an interaction scene where the access subject is located. If the access subject is the first access subject in the interaction process, the evaluation value is assumed to be used as the measurement of direct prior information of the access subject, the trust quantification and evaluation strategy is applied, and the access subject of the resource owner evaluates the comprehensive trust value of the access subject to be TO→S∈[0，1]When the visiting object has an integrated trust evaluation value T for the visiting subjectO→SWhen the value is more than or equal to theta, the access object selects an authorization permission strategy and accesses the access subjectThe line authorizes the permission, otherwise refuses the access request of the access subject; wherein θ ∈ [0, 1]]The access object is the minimum trust threshold of the access right (dynamically) set according to the evaluation policy such as statistical analysis or association rule analysis. If the access subject has last been honest to access, then the trust evaluation is motivated by adopting reward measures and then the access is authorized, and if the access subject has executed fraudulent access, the access subject is punished as appropriate until the access subject is refused forever.
The access subject who has received the access permission is a post-actor with respect to the authorized action of the access object, and in this case, it is rational to select an action policy of the access subject whose goal is clear and whose profit is maximized. But obeys the trigger policy, after a fraudulent access, the access subject will be punished by the incentive constraint mechanism until the access authorization permission of the access object is never obtained, namely, the posterior information such as access feedback is applied to correct the relevant trust evaluation value of the access object to the access subject
(i represents the number of accesses) or increasing the associated access trust threshold θ < 1 for the accessing subject by the accessing object.
2. Reward and punishment incentive constraint mechanism
The reward punishment incentive constraint mechanism is used as a feedback mechanism, so that incentive constraint dishonest nodes can be effectively punished, and dishonest behaviors can be abandoned by the interactive entity. In order to guide the integrity access of a subject who is granted access authorization, the following punishment and punishment constraint mechanisms are designed in combination with an established dynamic access control game model: under the incentive and punishment incentive constraint mechanism, if the access subject (object) can adopt the honest access behavior (host) according to the purpose of mechanism design, the reward factor (reward coefficient) is passed
Suitably increasing the expected total return profit margin for a subject (object) to choose a trustworthy access to incentivize subject behavior, and if a subject (object) is accessed to take a fraudulent access, penalizing the subject (object) as appropriate until the fraudulent access is never obtainedAccess permission of the access object;
(1) reward incentive constraint mechanism
The reward principle is as follows: when the access subject selects the honest access strategy, the incentive constraint mechanism incentivizes the access subject by increasing the profit value of the access subject, improves the trust evaluation value of the access subject, and enables the access subject to obtain more access permission rights.
The direct trust is represented by alpha, the recommendation trust is represented by delta, the entity and network decision attribute factors are represented by gamma (the more the attributes are, the larger the gamma value is), the posterior probability is represented by rho, and the reward factor is represented by
Can be expressed as a function of α, δ, γ and ρ, i.e.:
in the case where the other variables are not changed,
the values are corrected according to the change of various factors, and the values are continuously increased along with the increase of alpha, delta and gamma;
the credibility and incentive of the integrity behavior of the subject can be correctly reflected without any strict assumption in an open network environment.
In the network interaction process, the reward factor of the access subject is assumed to be
Then t1The expected revenue of the instant access principal for selecting honest access is:
then tiThe expected total return revenue of the time access subject for selecting the honest access is as follows:
the expected revenue of the current access principal selecting a dishonest access is:
Rdish＝p*Sn.ta.u.inco+(1-p)*Sn.nta.u.inco
as can be appreciated from the principles of the game,
it can be seen that when the expected income of the access subject for selecting the dishonest access is larger than the expected total return income of the access subject for selecting the dishonest access, the access subject selects a dishonest strategy; when the expected income of the access subject for selecting the dishonest access is equal to the expected total return income of the access subject for selecting the dishonest access, the access subject may select a dishonest strategy and may also select a dishonest strategy; when the expected return for the access subject selecting the dishonest access is smaller than the expected total return for the access subject selecting the dishonest access, the access subject always selects the dishonest strategy to interact with the access object so as to obtain the maximum return.
Thus, an appropriate reward factor is selected
When the following conditions are satisfied:
the access subject is pursuing the maximization of the income, and the selected access behavior strategy is honest forever.
(2) Penalty incentive constraint mechanism
The penalty principle is:
when the access subject selects the dishonest access strategy, the incentive constraint mechanism punishs the access subject by reducing the profit value of the access subject, and reduces the related trust evaluation value of the access subject, so that the access subject does not have enough trust value to meet the minimum trust threshold of the applied access permission.
After the fraudulent access behavior occurs, the penalty incentive constraint mechanism optionally makes the following policy choices:
strategy a: (mild punishment strategy) improves the relevant access trust threshold value theta of the access object to the access subject, reduces the income U of the interaction entity, and reduces the relevant trust evaluation value of the access object to the access subject
And (b) strategy: (strict penalty policy) rejection when integrating the trust assessment value TO→STheta or the number of times k ═ N that dishonest (fraudulent) access actions occurset(NsetThe maximum tolerance minimum trigger value is set in advance for the access control mechanism, and the value is set to be 3) in the text, the access to the object is prohibited forever.
Therefore, the access object adopts strategy selection according to the harmfulness of the access subject in dishonest access behavior as follows:
due to the introduction of the penalty incentive constraint mechanism, rational interactive entity nodes have to consider the influence of the current behavior on the subsequent gaming phase. The expected loss of the dishonest access agent in the tth interaction stage is the sum of the losses of the stages, namely:
wherein, β ∈ (0,1) is a penalty factor (penalty coefficient), the smaller β is, the larger the loss of the dishonest access subject is, and the more severe the constraint mechanism is, and its value is determined by each factor of the network itself in the open network environment. U shapes(k) The loss of revenue for the visiting entity node S at stage k.
Trust-based access control gaming of Table 1Substituting relevant parameters into U in sub-matrixs_loss(t) to obtain:
from the above analysis, if the network interaction entity node S is in the process of changing from honest interaction to dishonest state, the maximum profit loss U of the network interaction entity node S can be calculated through game analysiss_lossThereby obtaining the U after the dishonest behavior of the user occurss(t+1)＝Us(t)-Us_loss(t) evaluating a comprehensive trust evaluation value TO → S, and judging whether the value is smaller than a threshold value theta; the defined time period n of the punishment stage is used for enhancing the strength of a system mechanism for punishing the dishonest access behavior by adjusting the value n through the system, and also used as an emergency mechanism for system safety, so that the safety of the system mechanism is guaranteed to the maximum extent, the minimum loss of an object is reduced, the conversion from the dishonest behavior to the dishonest behavior of an access subject is prevented, and the dishonest network entity is deterred.
The reward factor is to increase the interactive entity profit value to incentivize honest access and authorization of the entity, and the opposite penalty factor is to incentivize honest access and authorization of the entity by decreasing the interactive entity profit value. The reward factor and penalty factor example analysis are similar, and due to space limitation, only the reward factor is subjected to example analysis, and the penalty factor example analysis is not described in a reiterated way.
According to the reward and punishment incentive constraint mechanism design thought, the invention provides a reward and punishment incentive constraint method for dynamic game access control, which comprises the following steps of:
step 1, in an initialization stage, a network interaction entity is in an interaction waiting state, meanwhile, an entity node is also in an interaction mixed monitoring state, initialization parameters (N, S, p, …, T) are obtained from a game model mechanism, and integrity categories (host, disonest) and integrity degrees of behaviors of the nodes are evaluated and judged.
Step 2, when the interactive entity has honest access, triggering a reward incentive mechanism; determining conditions satisfied by the associated avails, and setting appropriate rewards by the systemFactor(s)
The motivation subject consciously selects honest access and turns to step 4;
step 3, triggering a punishment incentive constraint mechanism when the interactive entity is accessed in a dishonest way; judging the integrity degree of the entity evaluating the dishonest behavior and deciding the punishment degree; when T isO→S＜θ||k＞＝NsetAnd when the penalty reaches the maximum, directly refusing the access.
And 4, evaluating the interaction result and feeding back related parameters.
Under the incomplete information of an open network environment, the invention establishes a trust-based dynamic game access control model, prompts an interactive entity to rationally select a strategy expected by a system (designer) under the drive of own benefits through a designed punishment incentive constraint mechanism, takes the benefits as a driving force, rewards honest nodes for incentive, punishs and constrains the incentive dishonest nodes, plays a role in constraining dishonest access behaviors, and realizes the overall balance state among entities meeting the target requirements. The incentive constraint mechanism is effective in the aspect of the dishonest access problem of the network interaction entity, and the incentive purpose is achieved.
Claims (1)
1. A dynamic game access control reward and punishment incentive constraint method is characterized by comprising the following steps:
step 1, in an initialization stage, a network interaction entity is in an interaction waiting state, meanwhile, an entity node is also in an interaction mixed monitoring state, initialization parameters (N, S, p, …, T) are obtained from a game model mechanism, and the integrity category (host, disonest) and the integrity degree of the behavior of a judgment node are evaluated;
step 2, when the interactive entity has honest access, triggering a reward incentive mechanism; determining conditions satisfied by the associated proceeds, and setting a reward factor by the system
The main body is stimulated to voluntarily select honest access, and the step 4 is turned to;
step 3 Interactive entityTriggering a punishment incentive constraint mechanism during the dishonest access; judging the integrity degree of the entity evaluating the dishonest behavior and deciding the punishment degree; when T isO→S<θ||k>＝NsetWhen the punishment reaches the maximum, directly refusing the access;
step 4, evaluating the interaction result and feeding back related parameters;
under the incentive and punishment incentive constraint mechanism, if the access subject can take honest access behaviors (host) according to the design purpose of the mechanism, the access subject passes through the reward factor
The expected total return income amplitude of the access subject for selecting the honest access is improved to stimulate the behavior of the constraint subject, and if the access subject adopts the fraudulent access, the access subject is punished until the access subject can never obtain the access permission of the access object;
wherein, the reward punishment incentive constraint mechanism is as follows:
(1) reward incentive constraint mechanism
The reward principle is as follows: when the access subject selects an honest access strategy, the incentive constraint mechanism incentivizes the access subject by increasing the profit value of the access subject, improves the trust evaluation value of the access subject and enables the access subject to obtain more access permission rights;
alpha is adopted to represent direct trust, delta is adopted to represent recommendation trust, gamma is adopted to represent entity and network decision attribute factors, rho is used to represent posterior probability, and reward factor
Can be expressed as a function of α, δ, γ and ρ, i.e.:
in the case where the other variables are not changed,
the values are corrected according to the change of various factors, and the values are continuously increased along with the increase of alpha, delta and gamma;
the credibility and incentive of the honest behavior of the subject can be correctly reflected without any strict assumptions in the open network environment,
in the network interaction process, the reward factor of the access subject is assumed to be
Then t1The expected revenue of the instant access principal for selecting honest access is:
then tiThe expected total return revenue of the time access subject for selecting the honest access is as follows:
the expected revenue of the current access principal selecting a dishonest access is:
Rdish＝p*Sn.ta.u.inco+(1-p)*Sn.nta.u.inco
as can be appreciated from the principles of the game,
thus, an appropriate reward factor is selected
When the following conditions are satisfied:
the access subject pursues the maximization of the income, and the selected access behavior strategy is honest forever;
(2) penalty incentive constraint mechanism
The penalty principle is: when the access subject selects a dishonest access strategy, the incentive constraint mechanism punishs the access subject by reducing the profit value of the access subject, and reduces the related trust evaluation value of the access subject, so that the access subject does not have enough trust value to meet the minimum trust threshold of the applied access permission;
after the fraudulent access behavior occurs, the penalty incentive constraint mechanism optionally makes the following policy choices:
strategy a: (mild punishment strategy) improves the relevant access trust threshold value theta of the access object to the access subject, reduces the income U of the interaction entity, and reduces the relevant trust evaluation value of the access object to the access subject
And (b) strategy: (strict penalty policy) rejection when integrating the trust assessment value TO→S<Theta or the number of times k > N that a dishonest access action occursset，NsetSetting a maximum tolerance minimum trigger value for an access control mechanism in advance, and forbidding the access to the object forever;
the strategy selection is adopted by the access object according to the harmfulness of the dishonest access behavior of the access subject as follows:
the expected loss of the dishonest access agent in the tth interaction stage is the sum of the losses of the stages, namely:
wherein, beta E (0,1) is a penalty factor, the value of which is determined by each factor of the network in the open network environment, Us(k) Loss of revenue at stage k for visiting entity node SLosing;
substituting relevant parameters in access control game payment matrix based on trust into Us_loss(t) to obtain:
if the network interaction entity node S is in the process of changing from the honest interaction state to the dishonest state, the maximum profit loss U of the network interaction entity node S is calculated through game analysiss_lossThereby obtaining the U after the dishonest behavior of the user occurss(t+1)＝Us(t)-Us_loss(T) value, evaluating comprehensive trust evaluation value TO→SJudging whether the value is smaller than a threshold value theta; the time period n of the penalty stage is defined, so that the strength of the system mechanism for punishing the dishonest access behavior is enhanced by adjusting the value of n through the system.
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| CN103532967A (en) * | 2013-10-23 | 2014-01-22 | 北京工业大学 | Trust quantification method based on subject access behavior |
| CN103618731A (en) * | 2013-12-05 | 2014-03-05 | 北京工业大学 | Access control method based on dynamic game |
| CN103916392A (en) * | 2014-03-31 | 2014-07-09 | 北京工业大学 | Entity recommendation trust calculation method based on reward and punishment factor and evaluation credibility |
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| US9934048B2 (en) * | 2016-03-29 | 2018-04-03 | Intel Corporation | Systems, methods and devices for dynamic power management of devices using game theory |
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| CN103532967A (en) * | 2013-10-23 | 2014-01-22 | 北京工业大学 | Trust quantification method based on subject access behavior |
| CN103618731A (en) * | 2013-12-05 | 2014-03-05 | 北京工业大学 | Access control method based on dynamic game |
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