CN109548029A - A kind of two-stage method for trust evaluation of nodes of Wireless Sensor Networks - Google Patents
A kind of two-stage method for trust evaluation of nodes of Wireless Sensor Networks Download PDFInfo
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- CN109548029A CN109548029A CN201910019272.9A CN201910019272A CN109548029A CN 109548029 A CN109548029 A CN 109548029A CN 201910019272 A CN201910019272 A CN 201910019272A CN 109548029 A CN109548029 A CN 109548029A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
- H04W12/12—Detection or prevention of fraud
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
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Abstract
The present invention discloses a kind of two-stage method for trust evaluation of nodes of Wireless Sensor Networks, carries out trust evaluation for the node that behavioral data obscures, prevents attack of the malicious node to sensor network.Specific step is as follows: firstly, to the behavioral data deblurring that evaluated node directly monitors, to obtain node direct trust value；Secondly, combining direct, history and the final trust value of recommendation trust data assessment node；Then, sample of establishing trust repeatedly is assessed, and introduces cloud model and establishes normal state trust cloud, assesses foundation as the final reliability rating of node；Finally, the criteria for classifying trusts cloud group according to actual needs, and cloud is trusted using classification method matched node optimality criterion is simplified, so that it is determined that node reliability rating.The present invention analyzes influence of the behavioral data ambiguity to trust evaluation, constructs two-stage Trust Valuation Model, including fuzzy reasoning, multi-source trust Intelligent Fusion and trust cloud reconstruct and classification, to improve the Evaluation accuracy of node reliability rating.
Description
Technical field
The present invention relates to the faith mechanism technical field of the network information security more particularly to wireless sensor network nodes
Method for evaluating trust.
Background technique
Wireless sensor network is especially subject to physical entrapment since the particularity of its application environment causes its vulnerable
And become compromise node, and traditional safe practice not can be used directly and solve the problems, such as Security routing in wireless sensor network,
Therefore security mechanism must be added.Node is endowed certain trust value and reflects its reliability, and the past part of the node or
The foundation that all behavior evidences will be assessed as trust value.However quality of wireless channel has unstability, and node failure
Also random to occur, there is certain ambiguity and randomness by the behavioral data that dynamic monitoring obtains.How to monitoring
Carrying out trust evaluation with the behavioral data of ambiguity and randomness is that one, wireless sensor network security field urgent need solves
The problem of.
Summary of the invention
Present invention aims at solving above-mentioned the deficiencies in the prior art, a kind of Wireless Sensor Networks are proposed
Two-stage method for trust evaluation of nodes, this method analyzes wireless channel unreliability and node malfunctions at random or failure is to trust
The influence of assessment constructs two-stage Trust Valuation Model, trusts Intelligent Fusion and trust by behavioral data fuzzy reasoning, multi-source
Cloud reconstruct and classification, influence of the ambiguity and randomness for solving to trust evidence to Evaluation accuracy.
The technical scheme adopted by the invention to solve the technical problem is that: a kind of two-stage section of Wireless Sensor Networks
Point method for evaluating trust, this method comprises the following steps:
Step 1: the behavioral data directly monitored to evaluated node is handled through fuzzy system, is obtained to evaluated section
The direct trust value of point；
The data obtained by behavior dynamic monitoring system, i.e. average delay (Average Delay, AD) and data packet pass
Defeated rate (Packet Delivery Rate, PDR) first passes around and trusts fuzzy reasoning subsystem processes to obtain the direct of node
Trust evaluation value.Fuzzy inference system includes that blurring, fuzzy reasoning and de-fuzzy handle three parts:
(1) blurring is to convert corresponding mould according to AD and PDR for the trust of input according to the fuzzy set in knowledge base
Paste set；
(2) fuzzy reasoning is will to trust the fuzzy set that trust value is mapped to according to fuzzy set according to fuzzy rule；
(3) de-fuzzy is will to trust fuzzy set to be converted into specific trust numerical value.
Step 2: the history and recommendation trust data for the direct trust value combination node that step 1 is obtained are obtained to quilt
Assess the final trust value of node；
The direct trust data that obtains through Fuzzy Processing is simultaneously insufficient, it is necessary to further combined with the history and recommendation of node
These three types of trust datas are carried out fusion treatment realization and comprehensively assessed node trust by trust data, specific fusion process point
Three steps:
(1) design is reasonable trusts prediction model, realizes the prediction to current trust value according to historical trusted data, thus
Obtain prediction trust value TF。
(2) according to the Value Realization of trust recommendation node to the weighting fusion treatment of recommendation trust data, to be pushed away
Recommend trust value TR。
(3) reasonable adaptive weighted system is designed, is realized to direct trust value TD, prediction trust value TFAnd recommendation
Appoint value TRWeighting fusion treatment, to obtain the final trust value T of evaluated node.
Step 3: repeatedly assessing obtained trust sample combination cloud model to normal state trust cloud reconstruct, as section by utilizing
The foundation of the final reliability rating assessment of point；
The problems such as in view of wireless channel poor reliability and node random fault, repeatedly assesses obtained letter by utilizing
Appoint sample combination cloud model to trust cloud reconstruct to normal state, can remove random error or mistake in single evaluation.In this project
The reliability rating evaluation process of node is carried out with taking turns, wherein each round several times assesses node trust value, obtains k
Current trust value T1、T2、…、Tk, these trust datas as sample and are combined into trust cloud model, node can reconstructed just
State trusts cloud, using the foundation as the final reliability rating assessment of node.The main purpose of this method is to avoid becoming because of network topology
Change, channel reliability difference and the problems such as node random fault caused single evaluation random error or mistake, with as far as possible
Improve the precision assessed node reliability rating.
Step 4: standard credit cloud being constructed according to the division combination normal cloud model of practical reliability rating, using with low multiple
The method for classifying modes of miscellaneous degree trusts cloud classification to node, to obtain best match standard credit cloud to determine that node is trusted
Grade.
Compared with prior art, the invention has the following beneficial technical effects:
The present invention analyzes wireless channel unreliability and node and malfunctions at random or influence of the failure to trust evaluation, structure
Two-stage Trust Valuation Model is built, Intelligent Fusion is trusted by behavioral data fuzzy reasoning, multi-source and trusts cloud reconstruct and classification,
Solve the influence of the ambiguity and randomness of trust evidence to Evaluation accuracy.
Detailed description of the invention
Fig. 1 is node Trust Valuation Model figure.
Fig. 2 is fuzzy inference system figure.
Fig. 3 is behavioral data fuzzy set degree of membership functional arrangement.
Fig. 4 is trust value fuzzy set degree of membership functional arrangement.
Fig. 5 is degree of membership, trusts fuzzy set curve and x-axis intersecting area area schematic diagram.
Fig. 6 is that multi-source trusts fusion process figure.
Fig. 7 is that node history trusts change curve.
Fig. 8 is reliability rating estimation flow figure.
Table 1 is fuzzy reasoning table.
Specific embodiment
The present invention is described in detail with reference to the accompanying drawings and examples.
As shown in Figure 1, being needed the present invention provides a kind of two-stage method for trust evaluation of nodes of Wireless Sensor Networks
By the trust evaluation process of two-stage four-stage.Wherein first order Trust Valuation Model includes trusting fuzzy reasoning and multi-source
Trust fusion；Second level Trust Valuation Model includes trusting cloud reconstruct and trusting cloud classification, and estimation flow includes the following steps:
Step 1: the behavioral data directly monitored to evaluated node is handled through fuzzy system, is obtained to evaluated section
The direct trust value of point；
The data obtained by behavior dynamic monitoring system, i.e. data packet mean transit delay AD and Successful transmissions rate PDR,
It first passes around and trusts fuzzy reasoning subsystem processes to obtain the direct trust evaluation value of node.Fuzzy reasoning system as shown in Figure 2
System handles three parts comprising blurring, fuzzy reasoning and de-fuzzy:
(1) blurring is to convert corresponding mould according to AD and PDR for the trust of input according to the fuzzy set in knowledge base
Paste set；
The fuzzy set subordinating degree function of behavioral data is as shown in Figure 3.The wherein subordinating degree function of fuzzy set " low " and "high"
Curve be it is trapezoidal, the subordinating degree function curve of fuzzy set " moderate " is triangle, and design parameter a, b, c and d are according to practical application
It determines, but the sum of cross sectional area that must assure that three subordinating degree function homologous threads and x-axis is equal to 1.
(2) fuzzy reasoning is will to trust the fuzzy set that trust value is mapped to according to fuzzy set according to fuzzy rule；
The corresponding fuzzy set subordinating degree function of trust evaluation value is as shown in Figure 4.Wherein fuzzy set " completely distrust " and
The subordinating degree function curve of " trusting completely " is triangle, the subordinating degree function of fuzzy set " moderate distrust " and " moderate trust "
Curve be it is trapezoidal, each fuzzy membership function parameter is specifically determined according to practical situations, but must assure that all degrees of membership
The sum of area of curve and x-axis intersecting area is equal to 1.Fuzzy rule is specifically defined as shown in table 1.
(3) de-fuzzy is will to trust fuzzy set to be converted into specific trust numerical value.
De-fuzzy processing step is as follows:
1. (being expressed as v according to specific behavioral data AD and PDR valueADAnd vPDR), combine the behavioral data in Fig. 3
Fuzzy set subordinating degree function finds out three pairs of degrees of membership of corresponding fuzzy set " low ", " moderate " and "high": (DL respectivelyAD,DLPDR)、
(DMAD,DMPDR)、(DHAD,DHPDR)。
2. select 4 fuzzy rules (such as regular Isosorbide-5-Nitrae, 5,9) to respectively correspond 4 groups of trust value fuzzy sets from table 1, will 1. in
Three pairs of obtained degrees of membership are combined into 4 pairs and respectively correspond 4 fuzzy rules: (DLAD,DHPDR)、(DLAD,DMPDR)、(DMAD,
DMPDR)、(DHAD,DLPDR)。
3. taking the minimum value of 4 pairs of degrees of membership respectively, 4 groups of trust value fuzzy sets are mapped to, are asked respectively and x-axis intersecting area
Area ACNT、AMNT、AMTAnd ACT, as shown in Figure 5.
The behavioral data directly monitored to evaluated node is handled through fuzzy system, is finally obtained to evaluated node
Direct trust value TD, it is expressed as follows:
TD=ACNT+AMNT+AMT+ACTFormula (1)
Step 2: the history and recommendation trust data for the direct trust value combination node that step 1 is obtained are obtained to quilt
Assess the final trust value of node；
The direct trust data that obtains through Fuzzy Processing is simultaneously insufficient, it is necessary to further combined with the history and recommendation of node
These three types of trust datas are carried out fusion treatment realization and comprehensively assessed node trust by trust data, multi-source letter as shown in Figure 6
Appoint three steps of fusion process point:
(1) design is reasonable trusts prediction model, realizes the prediction to current trust value according to historical trusted data, thus
Obtain prediction trust value TF；
By combining distribution function appropriate to be fitted, it can be achieved that the pre- of current trust value node historical trusted data
It surveys, and history trust distribution curve should have slow raising speed drop characteristic, make congenial sexual assault to avoid malicious node.Work as node
It is always maintained at cooperative attitude, then corresponds to trust value and slowly rises；And malicious act trust value if occurring declines rapidly.It is this
The trust that mode can effectively reduce malicious node is expected, prevents the purpose of its selection cooperation only for that can cater to trust evaluation
System.
The history trust value for having n item to be evaluated node: Th is assumed according to chronological order1、Th2、...、Thn, wherein
ThnFor nearest history trust value.If ThiAnd Thj(i < j) is to detect the final trust value obtained after malicious act, then this n item
The variation tendency of history trust value is as shown in Figure 7.
The history trust value variation tendency of node should meet changing rule shown in Fig. 7: for continuous cooperation behavior, section
The trust value of point will be slow increase, when reaching certain threshold value TthrWhen curve there is inflection point, subsequent trust value increases to always infinitely
Close to 1；When there is uncooperative behavior, trust value declines rapidly, if there is continuous uncooperative behavior, can drop to minimum value
0.Finding suitable secondary or exponential Function Model by segmentation can be obtained the history trust distribution curve of similar Fig. 7, in conjunction with
Currently stored historical trusted data is fitted curve design parameter, so that obtaining complete history trusts distribution curve equation,
It is final to obtain the prediction trust value T of evaluated node using distribution curve equationF。
(2) according to the Value Realization of trust recommendation node to the weighting fusion treatment of recommendation trust data, to be pushed away
Recommend trust value TR；
When using neighbor node recommendation trust data, it is necessary to the trust state for considering recommended node itself, for having
The recommended node of higher reliability rating should give its recommending data higher value, and with value for according to recommending data fusion
Processing obtains recommendation trust TR.Assuming that the recommendation trust that m neighbor node is obtained in assessment node one is TR1、TR2、...、
TRm, and the current trust value of m neighbor node for assessing node storage is T respectivelyN1、TN2、...、TNm, then it is evaluated node
Recommendation trust TRIt can calculate as follows:
(3) reasonable adaptive weighted system is designed, is realized to direct trust value TD, prediction trust value TFAnd recommendation
Appoint value TRWeighting fusion treatment, to obtain the final trust value T of evaluated node.
It is main to consider two aspects in system adaptive weighted for recommendation trust design data:
1. there is the fitting precision when history trust distribution curve of slow raising speed drop characteristic using historical trusted data fitting,
The average variance of i.e. practical trust value and fitting trust value.
2. the size of the quantity of trust recommendation node and its average trust value.
When historical trusted data fitting average variance it is bigger, show that fitting precision is lower, trust value predicted obtained from
Precision is also lower, therefore predicts trust value T for assigningFLesser weight；When trust recommendation number of nodes is less or recommends section
When the average trust value of point is smaller, show that the recommendation trust data of recommended node have lesser utility value, therefore imparting is pushed away
Recommend trust value TRLesser weight.Final trust value can calculate as follows:
T=wD·TD+wF·TF+wR·TRFormula (3)
Wherein wD、wFAnd wRRespectively correspond to direct trust value TD, prediction trust value TFAnd recommendation trust TRWeight.
Step 3: repeatedly assessing obtained trust sample combination cloud model to normal state trust cloud reconstruct, as section by utilizing
The foundation of the final reliability rating assessment of point；
The problems such as in view of wireless channel poor reliability and node random fault, repeatedly assesses obtained letter by utilizing
Appoint sample combination cloud model to trust cloud reconstruct to normal state, can remove random error or mistake in single evaluation.In the present invention
The reliability rating evaluation process of node is carried out with taking turns, wherein each round several times assesses node trust value, obtains k
Current trust value T1、T2、…、Tk, as shown in figure 8, these trust datas as sample and are combined trust cloud model, Ke Yichong
The normal state of structure node trusts cloud, using the foundation as the final reliability rating assessment of node.The main purpose of this method be avoid because
Network topology change, channel reliability difference and the problems such as node random fault caused single evaluation random error or mistake
Accidentally, to improve the precision to the assessment of node reliability rating as far as possible.
Present invention introduces normal cloud model theories, utilize k trust evaluation sample of each round in conjunction with specific generating algorithm
Value constructs the water dust for meeting normal distribution, thus by cognition randomness and ambiguity be included in probabilistic framework and uniformly retouch
It states.Normal cloud model introduces three numerical characteristics based on normal distribution and Gauss member function to describe, i.e. desired value
Ex, entropy EnWith super entropy He.Its expected value ExIt is the center of gravity of number field where all water dusts, represents the basic certainty of qualitativing concept；
Entropy EnIt is probabilistic measurement to qualitativing concept, reflects the randomness and ambiguity of concept；Super entropy HeReflect entropy En's
Uncertainty degree.
Evaluated node trusts water dust TCDIt indicates, T can be obtained according to normal state Clouds theoryCD~N (Ex,Eσ 2), and Eσ~N
(En,He 2).The subordinating degree function of normal state water dust is expressed as follows:
Using k trust evaluation value in every wheel reliability rating assessment as k water dust of normal cloud model, unite in conjunction with mathematics
Meter method obtains three numerical characteristic Ex、EnAnd He, and then can determine that the normal state of evaluated node trusts cloud completely.Wherein believe
Cloud is appointed it is expected ExIt can calculate as follows:
Wherein TiIndicate the i-th trust evaluation value in a certain wheel reliability rating assessment.
Trust cloud entropy EnIt may be expressed as:
Trust the super entropy H of cloudeIt may be expressed as:
Wherein TS 2The sample variance that water dust is trusted for k times, may be expressed as:
Step 4: standard credit cloud being constructed according to the division combination normal cloud model of practical reliability rating, using with low multiple
The method for classifying modes of miscellaneous degree trusts cloud classification to node, to obtain best match standard credit cloud to determine that node is trusted
Grade.
Assuming that trust value section [0,1] is divided into M standard credit grade subinterval, M reliability rating is respectively represented
TG1,TG2,…,TGM, wherein m-th of subinterval is represented by [Tmin m,Tmax m], Tmin mIndicate the trust value lower limit in the subinterval,
Tmax mIndicate the trust value upper limit.It is corresponding each trust subinterval and will construct a standard credit cloud, m-th trust subinterval
Standard credit cloud TCD mThree numerical characteristics be denoted as Ex m、En mAnd He m.Due to it is expected Ex mIt is the central value of standard credit cloud, therefore
It can calculate as follows:
Entropy En mThe uncertainty for representing standard credit water dust is determined according to the randomness of water dust and ambiguity, can be calculated
It is as follows:
En m=Prf(Tmax m-Tmin m) formula (10)
Wherein PrfFor standard water dust uncertain parameters (0 < Prf≤ 1) it, is determined according to concrete application situation, value is got over
The big randomness for indicating water dust and ambiguity are bigger.
Super entropy He mThe thickness for representing standard credit cloud indicates the ambiguity of trust value, and the more big then trust value of value is fuzzyyer,
Its calculating formula is as follows in the present invention:
The reliability rating of evaluated node is obtained, it need to be compared and trust cloud TCDWith M standard credit cloud TCD 1,TCD 2,…,
TCD MSimilarity, to obtain best match standard credit cloud, so that it is determined that node reliability rating.However the meter of sensor node
Calculation ability is limited, if will compared with each standard credit cloud similarity, calculation amount it is considerable.In this patent will
Simplify sorting algorithm, M standard credit cloud is obtained using mode identification method first and wherein any one standard credit cloud is (false
If TCD 1) similarity, be denoted as DS 1、DS 2、…、DS M；Then relatively more evaluated node trusts cloud TCDWith standard credit cloud TCD 1Phase
Like degree, it is denoted as DS；The finally similarity D of relatively more evaluated nodeSWith the Euclidean distance between M standard similarity, distance is selected
The corresponding standard credit cloud of a smallest similarity is as best match cloud.
The characteristics of present invention is according to massive wireless sensor distributed frame establishes one kind towards wireless sensor
The two-stage method for trust evaluation of nodes of network, it is contemplated that the unreliability and node random fault or malfunction etc. of wireless channel
Problem carries out fusion treatment to direct, history and recommendation trust data by establishing two-stage Trust Valuation Model, with
Improve the confidence level of trust evaluation.
In first order Trust Valuation Model, the Fuzzy Inference Model directly trusted node is initially set up, eliminates behavior
Influence of the ambiguity and randomness of data to Evaluation accuracy；Then node historical trusted data is fitted, construction has
The history of slow raising speed drop characteristic trusts distribution function, to establish the forecasting mechanism trusted node, malicious node is avoided to cater to
Trust evaluation system and the speculative attack made；Then to trust recommendation node, itself credit worthiness is analyzed, and is established
To the fusion treatment model of recommendation trust data, prevent malicious node from carrying out attack row to trust evaluation system using recommendation information
For；The reliability of ultimate analysis multi-source trust data, and the Fusion Model to multi-source trust data is established with this, to obtain essence
The high node trust evaluation sample value of exactness.
In the Trust Valuation Model of the second level, the trust sample value that first obtains first order trust evaluation as water dust,
Normal state is rebuild in conjunction with cloud model and trusts cloud, to reject the water dust with randomness and ambiguity and obtain more complete trust number
According to；Then standard credit cloud is established according to the division of standard credit grade, the reference standard that will be assessed as node reliability rating；
The method for classifying modes with low complex degree for being suitable for sensor network is finally proposed, thus according to standard credit cloud to node
Trust cloud to classify, to obtain node best match reliability rating.
1 fuzzy rule of table
Claims (8)
1. a kind of two-stage method for trust evaluation of nodes of Wireless Sensor Networks, it is characterised in that assessed by the first order
It to enough sample datas, is established in the assessment of the second level using sample data and trusts cloud, and introduce method for classifying modes
Determine the final reliability rating of node.It the described method comprises the following steps:
Step 1: the behavioral data directly monitored to evaluated node is handled through fuzzy system, is obtained to evaluated node
Direct trust value；
Step 2: the history and recommendation trust data for the direct trust value combination node that step 1 is obtained are obtained to evaluated
The final trust value of node；
Step 3: repeatedly assessing obtained trust sample combination cloud model to normal state trust cloud reconstruct, most as node by utilizing
The foundation of whole reliability rating assessment；
Step 4: standard credit cloud being constructed according to the division combination normal cloud model of practical reliability rating, using with low complex degree
Method for classifying modes to node trust cloud classification, to obtain best match standard credit cloud to determine node reliability rating.
2. a kind of two-stage method for evaluating trust of Wireless Sensor Networks node according to claim 1, feature
Be, in the step 1, establish the Fuzzy Inference Model directly trusted node, eliminate behavioral data ambiguity and with
Influence of the machine to Evaluation accuracy.
3. a kind of two-stage method for evaluating trust of Wireless Sensor Networks node according to claim 1, feature
It is, in the step 2, node historical trusted data is fitted, constructs the history with slow raising speed drop characteristic and trust
Distribution function avoids malicious node from catering to trust evaluation system and the throwing made to establish the forecasting mechanism trusted node
Machine sexual assault behavior.
4. a kind of two-stage method for evaluating trust of Wireless Sensor Networks node according to claim 1, feature
It is, in the step 2, to trust recommendation node, itself credit worthiness is analyzed, and establishes the fusion to recommendation trust data
Model is handled, prevents malicious node from carrying out attack to trust evaluation system using recommendation information.
5. a kind of two-stage method for evaluating trust of Wireless Sensor Networks node according to claim 1, feature
It is, in the step 2, analyzes the reliability of multi-source trust data, and the fusion mould to multi-source trust data is established with this
Type, to obtain the high node trust evaluation sample value of accuracy.
6. a kind of two-stage method for evaluating trust of Wireless Sensor Networks node according to claim 1, feature
It is, in the step 3, the trust sample value that first order trust evaluation is obtained is rebuild just as water dust in conjunction with cloud model
State trusts cloud, to reject the water dust with randomness and ambiguity and obtain more complete trust data.
7. a kind of two-stage method for evaluating trust of Wireless Sensor Networks node according to claim 1, feature
It is, in the step 4, standard credit cloud is established according to the division of standard credit grade, will be commented as node reliability rating
The reference standard estimated.
8. a kind of two-stage method for evaluating trust of Wireless Sensor Networks node according to claim 1, feature
It is, in the step 4, using the method for classifying modes with low complex degree for being suitable for sensor network, thus according to
Standard credit cloud trusts cloud to node and classifies, to obtain node best match reliability rating.
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