Research on Dependable level in Network Computing System Yongxia Li 1, a, Guangxia Xu 2,b and Shuangyan Liu 3,c


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1 Applied Mechaics ad Materials Olie: ISSN: , Vols , pp doi:0.408/ 04 Tras Tech Publicatios, Switzerlad Research o Depedable level i Network Computig System Yogxia Li, a, Guagxia Xu,b ad Shuagya Liu 3,c School of Iformatio ad Egieerig, Chogqig City Maagemet College, Chia School of Software Egieerig, Chogqig Uiversity of Posts ad Telecommuicatios,Chia 3 School of Commuicatio ad iformatio Egieerig, Chogqig Uiversity of Posts ad Telecommuicatios, Chia a b c Keywords: Network computig system, cloud model, iterval ituitioistic fuzzy theory, credible level, depedable moitorig. Abstract. The most weakess lik i credible moitorig is that how to process multidimesioal dyamic behavior data effectively. System behavior moitorig ofte eeds to deal with differet kids of behavior data ad those data ca adopt status sapshot i multidimesioal vector form to express. Obviously, data has strog useful kowledge iformatio, which is regarded as a kid of classificatio ability. So we eed to fiish the mappig ad classificatio betwee a variety of etwork behavior sapshot ad depedable level. This paper itroduces o etwork state sapshot owig the characteristics of high dimesio, heterogeeous ad dyamic ad uses the theory of iterval ituitioistic fuzzy to judge credible degree i the system ad geerate behavior quality trust level of odes. Itroductio The abormal behavior caused by various etities of their ow fault ad exteral security hidde dager has become more ad more promiet i etwork computig eviromet, which leads to people s distrust i etwork service[,]. Based o the basic thought of structure techology with depedable moitorig is to iject moitorig ability ito software ad hardware system that has o moitorig ability origially, thus moitorig behavior ad state of system so that we ca determie whether the system is abormal ad fially improve the credible ature of system, such as maitaiability, flexibility ad availability. The trust maagemet research i trusted computig shows that the key procedure of dyamic trust decisio is to moitor ad process of dyamic behavior data[3,4]. The most weakess lik i credible moitorig is that how to process multidimesioal dyamic behavior data effectively. System behavior moitorig ofte eeds to deal with differet kids of behavior data ad those data ca adopt status sapshot i multidimesioal vector form to express. Obviously, data has strog useful kowledge iformatio, which is regarded as a kid of classificatio ability. So we eed to fiish the mappig ad classificatio betwee a variety of etwork behavior sapshot ad depedable level, this paper focuses o etwork state sapshot owig the characteristics of high dimesio, heterogeeous ad dyamic ad uses the theory of iterval ituitioistic fuzzy to judge credible degree i the system ad geerate behavior quality trust level of odes. Credible Ability Based O Cloud Model This paper uses cloud model to idetify the eigematrix of credible supervisig, ad geerates the cocept of cofidece level based o cloud model through cloud trasform by cosiderig the multidimesioal properties of credible supervisig. Respectively,we geerate the cofidece level by usig differet idetifyig algorithms agaist the feature properties i istat ad a period, furthermore, we determie the cofidece level of feature properties by usig similarity algorithm ad comprehesive sythesis operator. All rights reserved. No part of cotets of this paper may be reproduced or trasmitted i ay form or by ay meas without the writte permissio of Tras Tech Publicatios, (ID: , Pesylvaia State Uiversity, Uiversity Park, USA/05/6,04:0:56)
2 06 Applied Sciece, Materials Sciece ad Iformatio Techologies i Idustry A. The cofidece level determiatio of period Feature properties based o the cloud model The moitor keeps several discrete status sapshots. So, we ca determie the cofidece levels of curret status ad curret time. The property feature durig a while geerate credible cloud algorithm, ad we take backward cloud algorithm with ucertaity. Through backward cloud algorithm, we ca gai the actual trusted cloud model of dimesio period feature properties, we ca adopt similar cloud algorithm to decide which cofidece level these credible cloud models belog to. N feature properties of credible level cloud models: C { C ( Ex, E, He ), C ( Ex, E, He )}( j =,,, ), As the base, make comparisos j j j j j jm jm jm jm betwee period feature properties of cloud model, T{ T ( Ex, E, He ),, T ( Ex, E, He )}, with credible level cloud model, though the compariso ca we obtai the credible of each property. The more the cloud i the overlappig of C cloud is, the higher the η is, ad the more close ad similar the two clouds are. The similarity of clouds is a ucertaity cocept, ad the similarity ca exactly reflect, more ad more, the degree of closeess betwee two clouds by the icreasig of cloud droplet. B. The comprehesive cofidece level compoud of state sapshot I the system of multidimesio feature trusted idetificatio, the goal of cloud compoud is to get a comprehesive trusted cloud model which is compouded by all the cofidece level cloud model. Defiitio Assumig there are two trusted cloud T ( Ex, E, He ) ad T ( Ex, E, He ), ad we defie the compoud of T ad T as T = T T. (if Ex > Ex ), the: Ex + Ex Ex = = µ Ex µ µ Ex E = E + E Ex Ex Ex Ex Ex µ µ Ex He = He + He Ex Ex Ex Ex Accordig to the two defie below, we ca get the compoud formula of cloud model related to idexes to a etity. T ( Ex, E, He) = T T,, T, T T T3 is ordered by expectatio from low to high. I the formula, Ex + Ex + + Ex Ex = = ρ( Exk ρ Exk + ) Ex Ex ρ Exk Exk + ρ Ex Ex () E = E + + Ek + Ek E Ex Ex Ex Ex Ex Ex Ex Ex Ex Ex ρ Ex Ex ρ Ex Ex He = He + + He + He + + Ex Ex Ex Ex Ex Ex Ex Ex He k k+ k k + Fially we ca gai the comprehesive cloud model ad the credible level through similarity cloud algorithm. Experimet ad Aalysis A. The geeratio of Characteristic attributes of credibility level based o cloud model Through the statistical aalysis of each dimesio property of sample data, we ca gai the frequecy distributio fuctio, o the other had, we ca obtai the umber of discrete coceptio by cloud trasform algorithm. Figure x shows credible level cocept cloud of two characteristics. ()
3 Applied Mechaics ad Materials Vols Fig. Differet characteristics of the depedable level cloud model As is show i Fig., each dimesio characteristic has itself uique credibility cloud model ad also itself distict cloud characteristic values. The characteristic of each dimesio is particularly obvious, which is differet from ay previous recogitio algorithm that ca ot distiguish characteristic behavior. Each dimesio characteristic has itself uique credibility cloud model, which are good for users or maagers to make a local observatios i order to take the best measures to deal with the curret system status. At the same time, comprehesive trusted level of the cloud model gives a very clear effect to sese the curret system state, which grasps the system s decisio strategy from a holistic perspective. B. Trusted level recogitio rate Usig differet samples as the traiig set ad uder the circumstaces of differet umber of sample, we ca obtai compariso for cloud model, PCA ad LPP reliable moitorig methods of recogitio about system trusted level o the trasiet performace by ijectig the icidet, as is show i Fig., recogitio rate is 66% ad relatively low whe the umber of traiig set is 300, but with the icreasig of sample, recogitio rate is icreasig gradually, at the same time, PCA uses miimal sample iformatio ad oly gai miimum sample mea square error. O the other had, whe the umber of sample is 300, PCA recogitio rate is oly 68%, with the icrease of time, recogitio rate is 83% whe the umber of sample is 3000, but it is difficult to adapt this recogitio rate to practical applicatio eviromet. LPP utilizes the iteral structure of data, with the icreasig of the umber of sample, structure of origial sample teds to stable ad shows the great improvemet, LPP algorithm is more 7% tha the PCA algorithm whe the umber of sample is 3000, which shows that it is very importat for trusted moitorig to classify iformatio. The cloud model ofte make a idetificatio based o statistical, so eve the recogitio rate is low whe the umber of sample is low, with the icreasig of sample, the recogitio rate is higher tha PCA ad LPP, so it has the practical sigificace i credible moitorig algorithm. cloud model sample umber Fig. Istataeous characteristic depedable recogitio rate compariso
4 08 Applied Sciece, Materials Sciece ad Iformatio Techologies i Idustry recogitio rate (%) Fig.3 A period of time characteristic depedable recogitio rate compariso The compariso of trusted recogitio rate for a period of time betwee cloud model of characteristic attributes ad classic highdimesioal recogitio algorithm PCA ad LPP is show i Fig.3.There are still some differeces betwee trasiet performace credible recogitio ad credible recogitio for a period of time. The period of time characteristic depedable recogitio uses the similarity recogitio algorithm ad its stability has icreased, so the fial recogitio rate has improved steadily. Summary This paper uses data mimig based o cloud model, aalysis the trusted level cocept ad gives a formal descriptio of status of system. O this basis, we explores cloud model geeratio algorithm for oedimesio s trusted level, cloud model geeratio algorithm for a period of time, ad cloud model similarity algorithm ad so o. Experimetal results show that this method ca hadle the moitorig, judgmets ad idetificatio of complex multidimesioal data. Refereces [] YigHua Mig. Network fault tolerace ad security research. Joural of computer.003, 6(9): [] Chuag Li, Xuehai Peg. Research o Trustworthy Network. Joural of computer.005,8(5), [3] Martiez A M, Kak A C. PCA versus LDA. IEEE Trasactios o Patter Aalysis ad Machie Itelligece. 00, 3():833. [4] Gumus E, Kilic N, Sertbas A, et al. Evaluatio of face recogitio techiques usig PCA, wavelets ad SVM. Expert Systems with Applicatios.00, 37(9): [5] Jiag Rog. A Study o Time Series Data Miig [6] Du Yi. Research ad Applicatio of Assoeiatio Rule i Data Miig [7] KaiChag Di. The framework of spatial data miig ad kowledge discovery. Geomatics ad Iformatio Sciece of Wuha Uiversity,vol(4):3833(999). [8] XiRe Zhao, Xiuya Peg, Guagyu Jiag.Chia Northeast Power Network Shortterm Load Forecastig Based o Neural Network. Joural of system simulatio, 006,8(6):
5 Applied Sciece, Materials Sciece ad Iformatio Techologies i Idustry 0.408/ Research o Depedable Level i Network Computig System 0.408/ DOI Refereces [3] Martiez A M, Kak A C. PCA versus LDA. IEEE Trasactios o Patter Aalysis ad Machie Itelligece. 00, 3(): / [4] Gumus E, Kilic N, Sertbas A, et al. Evaluatio of face recogitio techiques usig PCA, wavelets ad SVM. Expert Systems with Applicatios. 00, 37(9): /j.eswa
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