International Journal of Applied Mathematics and Soft Computing

Transcription

1 Internatonal Journal of Appled Mathematcs and Soft Computng Presdent: Zhang Jnrong Chef Planner: Hu Yuejuan Executve Chef Edtor: Chen Lhua, Cu Shuzhen, Shuyu Edtoral Board: L Shu, Xu Ya, Gao Shufen, Ya Hu, Su Daq, Albert, Yu Boru, Souza, Pe Lu, Chun Hao, L Dhda, Meng Yu Audt Commttee: Ln Lchan, Xu Ljuan, Dong Pewang, Su Janmn, Al Coskun, You Wenyng, Chen Xngeng, An Xn, Yan Yanhu, Tang Mng, Yang Mng, Zh Zhong, Xao Han, Sun Wenjun, Yoon-seon Lee, Bom Sook Km, Chang-Duk Jun, Jn Hong Cha, Tan Ker Kan, Tan-Hua Huang, Jorge Serra Colna, Yong Shao, Vkram Kate Publsher: HongKong New Century Cultural Publshng House Address: Unt A1, 7/F, Cheuk Nang Plaza, 50 Hennessy Road, Wancha, Hong Kong Tel: Fax: Copyrght 016 HongKong New Century Cultural Publshng House -All Rghts Reserved

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3 Contents Research on Data Network Equvalence Based on Real Estate Hufeng LONG (1) Spectrum Allocaton of Cogntve Wreless Network based on Immune Clonal Optmzaton Algorthm Hean LIU, Haogu CHEN, Zhke KUANG (6) Analyss on the Securty of Accountng Informaton under the Cloud Xnyu DU (10) Applcaton of Neural Network based on Clusterng Analyss Haogu CHEN (14) Research of Wreless Network based on Immune Genetc Algorthm Gusheng SHU (18)

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5 Research on Data Network Equvalence Based on Real Estate Hufeng LONG Hunan Cty Unversty, Y yang Hunan , Chna Abstract: Wth the rapd development of real estate ndustry, real estate network data are also ncreasngly large, complex, not easy to manage, n order to solve ths problem, the real estate data network research and applcaton based on peer-to-peer network. Usng the prncple of data technology are analyzed, a seres of research shows that the study than tradtonal the network can be more accurate, more convenent to manage the entre real estate data, t has practcal value. Keywords: Equvalence Network; Node Trust; Informaton Flterng 1. Introducton In recent years, the rapd applcaton development of PP networks has become one of the mportant technologes of affectng the future development of the Internet. The dstrbuted archtecture of PP made t has good scalablty and flexblty, but ts open, anonymous and self-organzng propertes provde a way for the spread of vruses and junk data, and the safety problems caused wdespread concern. Blaze, who for the frst tme the concept of "trust management", the ntroducton of network servces n 1996 [1]. Subsequently, researchers PP network based on trust management model has been extensvely studed. Dependng on the structure of the current PP networks, trust models nto a centralzed trust model and dstrbuted trust model. Centralzed trust model s equpped wth a central server whch s responsble for managng the trust of all the nodes, such as PKI-based trust model []. Dstrbuted Trust Model has no central server, to determne the trust of nodes evaluated by collectng trust other nodes on the target node. Dstrbuted trust model was classfed nto global trust model and local trust model n accordance wth the trust's search. Processng to obtan global trust model need to fnd a trusted node n the network of all transactons wth the target node over node testmony [3-6]. The commonly used methods are teratve and feedback entre network. Iteratve algorthm teratve global trust value of each node n the network updated untl the trust values of all nodes to stablze based on the tradng results over tme. The more famous algorthm had early Egen Rep algorthm model, and model the dynamc Peer Trust TVM algorthm. Convergence of the algorthm and teratve overhead n large-scale network, t has been a major factor restrctng the development of ths model [4]. After the model-based feedback of each transacton, the transacton clent node evaluatons wll feedback drectly to the servce node. Typcal algorthms have TBRM algorthms and dynamc models Peer Trust PSM algorthm. When ths method of calculatng the trust s less overhead, the larger scope of malcous attack was beng dshonest feedback. How to dstngush between honest and dshonest feedback has been facng challenges such models.. Super Trust Model In Super Trust, t makes all nodes n the group as a unt to be dvded, and each group has only one super-node. Smplcty, Super Trust assume that, each node (f not specfcally stated heren "node" refers to common node) belongs to only one group (for a node belongng to a pluralty of groups whch can be seen as the node n each group there are dfferent denttes). Fgure 1 s the PP network structure wth a super node, the fgure shows the four connectons between groups, S represents super node. The fgure shows three knds of trust type, n the group s 1, settng the relatonshp between super node S p 1 and node p, and the relatonshp between p and p 3 as drect trust relatonshp, and settng the relatonshp between S p 1 and p 3 as the recommended trust relatonshp. The relatonshp between p and p 4 represents the trust relatonshp between the ordnary nodes n dfferent groups, meanwhle, we characterzed the trust relatonshp between the super nodes S p 1 and S p 3. The basc dea of Super Trust s that, the nodes establsh local trust relatonshp to target node accordng to the transacton. If the target node (for example, p 3 ) s n the same group, the node ( p ) wll store the transactons of the target node n the local. If the target node (such as p 4 ) s n other groups, then the node ( p ) wll p 1

6 feedback the outcome of the transacton to ts supergroup node (SP1). The super node S p Where Trsp expressed the trust of super node sp, 1 establshes the trust relatonshp of super node S p n the group of node p4 accordng to the feedback of node p. When the node assesses the trust value of other node wthn the same group, t wll calculate the trust value of the node n accordance wth the group's reputaton-based trust mechansm. The trust value of super nodes s calculated by the global trust n the group wth all of ts nodes. In the followng, t frst descrbes the calculatng way of trust between nodes wthn the same group. Fgure 1. The PP network structure wth super nodes.1. Calculaton of node trust Frst, t gves the calculaton methods of trust between ordnary nodes wthn the same group. We have the trust between ordnary nodes wthn the same group: Tr = λ R + (1 λ)re (1) j j j Where η s a drect trust confdence factor, η values and nteracton related to the number, the more the number of nteractons larger the value of η, 0 η 1. We can take η = h/ HLmt, where h s the node and node j, the number of nteractons between, HL mt to set the threshold number of nteractons heren value s 0... Trust calculaton of super nodes In Super Trust, we assume that the ntal network jonng the PP network nodes are trusted n the ntal stage, whch can be used as super-node, because as a whole PP network bulder and the orgnal user, there s no ulteror motve to destroy ths network. Meanwhle, we entrust super node the followng functons: nvolved n the transacton, to safeguard ther tradng results; mantenance group's node management; n addton, supernode also stores the trust nformaton across the node group transactons. The trust from the node to super node of the group s changng. In Super Trust, the trust calculaton of the node to super node s n accordance wth global trust calculaton, that Trsp = ( RR ) () k KSP k I( G ) I( G ) s a collecton of nodes wthn the group sp, R k represents the partal trust of node. Thus, to the entre group at a certan moment, the trust of super node s unque, rather than a specfc evaluaton node tself. Because trust s determned by the nteracton result and affected by that, after a certan perod of tme, there are changes n the node trust. Thus, the entre group of nodes can perodcally partcpate n the assess of the trust. The super nodes whch falls below a certan threshold can be replaced wth the backup super node replace ths super-node, so you can avod malcous nodes camouflage dentty spoofng. The selecton of backup super node s decded based on the stablty and overall performance of all members. For the new added ordnary node, we requre full trust at the begnnng of the super node wthn the group, wth the deepenng of ther nteracton, and gradually adjust to ths super node trust relatonshp. Thus, n Super Trust, the degree trust of node to super node sp s, 1, n = 1 Trsp = (3) θtrsp, n> 1 In the above equaton, ψ s a constant and 0pψ p 1, R ISP s the drect trust of node to super node sp, s s the trust evaluaton standard devaton of the set of nodes to super node. In the smulaton, ψ = 1 ( 1 ψ). Rsp s. The trust between super node sp and sp j s establshed rely on the overall trust evaluaton of nodes n sp group to sp group. The defnton s as follows..3. Ant- attack capablty j.3.1. Smple malcous nodes (SM) To the smulaton, we assume that the cooperaton node provde a credble probablty of documents to 0.98, so when the system does not have malcous node, the node's successful request for cooperaton was It knows from Fgure 3, when the malcous nodes provde only un-trusted fles, Super Trust and PD Trust can effectvely dentfy malcous nodes n small proporton of malcous nodes, so the success of co-operatve nodes transacton rate decreases slowly as the ncrease n the proporton of malcous nodes. But wth the ncrease of the proporton of malcous nodes, the Super - Trust system performance ndcates a sgnfcant advantage. The RB Trust system success rate of requests sgnfcantly reduce wth the ncrease proporton of malcous nodes, whch s because n the RB Trust system, the node nformaton or the use of local trusted frends and ther frend's recommendaton to determne the val-

7 ue of the gven node's trust. And therefore cannot trust Super Trust can effectvely dentfy slander node, leavng effectve access to nformaton for all nodes. Addtonally, most of the attacks neffectve, so when the propor- n our smulaton, cooperatve nodes may provde ton of malcous nodes reaches 0.6, t also has a hgher un-trusted fles because of an error, whle malcous successful transacton rate. The successful tradng rate nodes n order hde ts malcous behavor and provde of RB Trust system decrease rapdly wth an ncrease n credble documents wth a certan probablty, therefore, the proporton of malcous nodes slander, ths s because n RB Trust, nodes may ncorrectly assess the credblty when there are many slander nodes, the untrue of the other nodes, resultng n successful transacton and msleadng nformaton of recommended nformaton rate. becomes avalable, the system can not effectvely 1.0 dstngush these nformaton, the node trust judgment error s large, and therefore can not effectvely choose 0.9 to download the source resultng n successful transacton rate Super Trust PDT Trust RBT Trust Malcous nodes proporton Fgure. Comparson of successful transacton rate under SM Fgure 4 s the rate changes wth the successful transacton of the three types of system smulaton cycle of Super Trust, PD Trust and RB Trust n the proporton of malcous nodes 0.6. It can be seen, Super-Trust optmal system performance, PD Trust second, RB Trust worst, these show the strong ablty to resst attacks of malcous nodes of Super Trust. Successful transacton rate Fgure 3. Changes of successful transacton rate wth the perod.3.. Slander node (DM) Fgure 5 s the comparson of successful transacton rate of the three systems of Super Trust, PD Trust and RB Trust under the attack of malcous slander node. As can be seen from Fgure 5, when the proporton of malcous nodes n system s small, the system successful transacton rate of three mechansms has lttle dfference, But wth the ncrease n the proporton of malcous nodes, the Super Trust system performance ndcates a sgnfcant advantage, whch because we propose feedback flterng algorthm to flter out the cooperatve node slander publshed njustce nformaton, so Fgure 4. The comparson of successful transacton under DM. Fgure 6 s the changes of the three systems successful transacton rate of Super Trust, PD Trust and RB Trust wth smulaton cycle. It can be seen that, the system successful tradng rate of Super Trust rapdly ncrease wth smulaton cycle, and ultmately remaned relatvely stable, and t s always hgher than the other two n the entre transacton cycle, showng the strong slander effectveness of Super Trust aganst attack. Successful transacton rate Fgure 5. Changes of the successful transacton rate under DM wth the perod.3.3. Collusve fraud (CM) Fgure 7 s the comparson stuaton of successful transacton rate of the three systems Super Trust, PD Trust and RB of the malcous nodes. 3

8 In the smulaton experments, we assume that the entre front-end node s a proporton of 1% of malcous nodes. In fgure 9, we gve the successful tradng rate of cooperate nodes n Super Trust, PD Trust and RB Trust system wth a front-end node under fraudulent colluson attack. Whle wth the varous systems malcous nodes under attack fraud conspracy case were compared. Fgure 6. The comparson of successfully request rate of collusve fraud In Super Trust and PD Trust system, a malcous node s assocated wth a transacton cooperatve nodes negatve feedback, whle there have been transactons wth smlar nodes provde a hgh postve feedback. Addtonally, malcous nodes may collude wth each other frequently submtted for each other hgh postve feedback. In RB Trust, assumng a malcous node receves another node recommendaton trust request, f the nqury s smlar malcous node recommendaton trust s gven a value of 1 ; otherwse, t s -1. Fgure 8 shows the case of fraud n the colluson, when the proporton of malcous nodes s 0.6, the changes stuaton of successful tradng rate of the three types of system wth the smulaton perod. It can be seen that, the system successful tradng rate of Super Trust rapdly ncrease and eventually reman relatvely stable wth the smulaton cycle. PD Trust and RB Trust system has declned wth ncreased system performance, and the overall performance of PD Trust than RB Trust, because the feedback nformaton drectly PD Trust smple summaton of the system calculated for each node n the global trust value, when a hgh proporton of malcous nodes, the system performance s completely controlled by malcous nodes. To the RB Trust, the credblty of nformaton can determne the credblty of a gven node accordng to the local node, so the performance s better than PD Trust. Successful transacton rate Fgure 7. The changes of successful transacton rate under fraudulent colluson wth cycle.4. The collusve fraud wth a front-end node Fgure 8. The comparson of successful transacton rate under CF Under the attack of colluson fraud wth a front-end node, as the same as n the fraudulent conspracy of malcous nodes under attack, the successful transacton rate of Super Trust s better than that of PD Trust and RB Trust. But the dfference s that, n Super Trust system, the rate of successful tradng system under attack n the CM attack s better than the CF, whle n PD Trust and opposte RB Trust systems. Ths s because n the smulaton, we set the node Super Trust as long as the number of nodes n the cluster are smlar, that all nodes n the cluster as a whole s smlar to the attack of CF, malcous concealment of front-end node behavor makes t smlar to the node wth the ncreased possblty of cooperaton, and to become members of the cluster evaluaton, whch submtted a hghly malcous nodes postve comments n PD Trust and RB Trust systems, the proporton of malcous nodes n the same crcumstances, under the attack of CF, the CM system has more nodes to provde vald documents, so the system successful tradng has hgher attack rate n the CF. Fgure 10 s the comparson between colluson wth fraud and conspracy to fraud under the attack of contrast under the three systems of Super Trust, PD Trust and RB Trust whle a malcous node rato s 0.5. The system performance under the colluson attack has a front end node colluson fraud reasons as descrbed above, however, PD Trust and RB Trust opposte two mechansms, transacton success rate of the system has a front end node of colluson fraud under all the above. It s because the front end node n the sake of provdng effectve document, but PD Trust n colluson attack, the system front-end node of the hghest reputaton, and wth the ncrease of the smulaton perod, front-end node enhanced ablty to provde servces, so there cannot be effectve from the start node to dentfy malcous 4

9 front-end node capabltes, the system performance has the upward trend. 3. Concluson For the ssue of trust between the nodes n PP networks, ths paper presents a PP network trust model based on super nodes Super Trust. The model for trust ordnary nodes wthn the same group calculated usng the node nformaton and the use of local trust group belongs to recommendaton trust nformaton to determne the value of the target node's trust. Trust evaluaton super node mode usng the global trust group all nodes n the calculaton of super nodes. Fgure 9. The changes of successful transacton rate under CF wth cycle.5. Rato of malcous nodes affectng the success rate for the download In the fle download process, malcous nodes percentage success rate for the download has some nfluence, as shown n Fgure 9, because Super Trust for dentfyng malcous nodes reasonable and effectve, and to dentfy a hgh success rate, so n ths model, the rato of malcous nodes success rate for download to reduce the nfluence of some. Super Trust and PD Trust, RB Trust compared to download the success rate has greatly mproved, and wth the larger rato of malcous nodes, download success rate declned. Expermental results show that, Super Trust has a stronger ablty to resst rsks n response to changes n the proporton of malcous nodes. References [1] Raul Medna-Mora, Kelly W. Cartron. ActonWorkflow n Use: Clark County Department of Busness Lcense. In Proceedngs of the Twelfth Internatonal Conference on Data Engneerng, IEEE Computer Socety Washngton, DC, USA, 1996: [] Jan Yun, Xaotong L, Chunxa Wang. Smulaton of Conductng Early-warnng to the Endangered State of Language. Journal of Multmeda, Vol 8, No 5 (013), [3] Edward A,Stohr J, Loan Zhao. Workflow Automaton: Overvew and Research Issuse, Informaton Systems Fronters, 010, 3(3): [4] Y Yang. An Archtecture and the Related Mechansms for Web-based Global Cooperatve Teamwork Support. Internatonal Journal of Computer Informaton, 000, 4(1): [5] Fe Lu, Guangzhou Zeng. A Multagent Cooperatve Learnng Algorthm. Lecture Notes n Computer Scence, 007,440: [6] Benatalhth B,Sheng Q Z,Dumas M. The Self-serv Envronment for Web Servces Composton. IEEE Internet Computng, 013, 7(1); Fgure 10. Downloads affect the success rate of download 5