Effective Network Defense Strategies against Malicious Attacks with Various Defense Mechanisms under Quality of Service Constraints
|
|
- Alannah Holmes
- 8 years ago
- Views:
Transcription
1 Effectve Network Defense Strateges aganst Malcous Attacks wth Varous Defense Mechansms under Qualty of Servce Constrants Frank Yeong-Sung Ln Department of Informaton Natonal Tawan Unversty Tape, Tawan, R.O.C. Yu-Shun Wang Department of Informaton Natonal Tawan Unversty Tape, Tawan, R.O.C. Yu-Pu Wu Department of Informaton Natonal Tawan Unversty Tape, Tawan, R.O.C. Cha-Yang Hsu Department of Informaton Natonal Tawan Unversty Tape, Tawan, R.O.C. Abstract How to apply tmely and effectve defense strateges aganst attackers whle maxmzng system survvablty s a crtcal ssue for a defender. Ths paper mathematcally models attack and defense scenaros, usng varous defensve mechansms durng both the plannng and defendng stages and under qualty of servce constrants. Ths model ncorporates hgh degrees of randomness, as attackers are assumed to have ncomplete nformaton. Gven such nondetermnstc problems, ths paper dentfes the approprate tme for applyng defense n depth or resource concentraton strategy. Keywords: Network Survvablty; Defense Strateges; Mathematcal Programmng; Incomplete Informaton; I. INTRODUCTION The losses caused by cyber-attacks are a crtcal ssue for busness enterprses. In State of Enterprse Securty [1], the authors note that the top costs of cyber-attacks nclude lost productvty, lost revenue and the loss of customer trust. Smlarly, the 2011 Global State of Informaton Securty Survey [2] also lsted fnancal losses, theft of ntellectual property, and a compromsed brand or reputaton as the top three consequences of cyber-attacks. The frst step n dscussng cyber-attack-and-defense s to determne how to measure the defensve status of a gven system. There are many deal metrcs wthn the exstng lterature that are wdely used to descrbe system status, for example, survvablty, avalablty, relablty and dependablty. In ths work, we focus specfcally on survvablty for measurng network systems. We adopt the defnton from [3] and defne survvablty as the capablty of a system to fulfll ts msson, n a tmely manner, n the presence of attacks, falures, or accdents, where system s defned n the broadest possble sense, ncludng networks and large-scale systems of systems. In ths way, our adopted defnton not only follows a system perspectve, but also a servce perfectve whch focuses on mantanng the qualty of servce. Mantanng a focus on servce s mportant because whle defendng aganst malcous attacks, the defender must mantan provded servces smoothly. In other words, the defender must not only protect the system, but also smultaneously serve legtmate users at a certan level of QoS (Qualty of Servce). Therefore, survvablty s an deal metrc for judgng defensve capabltes. In our prevous network attack and defense studes, models are often confgured so that attackers can compromse target nodes only f ther spent resources are greater than the defensve resources deployed on nodes. However, such models are too smple to reflect real world scenaros snce t s determnstc. One soluton to ths problem s to adopt the contest success functon. In [4], the contest success functon s appled from economcs to determne each player s probablty of wnnng as a functon of all players efforts. The form of the contest success m functon s T where T and t denote resources that each m m T t player has nvested respectvely, and m denotes contest ntensty. In [5] and [6], the authors adopt the contest success functon for network attack and defense scenaros, where T refers to resources that the attacker spends on the target node and t stands for the defensve resources deployed on the same node. In ths work, the contest success functon s also utlzed for determnng the probablty of an attacker successfully compromsng one node. Defenders often act reactvely n network attack and defense scenaros. However, as technology progresses, defense solutons are no longer bounded by general defense resources such as frewalls or IPS (Intruson Preventon System). Now, there are also mechansms lke dynamc topology reconfguraton [7] [8], cloud computng securty servces (.e. Securty as a Servce, SaaS) [9] [10], and attack sgnatures that help grant mmunty to certan types of attacks. Moreover, as cloud computng ncreases n popularty, vrtualzaton technques have garnered more attenton. Many defense solutons are developed based on these technologes, such as the Vrtual Machne Montor Intruson Preventon System (VMM-IPS), whch s an ntruson preventon system embedded n the VMM that controls all correspondng vrtual machnes [11] [12]. However, tme and mproved technques have not only resulted n mproved defenses, but also mproved attacks. Attack tools and equpment have contnually evolved not only n quantty but also qualty. Before launchng an attack, attackers spend a certan porton of ther budget to acqure attack tools as preparaton. Ths ncludes buyng ready-made tools, reconstructng tools based on ready-made examples, and self-development projects. Intutvely, buyng readymade tools costs less snce attackers can spend less tme confgurng them. However, they are also easly blocked by securty tools such as ntruson preventon systems, snce the
2 sgnatures of the ready-made tools may already be well known. Furthermore, ready-made tools tend to be lower n qualty than self-developed alternatves. Hgher-qualty attack tools often more effectvely utlze the full budget of an attacker. Obtanng attack tools allows an attacker to launch an attack, however n most cases; attackers do not have complete nformaton regardng the target network, lke the exact locaton of core nodes and defense confguraton. They can only collect nformaton durng an attack. The ncomplete nformaton assumpton makes the attack-defense scenaro more realstc but rases the dffculty n solvng the problem. As a result, much prevous research must assume that attackers have perfect knowledge regardng target networks [5] [6] [13]. Although the defender can apply dverse defense mechansms, some of these solutons have negatve effects on the qualty of servce. For nstance, dynamc topology reconfguraton may ncrease the number of hops (.e., ntermedate nodes) that legtmate users experence. Therefore, defenders must delberately apply defense strateges to mnmze the attackers success probablty. For defense resource allocaton, there are two wellknown strateges defenders can apply: resource concentraton and defense n depth strategy. However, these strateges are not unversal. It s extremely mportant to determne under what condtons resource concentraton or defense n depth strategy perform better n terms of survvablty. Gven these problems, ths paper provdes several contrbutons to the exstng lterature. It frst examnes the robustness of grd, random, and scale free networks. It then dentfes the proper tme and condtons for resource concentraton and defense n depth strateges, as well as consders varous attack/defense mechansms and the qualty of servce constrants wthn attack and defense scenaros. Lastly, t provdes a more realstc attack scenaro by assumng that attackers have ncomplete nformaton on the target network. II. RELATED WORK As mentoned above, the determnaton of whether an attacker compromses a node s based on the contest success functon, orgnatng from economcs. The major characterstc of ths functon s contest ntensty. As an exponent, the value sgnfcantly nfluences the result. Accordng to prevous research [14] [15], the dfferent values of contest ntensty reflect dstnct real world battle scenaros. When contest ntensty les from 0m 1, t represents fght to wn or de crcumstances. Wth respect to 1 m, t stands for the effectveness of resources each player nvested s exponentally ncreasng snce contest ntensty s the exponent of contest success functon. When m, t depcts wnner takes all crcumstance. In most attack and defense scenaro studes, researchers ether consder few defense mechansms n a smple system or assume the attacker has complete nformaton on the target network [5] [6] [13]. For example, n [5], the authors propose an optmal dstrbuton of defense resources n a seres system, rather than a network topology. Regardng [6], the authors assume that the defender only has one sngle object that can be destroyed by the attacker. Such an assumpton s not sutable for the servce-provdng scenaro consdered n ths work, snce t results n poor qualty of servce. Lastly, wth [13], the authors assume the attacker has complete nformaton on the target network and that the attacker collects every detal of the defense confguraton before launchng ther assault. The defense mechansms consdered n ths work are all referenced from ether academc or practcal domans. For nstance, n [4] and [5], the authors apply the concept of rotatng servers to mprove system survvablty. Extendng ths deal, wth the help of the Securty Operaton Center (SOC), the defender s capable of dynamcally regulatng the network s topology, such as the connectons between nodes. Once there s a detected compromsed node, the defender can flter out the traffc sourced from that node. Here, ths knd of defense strategy s denoted as Dynamc Topology Reconfguraton. Although ths technque s effectve n stallng attackers, t may severely jeopardze the qualty of servce. Therefore, a defender should carefully consder ther optons before applyng ths defense mechansm. Other optons nvolve vrtualzaton technques, whch allow underlyng physcal resources to be shared between dfferent Vrtual Machnes (VMs). The frmware that provdes ths vrtualzaton s called a Vrtual Machne Montor (VMM). Snce all access to hardware resources must go through the VMM, t becomes an deal place to mplement the Intruson Preventon System (IPS) [11] [12] [16]. Therefore, the term VMM-IPS denotes an ntruson preventon system constructed wthn a VMM, protectng all VMs governed by the same VMM. For nstance, a VMM can flter out malcous traffc to protect the system. However, ths knd of mechansm, called local defense, may also result n false postves that flter out legtmate users. Therefore, t s assumed that there s a certan probablty that ths local defense servce has a negatve effect on qualty of servce. Furthermore, whle under attack usng a VMM, the defender s able to request from a thrd party securty servce provder the sgnature of the attack. Once the sgnature s updated, all VMs and VMMs are mmune to ths partcular attack. Nevertheless, because the VMM has total control of ts VMs, compromsng the VMM s the same as compromsng all the VMs governed by t. In addton to the sgnature, the concept of servce orented perspectve s ncreasngly popular wthn the securty doman. Provders are gradually preferrng to perform securty servces remotely rather than sellng local products. For example, n [17], the provder performs dfferent levels of traffc nspecton and flterng servces from a cloud envronment. Nonetheless, smlar to local defense servces, there are stll chances that false postves wll occur. Thus t s assumed that there s a certan probablty that ths strategy wll stll jeopardze QoS.
3 III. PROBLEM FORMULATION A. Problem Descrpton In order to mprove network survvablty, the defender allocates fnte resources on nodes durng the plannng phase, ncludng nstallng a vrtualzaton envronment, settng up cloud securty servce software and establshng the VMM- IPS. Whle under attack, the defender s capable of mmedately applyng some defense strateges, such as requestng an attack sgnature, under qualty of servce constrants. The defender may be an enterprse or a government admnstrator, and there are several core nodes provdng servces wth dfferent prortes. The detaled assumptons are lsted n table 1. TABLE I: PROBLEM ASSUMPTIONS 1. There are multple core nodes and servces n the network. 2. Each core node can provde only one specfc servce. 3. Each servce has dfferent weght determned by the defender. 4. There s a Securty Operaton Center (SOC) governng the network. 5. The defender has perfect knowledge of network and can allocate resources or adopt defense solutons by the SOC. 6. Attackers only have ncomplete nformaton about the network. 7. Whether a node s compromsed or not s determned by the revsed contest success functon. 8. Only malcous nodal attacks are consdered. For attackers, each carres a dstnct budget, capablty and aggressveness that match a general dstrbuton. Whle selectng the next canddate to compromse, attackers depend on the stuaton at the moment to adopt correspondng crtera. Accordng to [18], the authors propose several attack strateges, most of whch are mplemented n the attackers selectng crtera whch s used to choose next vctm to compromse. B. Mathematcal Formulaton Based on the problem descrpton, a correspondng mathematcal formulaton s proposed. The gven parameters are lsted n table 2, and the decson varables are presented n table 3. Snce the prevously dscussed scenaro s nondetermnstc and nvolves sgnfcant amounts of randomness, t s qute dffcult to formulate purely usng mathematcs. Consequently, the proposed model ncludes verbal notatons, whch are lsted n table 4. TABLE II: GIVEN PARAMETERS Gven Parameters Notaton Descrpton N The ndex set of all nodes C The ndex set of all core nodes L The ndex set of all lnks M The ndex set of all level of vrtual machne montors (VMMs) H The ndex set of all level of cloud securty servces S The ndex set of all knds of servces Q The ndex set of all canddate nodes equpped wth cloud securty agent B The defender s total budget E All possble defense confguratons, ncludng defense resources allocatons and defendng strateges All possble attacker categores, ncludng attacker attrbutes, Z correspondng strateges and transton rules An attack confguraton, ncludng the attrbutes, A j correspondng strateges and transton rules of the attacker launches j th attack on th servce, where S, 1 j F The total attackng tmes on th servce for all attackers, where S w The cost of constructng one ntermedate node o The cost of constructng one core node p The cost of constructng each vrtual machne (VM) k The maxmum number of vrtual machnes on VMM level, where M The weght of th servce, where S c The cost of settng a cloud securty agent to one node The rato of defense enhanced on VMs and VMM when local d r defense s actvated The rato of defense enhanced by applyng level cloud securty servces, where H TABLE III: DECISION VARIABLES Decson Varables Notaton Descrpton A defense confguraton, ncludng defense resource D allocaton and defendng strateges on th servce, where S 1 f the attacker can acheve hs goal successfully, and 0 Tj ( D, Aj ) otherwse, where S, 1 j n The general defense resource allocated to node, where N e The total number of ntermedate nodes The capacty of drect lnk between node and j, where N, q j j N l The number of VMs and level VMM purchased, where M vl ( ) The cost of constructng a level VMM wth l VMs, where M 1 f node s equpped wth the cloud securty agent, 0 x otherwse, where N B NL The budget of constructng nodes and lnks B general The budget of general defense resource B specal The budget of specal defense resources B vrtualzaton The budget of vrtualzaton The budget of equppng cloud agents B cloud agent TABLE IV: VERBAL NOTATIONS Verbal Notatons Notaton Descrpton G core Resdual loadng of each core node, where C Ulnk K effect I effect J effect P effect O tocore Y W threshold W fnal W() defense hops Lnk utlzaton, where L Negatve effect caused by applyng flawed sgnature Negatve effect caused by applyng dynamc topology reconfguraton Negatve effect caused by applyng flawed local defense Negatve effect caused by applyng cloud securty servce The number of hops that legtmate users experenced from one of the edge nodes to core nodes The total compromse events The predefned QoS threshold The fnal QoS level at the end of an attack The total defense resource of the shortest path from detected compromsed nodes to one core node dvded by total defense resource The mnmum number of hops from detected compromsed nodes to one core node dvded by the maxmum number of hops from attacker s startng pont to one core node The lnk degree of one core node dvded by the maxmum degree lnk degree among all nodes n the topology s prorty threshold The prorty of servce dvded by the hghest prorty of servce n the network, where S The rsk threshold of core nodes
4 () The rsk status of each core node whch s the aggregaton of defense resource, number of hops, lnk degree and servce prorty The objectve functon (IP 1) stands for the defender s objectve, whch s to mnmze the weghted servce compromse probablty by effectvely adjustng the defense confguraton. Evdently, any defense confguraton that the defender apples should come out of all possble defense confguratons; the correspondng constrant s (IP 1.1). Alternatvely, (IP 1.2) represents a smlar deal for the attackers sde. (IP 1.3) means that the lnk capacty must be a postve quantty. (IP 1.4) ~ (IP 1.9) jontly descrbe that the cost of constructng nodes, lnks, vrtual machnes, cloud securty agents and deployng general defense resources durng the plannng phase should not volate budget lmtatons. (IP 1.10) ~ (IP 1.14) are ntegral and numercal constrants. Objectve Functon: F Constrants: D E mn D S j j j1 S T ( D, A ) F (IP 1) S (IP 1.1) Aj Z S,1 j Fk (IP 1.2) ks qj 0, j N (IP 1.3) BNL Bgeneral Bspecal B (IP 1.4) Bvrtualzaton Bcloudagent Bspecal (IP 1.5) gq ( j ) weo C N jn 2 BNL (IP 1.6) n Bgeneral (IP 1.7) N vl ( ) p l k B vrtualzaton (IP 1.8) M M x c Bcloudagent (IP 1.9) N gq ( j ) 0, j N (IP 1.10) n 0 N (IP 1.11) vl ( ) 0 M (IP 1.12) e 0 (IP 1.13) x 0 or 1 N (IP 1.14) Verbal constrants : Y y WGcore U K I J Peffect Otocore dy (IP 1.15) lnk effect effect effect j [ (,,,,,, )] 1 Y Wthreshold, where C, jl Wfnal Wthreshold (IP 1.16) The total cost of applyng defendng phase solutons must not volate budget lmtaton (IP 1.17) ( defense, hops, degree, s ) threshold, where S. (IP 1.18) prorty Beyond those constrants that are well-modeled mathematcally, there are stll some constrants that must be descrbed verbally. (IP 1.17) refers to the budget constrant of the defense phase. Whle adoptng any defense soluton, the defender must consder related budget lmtatons. (IP 1.18) descrbes how all defendng phase solutons are actvated only f the rsk level s hgher than a predefned threshold. IV. COMPUTATIONAL EXPERIMENTS A. Smulaton Envronment All smulatons are programmed n the C language. The system parameters are lsted n table 5. The evaluaton tmes for each attack and defense scenaro are determned by smulatons, whch are presented n the next secton. For defender-related parameters, grd, random and scale free topologes are appled to network types. The constructng algorthms of random and scale free networks are cted from [19] and [20]. The remanng parameters are presented n table 6. For attacker-related parameters, three mportant attrbutes are consdered, ncludng total budget, capablty, and aggressveness. All of these attrbutes shown n table 7 are determned by a general dstrbuton. In the followng smulatons, a normal dstrbuton s appled for decdng the value of each attrbute. TABLE V. SYSTEM PARAMETERS Parameter Value Compler GNU GCC Evaluaton Tmes for each 70,000 Attack and Defense Scenaro TABLE VI: DEFENDER PARAMETERS Parameter Topology Type Value Grd, Random, Scale-Free Topology Scale Small Medum Number of Nodes 9 25 Number of Servce(s) 1 2 Number of Total Core Node(s) 1 3 Total Budget for Network Constructon and defense 500,000 1,000,000 TABLE VII: ATTACKER PARAMETERS Parameter Value Total Budget Normal dstrbuton wth boundary (300,000 ~ 1,500,000) Capablty Normal dstrbuton wth boundary (0 ~ 1) Aggressveness Normal dstrbuton wth boundary (0 ~ 1) B. Smulaton Results 1) Convergence In ths work, the convergence of data s consdered as the numercal stablty. Whle the magntude of data vbratons s wthn the acceptable nterval, for example, 0.2%, the correspondng number of smulaton tmes s set to be the evaluaton tmes for each attack and defense scenaro. For each smulaton, the horzontal axs represents the evaluaton tme, and the vertcal axs stands for the network system compromse probablty, whch s the objectve functon of the proposed mathematcal model. Fgure 1 demonstrates that when the attack and defense scenaro takes place on a 9 node grd network, the contest ntensty equals 2. The fluctuaton of the network compromse probablty s less than 0.2% when evaluaton tmes exceed 69,000. Based on ths result, the evaluaton tme for each attack and defense scenaro s determned to be 70,000.
5 Fgure 1. Convergence experment on a 9 nodes grd network 2) Influence of Contest Intensty and Aggressveness As mentoned n the problem descrpton, the contest ntensty greatly nfluences the nature of an attack and defense scenaro. However, there s no obvous trend for system compromse probablty through dfferent values of contest ntensty [5] [6]. The result of these smulatons s consstent wth prevous research [5], [6]. In fgure 2, a 9 nodes scale free network s taken for example. As the value of contest ntensty ncreases, the system compromse probablty does not show an ncreasng or decreasng trend. Instead, the compromse probablty s low when contest ntensty equals 0.5 and 1.5. Whle the ntensty s 1 and 2, the probablty s hgh. Fgure 3. Influence of contest ntensty and aggressveness on a 25 nodes grd network The same results can be observed n scale free and random networks. Correspondng data s shown n fgure 4 and 5. Ths result s because once the attacker determnes hs/her aggressveness to a certan node, the correspondng cost can be calculated by the contest success functon. Wth dfferent values of contest ntensty, the cost that one attacker must spend for compromsng each node s dstnct. In other words, when the value of contest ntensty ncreases, the cost of compromsng one node for a certan attacker exponentally decreases. Fgure 4. Influence of contest ntensty and aggressveness on a 25 nodes scale-free network Fgure 2. Influence of contest ntensty on a 9 nodes scale-free network However, f the nfluences of contest ntensty and attacker aggressveness are jontly consdered, there are some nterestng results that must be explaned. As shown n fgure 3, the attack and defense scenaro s constructed on a 25 nodes grd network. If attacker aggressveness s determned by a normal dstrbuton wth lower boundary 0.1 and upper boundary 0.9, there s no trend on compromse probablty. Nevertheless, f the normal dstrbuton of attacker aggressveness s bounded by 0.1 ~ 0.5 or 0.5 ~ 0.9, there are obvous trends. For the lower nterval of attacker aggressveness, the system compromse probablty shows a decreasng trend through the value of contest ntensty from 0.5 to 2. Wth regard to the hgher nterval of attacker aggressveness, the compromse probablty dsplays an ncreasng tendency. Fgure 5. Influence of contest ntensty and aggressveness on a 25 nodes random network Therefore, when the value of contest ntensty s small, attackers wth a hgh value of aggressveness must spend a large porton of ther budget to compromse every target. More specfcally, attackers wth hgh aggressveness wll
6 exhaust ther budget at an early stage. They consume more resources to compromse fewer nodes wth hgh success probablty. Consequently, the system compromse probablty s low. In contrast, when the value of contest ntensty s large, the cost of compromsng each node for attackers wth a hgh degree of aggressveness s far lower than the scenaro wth a small value of contest ntensty. Hence, even for attackers wth hgh degrees of aggressveness, the cost of compromsng the whole system s affordable. For attackers wth low levels of aggressveness, the system compromse probablty s hgher when the degree of contest ntensty s small. Although these attackers may suffer from many attack falures and have to compromse agan, the total attack cost s stll lower than attackers wth a hgh value of aggressveness. V. DISCUSSION OF RESULTS Defense n depth strategy s advantageous for defenders facng less aggressve attackers wth fght to wn or de crcumstances Less aggressve attackers tend to spend small amounts of resources on compromsng ther target nodes. They prefer opportunsm; falure n compromsng ntermedated nodes s acceptable. Spendng large amounts of resources for compromsng targets s not a proper strategy for such attackers. Whle under fght to wn or de crcumstances (.e., contest ntensty s small [14]), the effectveness of defensve resources exponentally decreases. Thus, a resource concentraton strategy results n poor survvablty. Therefore, for the defender facng less aggressve attackers wth fght to wn or de crcumstances, a defense n depth strategy mantans a better degree of survvablty than resource concentraton strateges. Resource concentraton strategy s advantageous for defense aganst aggressve attackers wth wnner takes all crcumstances Aggressve attackers tend to spend large amounts of resources to compromse ther target nodes wth hgh success probablty. They prefer pragmatsm. A one-shot compromse of the targeted node s an deal strategy. Spendng fewer resources for each attack and acceptng rsk or falure s not acceptable for ths attack type. Wth wnner takes all crcumstances (.e., contest ntensty s large [15]), the effectveness of defense resource s sgnfcant. The performance of resource concentraton strategy s better than defense n depth strategy. Hence, for the defender facng aggressve attackers wth wnner takes all crcumstances, concentratng fnte defense resources on a few mportant nodes s a better strategy for achevng hgher survvablty. VI. CONCLUSION AND FUTURE WORK In summary, the degree of randomness nvolved n the problem dscussed above creates a non-determnstc stuaton, for whch varous defense mechansms are consdered. Ths paper successfully models the problem as a mathematcal formulaton. Further, through the smulaton results, effectve defense strateges are provded to the defender. For future work, other types of defense mechansms and attrbutes may be consdered to ncrease the robustness of the modeled scenaro. ACKNOWLEDGMENT Ths work was supported by the Natonal Scence Councl, Tawan, Republc of Chna (grant nos. NSC E ). REFERENCES [1] "State of Enterprse Securty," Symantec Corporaton, Techncal report, [2] "Global State of Informaton Securty Survey," PwC, Techncal report, [3] R. J. Ellson, et al., "Survvable Network Systems: An Emergng Dscplne," Techncal Report CMU/SEI-97-TR-013, 1997 (Revsed: May 1999). [4] S. Skaperdas, "Contest success functons," Economc Theory, vol. 7, pp , [5] K. Hausken and G. Levtn, "Protecton vs. false targets n seres systems," Relablty Engneerng & System Safety, vol. 94, pp , [6] G. Levtn and K. Hausken, "False targets effcency n defense strategy," European Journal of Operatonal Research, vol. 194, pp , [7] Y. Huang, et al., "Incorruptble system self-cleansng for ntruson tolerance," 25th IEEE Internatonal Conference on Performance, Computng, and Communcatons (IPCCC), pp , [8] Y. Huang, et al., "Closng Cluster Attack Wndows Through Server Redundancy and Rotatons," Sxth IEEE Internatonal Symposum on Cluster Computng and the Grd, vol. 2, pp.21, [9] H. E. Schaffer, "X as a Servce, Cloud Computng, and the Need for Good Judgment," IT Professonal, vol. 11, pp. 4-5, [10] Johns, Software as a Servce - SaaS:Emergng trends n IT, ( [11] VMware, VMware vsheldtm Product Famly, [12] Trend Mcro, Trend Mcro Deep Securty, [13] C. Ryu, R. Sharman, H.R. Rao, S. Upadhyaya, Securty protecton desgn for decepton and real system regmes: A model and analyss, European Journal of Operatonal Research, Vol. 201, Issue 2, pp , [14] Jack Hrshlefer "Conflct and rent-seekng success functons - Rato vs dfference models of relatve success," Proc. Publc Choce, pp , [15] Jack Hrshlefer "The Paradox of Power," Proc. Economcs and Poltcs, Vol. 3, pp , [16] P. M. Chen and B. D. Noble, "When Vrtual Is Better Than Real," Eghth Workshop on Hot Topcs n Operatng Systems, [17] Zscaler Products. [18] F. Cohen, "Managng network securty: Attack and defence strateges," Network Securty, vol. 1999, pp. 7-11, [19] J. Bltzsten and P. Dacons, "A Sequental Importance Samplng Algorthm for Generatng Random Graphs wth Prescrbed Degrees," Internet Mathematcs, vol. 6, pp , [20] S. Nagaraja and R. Anderson, "Dynamc Topologes for Robust Scale-Free Networks," Bo-Inspred Computng and Communcaton, vol. 5151, pp , 2008.
IMPACT ANALYSIS OF A CELLULAR PHONE
4 th ASA & μeta Internatonal Conference IMPACT AALYSIS OF A CELLULAR PHOE We Lu, 2 Hongy L Bejng FEAonlne Engneerng Co.,Ltd. Bejng, Chna ABSTRACT Drop test smulaton plays an mportant role n nvestgatng
More informationSurvey on Virtual Machine Placement Techniques in Cloud Computing Environment
Survey on Vrtual Machne Placement Technques n Cloud Computng Envronment Rajeev Kumar Gupta and R. K. Paterya Department of Computer Scence & Engneerng, MANIT, Bhopal, Inda ABSTRACT In tradtonal data center
More informationANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING
ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING Matthew J. Lberatore, Department of Management and Operatons, Vllanova Unversty, Vllanova, PA 19085, 610-519-4390,
More informationThe OC Curve of Attribute Acceptance Plans
The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4
More informationStudy on Model of Risks Assessment of Standard Operation in Rural Power Network
Study on Model of Rsks Assessment of Standard Operaton n Rural Power Network Qngj L 1, Tao Yang 2 1 Qngj L, College of Informaton and Electrcal Engneerng, Shenyang Agrculture Unversty, Shenyang 110866,
More informationPAS: A Packet Accounting System to Limit the Effects of DoS & DDoS. Debish Fesehaye & Klara Naherstedt University of Illinois-Urbana Champaign
PAS: A Packet Accountng System to Lmt the Effects of DoS & DDoS Debsh Fesehaye & Klara Naherstedt Unversty of Illnos-Urbana Champagn DoS and DDoS DDoS attacks are ncreasng threats to our dgtal world. Exstng
More informationbenefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).
REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or
More informationData Broadcast on a Multi-System Heterogeneous Overlayed Wireless Network *
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 24, 819-840 (2008) Data Broadcast on a Mult-System Heterogeneous Overlayed Wreless Network * Department of Computer Scence Natonal Chao Tung Unversty Hsnchu,
More informationM3S MULTIMEDIA MOBILITY MANAGEMENT AND LOAD BALANCING IN WIRELESS BROADCAST NETWORKS
M3S MULTIMEDIA MOBILITY MANAGEMENT AND LOAD BALANCING IN WIRELESS BROADCAST NETWORKS Bogdan Cubotaru, Gabrel-Mro Muntean Performance Engneerng Laboratory, RINCE School of Electronc Engneerng Dubln Cty
More informationFeature selection for intrusion detection. Slobodan Petrović NISlab, Gjøvik University College
Feature selecton for ntruson detecton Slobodan Petrovć NISlab, Gjøvk Unversty College Contents The feature selecton problem Intruson detecton Traffc features relevant for IDS The CFS measure The mrmr measure
More informationA Secure Password-Authenticated Key Agreement Using Smart Cards
A Secure Password-Authentcated Key Agreement Usng Smart Cards Ka Chan 1, Wen-Chung Kuo 2 and Jn-Chou Cheng 3 1 Department of Computer and Informaton Scence, R.O.C. Mltary Academy, Kaohsung 83059, Tawan,
More informationAn Interest-Oriented Network Evolution Mechanism for Online Communities
An Interest-Orented Network Evoluton Mechansm for Onlne Communtes Cahong Sun and Xaopng Yang School of Informaton, Renmn Unversty of Chna, Bejng 100872, P.R. Chna {chsun,yang}@ruc.edu.cn Abstract. Onlne
More informationFault tolerance in cloud technologies presented as a service
Internatonal Scentfc Conference Computer Scence 2015 Pavel Dzhunev, PhD student Fault tolerance n cloud technologes presented as a servce INTRODUCTION Improvements n technques for vrtualzaton and performance
More informationA Replication-Based and Fault Tolerant Allocation Algorithm for Cloud Computing
A Replcaton-Based and Fault Tolerant Allocaton Algorthm for Cloud Computng Tork Altameem Dept of Computer Scence, RCC, Kng Saud Unversty, PO Box: 28095 11437 Ryadh-Saud Araba Abstract The very large nfrastructure
More informationThe Development of Web Log Mining Based on Improve-K-Means Clustering Analysis
The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.
More informationPower-of-Two Policies for Single- Warehouse Multi-Retailer Inventory Systems with Order Frequency Discounts
Power-of-wo Polces for Sngle- Warehouse Mult-Retaler Inventory Systems wth Order Frequency Dscounts José A. Ventura Pennsylvana State Unversty (USA) Yale. Herer echnon Israel Insttute of echnology (Israel)
More informationProject Networks With Mixed-Time Constraints
Project Networs Wth Mxed-Tme Constrants L Caccetta and B Wattananon Western Australan Centre of Excellence n Industral Optmsaton (WACEIO) Curtn Unversty of Technology GPO Box U1987 Perth Western Australa
More informationResearch of Network System Reconfigurable Model Based on the Finite State Automation
JOURNAL OF NETWORKS, VOL., NO. 5, MAY 24 237 Research of Network System Reconfgurable Model Based on the Fnte State Automaton Shenghan Zhou and Wenbng Chang School of Relablty and System Engneerng, Behang
More informationNetwork Security Situation Evaluation Method for Distributed Denial of Service
Network Securty Stuaton Evaluaton Method for Dstrbuted Denal of Servce Jn Q,2, Cu YMn,2, Huang MnHuan,2, Kuang XaoHu,2, TangHong,2 ) Scence and Technology on Informaton System Securty Laboratory, Bejng,
More informationBUSINESS PROCESS PERFORMANCE MANAGEMENT USING BAYESIAN BELIEF NETWORK. 0688, dskim@ssu.ac.kr
Proceedngs of the 41st Internatonal Conference on Computers & Industral Engneerng BUSINESS PROCESS PERFORMANCE MANAGEMENT USING BAYESIAN BELIEF NETWORK Yeong-bn Mn 1, Yongwoo Shn 2, Km Jeehong 1, Dongsoo
More informationMethodology to Determine Relationships between Performance Factors in Hadoop Cloud Computing Applications
Methodology to Determne Relatonshps between Performance Factors n Hadoop Cloud Computng Applcatons Lus Eduardo Bautsta Vllalpando 1,2, Alan Aprl 1 and Alan Abran 1 1 Department of Software Engneerng and
More informationHow To Understand The Results Of The German Meris Cloud And Water Vapour Product
Ttel: Project: Doc. No.: MERIS level 3 cloud and water vapour products MAPP MAPP-ATBD-ClWVL3 Issue: 1 Revson: 0 Date: 9.12.1998 Functon Name Organsaton Sgnature Date Author: Bennartz FUB Preusker FUB Schüller
More informationModule 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..
More informationCloud Auto-Scaling with Deadline and Budget Constraints
Prelmnary verson. Fnal verson appears In Proceedngs of 11th ACM/IEEE Internatonal Conference on Grd Computng (Grd 21). Oct 25-28, 21. Brussels, Belgum. Cloud Auto-Scalng wth Deadlne and Budget Constrants
More informationA Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression
Novel Methodology of Workng Captal Management for Large Publc Constructons by Usng Fuzzy S-curve Regresson Cheng-Wu Chen, Morrs H. L. Wang and Tng-Ya Hseh Department of Cvl Engneerng, Natonal Central Unversty,
More informationEfficient Project Portfolio as a tool for Enterprise Risk Management
Effcent Proect Portfolo as a tool for Enterprse Rsk Management Valentn O. Nkonov Ural State Techncal Unversty Growth Traectory Consultng Company January 5, 27 Effcent Proect Portfolo as a tool for Enterprse
More informationAn Alternative Way to Measure Private Equity Performance
An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate
More informationOn the Optimal Control of a Cascade of Hydro-Electric Power Stations
On the Optmal Control of a Cascade of Hydro-Electrc Power Statons M.C.M. Guedes a, A.F. Rbero a, G.V. Smrnov b and S. Vlela c a Department of Mathematcs, School of Scences, Unversty of Porto, Portugal;
More informationRecurrence. 1 Definitions and main statements
Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.
More informationJ. Parallel Distrib. Comput.
J. Parallel Dstrb. Comput. 71 (2011) 62 76 Contents lsts avalable at ScenceDrect J. Parallel Dstrb. Comput. journal homepage: www.elsever.com/locate/jpdc Optmzng server placement n dstrbuted systems n
More informationAn Analysis of Central Processor Scheduling in Multiprogrammed Computer Systems
STAN-CS-73-355 I SU-SE-73-013 An Analyss of Central Processor Schedulng n Multprogrammed Computer Systems (Dgest Edton) by Thomas G. Prce October 1972 Techncal Report No. 57 Reproducton n whole or n part
More informationMETHODOLOGY TO DETERMINE RELATIONSHIPS BETWEEN PERFORMANCE FACTORS IN HADOOP CLOUD COMPUTING APPLICATIONS
METHODOLOGY TO DETERMINE RELATIONSHIPS BETWEEN PERFORMANCE FACTORS IN HADOOP CLOUD COMPUTING APPLICATIONS Lus Eduardo Bautsta Vllalpando 1,2, Alan Aprl 1 and Alan Abran 1 1 Department of Software Engneerng
More informationA Novel Auction Mechanism for Selling Time-Sensitive E-Services
A ovel Aucton Mechansm for Sellng Tme-Senstve E-Servces Juong-Sk Lee and Boleslaw K. Szymansk Optmaret Inc. and Department of Computer Scence Rensselaer Polytechnc Insttute 110 8 th Street, Troy, Y 12180,
More informationAPPLICATION OF PROBE DATA COLLECTED VIA INFRARED BEACONS TO TRAFFIC MANEGEMENT
APPLICATION OF PROBE DATA COLLECTED VIA INFRARED BEACONS TO TRAFFIC MANEGEMENT Toshhko Oda (1), Kochro Iwaoka (2) (1), (2) Infrastructure Systems Busness Unt, Panasonc System Networks Co., Ltd. Saedo-cho
More informationA hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm
Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(7):1884-1889 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 A hybrd global optmzaton algorthm based on parallel
More informationForecasting the Direction and Strength of Stock Market Movement
Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye cjngwe@stanford.edu mchen5@stanford.edu nanye@stanford.edu Abstract - Stock market s one of the most complcated systems
More informationApplication of Multi-Agents for Fault Detection and Reconfiguration of Power Distribution Systems
1 Applcaton of Mult-Agents for Fault Detecton and Reconfguraton of Power Dstrbuton Systems K. Nareshkumar, Member, IEEE, M. A. Choudhry, Senor Member, IEEE, J. La, A. Felach, Senor Member, IEEE Abstract--The
More informationMultiple-Period Attribution: Residuals and Compounding
Multple-Perod Attrbuton: Resduals and Compoundng Our revewer gave these authors full marks for dealng wth an ssue that performance measurers and vendors often regard as propretary nformaton. In 1994, Dens
More informationOptimization Model of Reliable Data Storage in Cloud Environment Using Genetic Algorithm
Internatonal Journal of Grd Dstrbuton Computng, pp.175-190 http://dx.do.org/10.14257/gdc.2014.7.6.14 Optmzaton odel of Relable Data Storage n Cloud Envronment Usng Genetc Algorthm Feng Lu 1,2,3, Hatao
More informationOpen Access A Load Balancing Strategy with Bandwidth Constraint in Cloud Computing. Jing Deng 1,*, Ping Guo 2, Qi Li 3, Haizhu Chen 1
Send Orders for Reprnts to reprnts@benthamscence.ae The Open Cybernetcs & Systemcs Journal, 2014, 8, 115-121 115 Open Access A Load Balancng Strategy wth Bandwdth Constrant n Cloud Computng Jng Deng 1,*,
More informationA Design Method of High-availability and Low-optical-loss Optical Aggregation Network Architecture
A Desgn Method of Hgh-avalablty and Low-optcal-loss Optcal Aggregaton Network Archtecture Takehro Sato, Kuntaka Ashzawa, Kazumasa Tokuhash, Dasuke Ish, Satoru Okamoto and Naoak Yamanaka Dept. of Informaton
More informationAvailability-Based Path Selection and Network Vulnerability Assessment
Avalablty-Based Path Selecton and Network Vulnerablty Assessment Song Yang, Stojan Trajanovsk and Fernando A. Kupers Delft Unversty of Technology, The Netherlands {S.Yang, S.Trajanovsk, F.A.Kupers}@tudelft.nl
More informationTraffic-light a stress test for life insurance provisions
MEMORANDUM Date 006-09-7 Authors Bengt von Bahr, Göran Ronge Traffc-lght a stress test for lfe nsurance provsons Fnansnspetonen P.O. Box 6750 SE-113 85 Stocholm [Sveavägen 167] Tel +46 8 787 80 00 Fax
More informationForecasting the Demand of Emergency Supplies: Based on the CBR Theory and BP Neural Network
700 Proceedngs of the 8th Internatonal Conference on Innovaton & Management Forecastng the Demand of Emergency Supples: Based on the CBR Theory and BP Neural Network Fu Deqang, Lu Yun, L Changbng School
More informationSoftware project management with GAs
Informaton Scences 177 (27) 238 241 www.elsever.com/locate/ns Software project management wth GAs Enrque Alba *, J. Francsco Chcano Unversty of Málaga, Grupo GISUM, Departamento de Lenguajes y Cencas de
More informationTraffic State Estimation in the Traffic Management Center of Berlin
Traffc State Estmaton n the Traffc Management Center of Berln Authors: Peter Vortsch, PTV AG, Stumpfstrasse, D-763 Karlsruhe, Germany phone ++49/72/965/35, emal peter.vortsch@ptv.de Peter Möhl, PTV AG,
More informationHP Mission-Critical Services
HP Msson-Crtcal Servces Delverng busness value to IT Jelena Bratc Zarko Subotc TS Support tm Mart 2012, Podgorca 2010 Hewlett-Packard Development Company, L.P. The nformaton contaned heren s subject to
More informationFuzzy TOPSIS Method in the Selection of Investment Boards by Incorporating Operational Risks
, July 6-8, 2011, London, U.K. Fuzzy TOPSIS Method n the Selecton of Investment Boards by Incorporatng Operatonal Rsks Elssa Nada Mad, and Abu Osman Md Tap Abstract Mult Crtera Decson Makng (MCDM) nvolves
More informationDynamic Fleet Management for Cybercars
Proceedngs of the IEEE ITSC 2006 2006 IEEE Intellgent Transportaton Systems Conference Toronto, Canada, September 17-20, 2006 TC7.5 Dynamc Fleet Management for Cybercars Fenghu. Wang, Mng. Yang, Ruqng.
More informationTHE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek
HE DISRIBUION OF LOAN PORFOLIO VALUE * Oldrch Alfons Vascek he amount of captal necessary to support a portfolo of debt securtes depends on the probablty dstrbuton of the portfolo loss. Consder a portfolo
More informationNetwork Aware Load-Balancing via Parallel VM Migration for Data Centers
Network Aware Load-Balancng va Parallel VM Mgraton for Data Centers Kun-Tng Chen 2, Chen Chen 12, Po-Hsang Wang 2 1 Informaton Technology Servce Center, 2 Department of Computer Scence Natonal Chao Tung
More informationPolitecnico di Torino. Porto Institutional Repository
Poltecnco d Torno Porto Insttutonal Repostory [Artcle] A cost-effectve cloud computng framework for acceleratng multmeda communcaton smulatons Orgnal Ctaton: D. Angel, E. Masala (2012). A cost-effectve
More informationAn Integrated Approach of AHP-GP and Visualization for Software Architecture Optimization: A case-study for selection of architecture style
Internatonal Journal of Scentfc & Engneerng Research Volume 2, Issue 7, July-20 An Integrated Approach of AHP-GP and Vsualzaton for Software Archtecture Optmzaton: A case-study for selecton of archtecture
More informationAnswer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy
4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.
More informationEfficient QoS Aggregation in Service Value Networks
22 45th Hawa Internatonal Conference on System Scences Effcent QoS Aggregaton n Servce Value etworks Steffen Haak Research Center for Informaton Technology (FZI) haak@fz.de Benjamn Blau SAP AG benjamn.blau@sap.com
More informationA Resource-trading Mechanism for Efficient Distribution of Large-volume Contents on Peer-to-Peer Networks
A Resource-tradng Mechansm for Effcent Dstrbuton of Large-volume Contents on Peer-to-Peer Networks SmonG.M.Koo,C.S.GeorgeLee, Karthk Kannan School of Electrcal and Computer Engneerng Krannet School of
More informationStaff Paper. Farm Savings Accounts: Examining Income Variability, Eligibility, and Benefits. Brent Gloy, Eddy LaDue, and Charles Cuykendall
SP 2005-02 August 2005 Staff Paper Department of Appled Economcs and Management Cornell Unversty, Ithaca, New York 14853-7801 USA Farm Savngs Accounts: Examnng Income Varablty, Elgblty, and Benefts Brent
More informationRobust Design of Public Storage Warehouses. Yeming (Yale) Gong EMLYON Business School
Robust Desgn of Publc Storage Warehouses Yemng (Yale) Gong EMLYON Busness School Rene de Koster Rotterdam school of management, Erasmus Unversty Abstract We apply robust optmzaton and revenue management
More informationPreventive Maintenance and Replacement Scheduling: Models and Algorithms
Preventve Mantenance and Replacement Schedulng: Models and Algorthms By Kamran S. Moghaddam B.S. Unversty of Tehran 200 M.S. Tehran Polytechnc 2003 A Dssertaton Proposal Submtted to the Faculty of the
More informationPerformance Management and Evaluation Research to University Students
631 A publcaton of CHEMICAL ENGINEERING TRANSACTIONS VOL. 46, 2015 Guest Edtors: Peyu Ren, Yancang L, Hupng Song Copyrght 2015, AIDIC Servz S.r.l., ISBN 978-88-95608-37-2; ISSN 2283-9216 The Italan Assocaton
More informationCredit Limit Optimization (CLO) for Credit Cards
Credt Lmt Optmzaton (CLO) for Credt Cards Vay S. Desa CSCC IX, Ednburgh September 8, 2005 Copyrght 2003, SAS Insttute Inc. All rghts reserved. SAS Propretary Agenda Background Tradtonal approaches to credt
More informationOptimization of network mesh topologies and link capacities for congestion relief
Optmzaton of networ mesh topologes and ln capactes for congeston relef D. de Vllers * J.M. Hattngh School of Computer-, Statstcal- and Mathematcal Scences Potchefstroom Unversty for CHE * E-mal: rwddv@pu.ac.za
More information"Research Note" APPLICATION OF CHARGE SIMULATION METHOD TO ELECTRIC FIELD CALCULATION IN THE POWER CABLES *
Iranan Journal of Scence & Technology, Transacton B, Engneerng, ol. 30, No. B6, 789-794 rnted n The Islamc Republc of Iran, 006 Shraz Unversty "Research Note" ALICATION OF CHARGE SIMULATION METHOD TO ELECTRIC
More informationDEFINING %COMPLETE IN MICROSOFT PROJECT
CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMI-SP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,
More informationFuzzy Set Approach To Asymmetrical Load Balancing In Distribution Networks
Fuzzy Set Approach To Asymmetrcal Load Balancng n Dstrbuton Networks Goran Majstrovc Energy nsttute Hrvoje Por Zagreb, Croata goran.majstrovc@ehp.hr Slavko Krajcar Faculty of electrcal engneerng and computng
More informationResearch on Evaluation of Customer Experience of B2C Ecommerce Logistics Enterprises
3rd Internatonal Conference on Educaton, Management, Arts, Economcs and Socal Scence (ICEMAESS 2015) Research on Evaluaton of Customer Experence of B2C Ecommerce Logstcs Enterprses Yle Pe1, a, Wanxn Xue1,
More informationEnabling P2P One-view Multi-party Video Conferencing
Enablng P2P One-vew Mult-party Vdeo Conferencng Yongxang Zhao, Yong Lu, Changja Chen, and JanYn Zhang Abstract Mult-Party Vdeo Conferencng (MPVC) facltates realtme group nteracton between users. Whle P2P
More informationA New Paradigm for Load Balancing in Wireless Mesh Networks
A New Paradgm for Load Balancng n Wreless Mesh Networks Abstract: Obtanng maxmum throughput across a network or a mesh through optmal load balancng s known to be an NP-hard problem. Desgnng effcent load
More informationA Dynamic Energy-Efficiency Mechanism for Data Center Networks
A Dynamc Energy-Effcency Mechansm for Data Center Networks Sun Lang, Zhang Jnfang, Huang Daochao, Yang Dong, Qn Yajuan A Dynamc Energy-Effcency Mechansm for Data Center Networks 1 Sun Lang, 1 Zhang Jnfang,
More informationSelecting Best Employee of the Year Using Analytical Hierarchy Process
J. Basc. Appl. Sc. Res., 5(11)72-76, 2015 2015, TextRoad Publcaton ISSN 2090-4304 Journal of Basc and Appled Scentfc Research www.textroad.com Selectng Best Employee of the Year Usng Analytcal Herarchy
More informationERP Software Selection Using The Rough Set And TPOSIS Methods
ERP Software Selecton Usng The Rough Set And TPOSIS Methods Under Fuzzy Envronment Informaton Management Department, Hunan Unversty of Fnance and Economcs, No. 139, Fengln 2nd Road, Changsha, 410205, Chna
More informationStochastic Protocol Modeling for Anomaly Based Network Intrusion Detection
Stochastc Protocol Modelng for Anomaly Based Network Intruson Detecton Juan M. Estevez-Tapador, Pedro Garca-Teodoro, and Jesus E. Daz-Verdejo Department of Electroncs and Computer Technology Unversty of
More informationActivity Scheduling for Cost-Time Investment Optimization in Project Management
PROJECT MANAGEMENT 4 th Internatonal Conference on Industral Engneerng and Industral Management XIV Congreso de Ingenería de Organzacón Donosta- San Sebastán, September 8 th -10 th 010 Actvty Schedulng
More informationComplex Service Provisioning in Collaborative Cloud Markets
Melane Sebenhaar, Ulrch Lampe, Tm Lehrg, Sebastan Zöller, Stefan Schulte, Ralf Stenmetz: Complex Servce Provsonng n Collaboratve Cloud Markets. In: W. Abramowcz et al. (Eds.): Proceedngs of the 4th European
More informationFair Virtual Bandwidth Allocation Model in Virtual Data Centers
Far Vrtual Bandwdth Allocaton Model n Vrtual Data Centers Yng Yuan, Cu-rong Wang, Cong Wang School of Informaton Scence and Engneerng ortheastern Unversty Shenyang, Chna School of Computer and Communcaton
More informationAn Introduction to 3G Monte-Carlo simulations within ProMan
An Introducton to 3G Monte-Carlo smulatons wthn ProMan responsble edtor: Hermann Buddendck AWE Communcatons GmbH Otto-Llenthal-Str. 36 D-71034 Böblngen Phone: +49 70 31 71 49 7-16 Fax: +49 70 31 71 49
More informationHow Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence
1 st Internatonal Symposum on Imprecse Probabltes and Ther Applcatons, Ghent, Belgum, 29 June 2 July 1999 How Sets of Coherent Probabltes May Serve as Models for Degrees of Incoherence Mar J. Schervsh
More informationUsing Series to Analyze Financial Situations: Present Value
2.8 Usng Seres to Analyze Fnancal Stuatons: Present Value In the prevous secton, you learned how to calculate the amount, or future value, of an ordnary smple annuty. The amount s the sum of the accumulated
More informationVision Mouse. Saurabh Sarkar a* University of Cincinnati, Cincinnati, USA ABSTRACT 1. INTRODUCTION
Vson Mouse Saurabh Sarkar a* a Unversty of Cncnnat, Cncnnat, USA ABSTRACT The report dscusses a vson based approach towards trackng of eyes and fngers. The report descrbes the process of locatng the possble
More informationResearch Article Enhanced Two-Step Method via Relaxed Order of α-satisfactory Degrees for Fuzzy Multiobjective Optimization
Hndaw Publshng Corporaton Mathematcal Problems n Engneerng Artcle ID 867836 pages http://dxdoorg/055/204/867836 Research Artcle Enhanced Two-Step Method va Relaxed Order of α-satsfactory Degrees for Fuzzy
More informationCanon NTSC Help Desk Documentation
Canon NTSC Help Desk Documentaton READ THIS BEFORE PROCEEDING Before revewng ths documentaton, Canon Busness Solutons, Inc. ( CBS ) hereby refers you, the customer or customer s representatve or agent
More informationAllocating Collaborative Profit in Less-than-Truckload Carrier Alliance
J. Servce Scence & Management, 2010, 3: 143-149 do:10.4236/jssm.2010.31018 Publshed Onlne March 2010 (http://www.scrp.org/journal/jssm) 143 Allocatng Collaboratve Proft n Less-than-Truckload Carrer Allance
More informationGenetic Algorithm Based Optimization Model for Reliable Data Storage in Cloud Environment
Advanced Scence and Technology Letters, pp.74-79 http://dx.do.org/10.14257/astl.2014.50.12 Genetc Algorthm Based Optmzaton Model for Relable Data Storage n Cloud Envronment Feng Lu 1,2,3, Hatao Wu 1,3,
More information1 Example 1: Axis-aligned rectangles
COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture # 6 Scrbe: Aaron Schld February 21, 2013 Last class, we dscussed an analogue for Occam s Razor for nfnte hypothess spaces that, n conjuncton
More informationIWFMS: An Internal Workflow Management System/Optimizer for Hadoop
IWFMS: An Internal Workflow Management System/Optmzer for Hadoop Lan Lu, Yao Shen Department of Computer Scence and Engneerng Shangha JaoTong Unversty Shangha, Chna lustrve@gmal.com, yshen@cs.sjtu.edu.cn
More informationPerformance Analysis of Energy Consumption of Smartphone Running Mobile Hotspot Application
Internatonal Journal of mart Grd and lean Energy Performance Analyss of Energy onsumpton of martphone Runnng Moble Hotspot Applcaton Yun on hung a chool of Electronc Engneerng, oongsl Unversty, 511 angdo-dong,
More informationMaster s Thesis. Configuring robust virtual wireless sensor networks for Internet of Things inspired by brain functional networks
Master s Thess Ttle Confgurng robust vrtual wreless sensor networks for Internet of Thngs nspred by bran functonal networks Supervsor Professor Masayuk Murata Author Shnya Toyonaga February 10th, 2014
More informationRelay Secrecy in Wireless Networks with Eavesdropper
Relay Secrecy n Wreless Networks wth Eavesdropper Parvathnathan Venktasubramanam, Tng He and Lang Tong School of Electrcal and Computer Engneerng Cornell Unversty, Ithaca, NY 14853 Emal : {pv45, th255,
More informationHow To Plan A Network Wide Load Balancing Route For A Network Wde Network (Network)
Network-Wde Load Balancng Routng Wth Performance Guarantees Kartk Gopalan Tz-cker Chueh Yow-Jan Ln Florda State Unversty Stony Brook Unversty Telcorda Research kartk@cs.fsu.edu chueh@cs.sunysb.edu yjln@research.telcorda.com
More informationLuby s Alg. for Maximal Independent Sets using Pairwise Independence
Lecture Notes for Randomzed Algorthms Luby s Alg. for Maxmal Independent Sets usng Parwse Independence Last Updated by Erc Vgoda on February, 006 8. Maxmal Independent Sets For a graph G = (V, E), an ndependent
More informationAn Integrated Dynamic Resource Scheduling Framework in On-Demand Clouds *
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 30, 1537-1552 (2014) An Integrated Dynamc Resource Schedulng Framework n On-Demand Clouds * College of Computer Scence and Technology Zhejang Unversty Hangzhou,
More informationInstitute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic
Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange
More information行 政 院 國 家 科 學 委 員 會 補 助 專 題 研 究 計 畫 成 果 報 告 期 中 進 度 報 告
行 政 院 國 家 科 學 委 員 會 補 助 專 題 研 究 計 畫 成 果 報 告 期 中 進 度 報 告 畫 類 別 : 個 別 型 計 畫 半 導 體 產 業 大 型 廠 房 之 設 施 規 劃 計 畫 編 號 :NSC 96-2628-E-009-026-MY3 執 行 期 間 : 2007 年 8 月 1 日 至 2010 年 7 月 31 日 計 畫 主 持 人 : 巫 木 誠 共 同
More informationThe Application of Fractional Brownian Motion in Option Pricing
Vol. 0, No. (05), pp. 73-8 http://dx.do.org/0.457/jmue.05.0..6 The Applcaton of Fractonal Brownan Moton n Opton Prcng Qng-xn Zhou School of Basc Scence,arbn Unversty of Commerce,arbn zhouqngxn98@6.com
More informationThe Greedy Method. Introduction. 0/1 Knapsack Problem
The Greedy Method Introducton We have completed data structures. We now are gong to look at algorthm desgn methods. Often we are lookng at optmzaton problems whose performance s exponental. For an optmzaton
More informationAN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE
AN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE Yu-L Huang Industral Engneerng Department New Mexco State Unversty Las Cruces, New Mexco 88003, U.S.A. Abstract Patent
More informationCourse outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy
Fnancal Tme Seres Analyss Patrck McSharry patrck@mcsharry.net www.mcsharry.net Trnty Term 2014 Mathematcal Insttute Unversty of Oxford Course outlne 1. Data analyss, probablty, correlatons, vsualsaton
More informationWhat is Candidate Sampling
What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble
More informationJoint Scheduling of Processing and Shuffle Phases in MapReduce Systems
Jont Schedulng of Processng and Shuffle Phases n MapReduce Systems Fangfe Chen, Mural Kodalam, T. V. Lakshman Department of Computer Scence and Engneerng, The Penn State Unversty Bell Laboratores, Alcatel-Lucent
More informationAn MILP model for planning of batch plants operating in a campaign-mode
An MILP model for plannng of batch plants operatng n a campagn-mode Yanna Fumero Insttuto de Desarrollo y Dseño CONICET UTN yfumero@santafe-concet.gov.ar Gabrela Corsano Insttuto de Desarrollo y Dseño
More information