DDoS Attacks Detection Model and its Application

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "DDoS Attacks Detection Model and its Application"

Transcription

1 DDoS Aacks Deecion Model and is Applicaion 1, MUHAI LI, 1 MING LI, XIUYING JIANG 1 School of Informaion Science & Technology Eas China Normal Universiy No. 500, Dong-Chuan Road, Shanghai 0041, PR. China Deparmen of Compuer Science Zaozhuang Universiy Bei-An Road, Shandong 77160, PR. China Absrac: Wih he proliferaion of Inerne applicaions and nework-cenric services, nework and sysem securiy issues are more imporan han before. In he pas few years, cyber aacks, including disribued denial-of-service (DDoS) aacks, have a significan increase on he Inerne, resuling in degraded confidence and russ in he use of Inerne. However, he presen DDoS aack deecion echniques face a problem ha hey canno disinguish flooding aacks from abrup changes of legiimae aciviy. In his paper, we give a model for deecing DDoS aacks based on nework raffic feaure o solve he problem above. In order o apply he model convenienly, we design is implemenaion algorihm. By using acual daa o evaluae he algorihm, he evaluaion resul shows ha i can idenify DDoS aacks. Key- words: Algorihm, Aack, Applicaion, DDoS, Deecion, Modal 1 Inroducion A DDoS aack is a Denial-of-Service (DOS) aack, i has become one of he major hreas and among he hardes securiy problems in oday s inerne. whose impac has been well demonsraed in many compuer nework lieraures. A DoS aack is characerized by an explici aemp by aackers o preven legiimae users of a service from using ha service [1]. Examples include aemps o "flood" a nework, in order o preven legiimae nework raffic. aemps o disrup connecions beween wo machines, hereby prevening access o a service. aemps o keep a paricular individual from accessing a service. aemps o sop service o a specific sysem or person. The goal of a DoS aack is o preven a compuer or nework from providing normal services. The mos common DoS aacks will arge he compuer's nework bandwidh or conneciviy. Bandwidh aacks flood he nework wih such a high volume of raffic ha all available nework resources are consumed and legiimae user requess canno be responded. Conneciviy aacks flood a compuer wih so many connecion requess ha hey consume all available operaing sysem resources, and resul in he compuer can no longer process requess of legiimae user. Disribued Denial of Service (DDoS) is a relaively simple, ye very powerful echnique o aack Inerne resources. DDoS aack adds he many-o-one dimension o he DoS aack problem, and makes he prevenion and miigaion of hese aacks more difficul and he impac proporionally ISSN: Issue 8, Volume 7, Augus 008

2 severe. Unlike DoS aacks ha rely on a specific nework proocol or a sysem weakness, he DDoS aackers do no require o maser high compuer echnologies, hey can aack a sie server wih simply exploiing he huge resource asymmery beween he Inerne and he vicim, namely many o one. Before aacking, he aackers have conrolled a sufficien number of zombies. Then hey command hese zombies generae so huge amouns of useless packes ha overwhelm vicim. Boh DoS and DDoS aacks are have he same goal, his is o say, all of hey wan o ie up cerain nework resources compleely so ha he vicim server denies services for legiimae users. Compared wih a DoS aack, A DDoS aack is very difficul o be defended. Because DDoS aack can make use of opening inerne feaure, which is ha a large number of users can be permied o visi he same sie server a he same ime. The feaure of inerne makes he DDoS aack be able o block access o he horoughfare reaching he vicim, effecively aking he vicim off he Inerne so ha any vicim-level of defense becomes irrelevan. In addiion, he DDoS aack s sraegies of hierarchical aack and he echnologies of IP spoofing make aackers difficul o be raced. Alhough grea effors have been involved in aack deecion and prevenion, here is sill a lack of effecive and efficien soluions o inercep ongoing aacks in a imely fashion, i.e. shor enough o preven raffic build up from DDOS aack. By now, DDoS aacks have risen o be he Number 1 hrea on he Inerne [], DDoS aacks are comprised of packe sreams from disparae aack sources. Aacker can coordinae he power of a vas number of Inerne zombies o consume some criical resource of he arge and makes he sie server deny he service o legiimae cliens. Aack raffic is usually so similar o normal raffic ha i is difficul o disinguish legiimae aack packes from normal packes. A he same ime, he packe sreams of DDoS aack have no apparen characerisics ha could be direcly and wholesalely used for deecion and filering. For keeping from racing, aackers afford o change aack packe fields (especially IP address). Wih he rapid developmen of compuer echnologies, here are more and more exremely sophisicaed, user-friendly and powerful DDoS oolkis, i makes DDoS aacking programs have very simple logic srucures and possess less memory sizes, and makes hem relaively easy o implemen and hide. Aackers consanly change heir ools o bypass inspecion of securiy sysems developed by sysem managers and researchers, who are in a consan aler o modify heir approaches o handle new aacks. The DDoS field is evolving quickly, i is becoming increasingly hard o deec he aack. DDoS aacks are geing more sophisicaed, spreading faser, and causing more damages [3]. However, here have no been developed fundamenal defense soluions of DDoS aacks since hese aacks have firsly appeared in June 1998 [4]. Therefore, i is necessary o sudy a new deecion model and keep away DDoS aacks. he goal of DDoS Aacks is in order o make he sie deny he service of legiimae users, i is necessary o send such a large number of garbage packes o vicim ha he vicim s sysem has no abiliy o handle hem. Therefore, he mehod recognizing abnormal increase of raffic is he shorcu o deec DDoS aack. In his paper, we jus use he ideal o build a modal o solve he deecion problem of DDoS aacks. Following his inroducion, he paper is organized as follows. Secion inroduces previous work on DDoS aacks. Secion 3 gives he mehod how o build deecing modal. In his secion, we discuss he feaure of nework raffic, which is he base o build deecing modal, and give an implemen algorihm of he deecing modal. Secion 4 applies deecing algorihm o verify validiy of he modal. Secion 5 draws he conclusion of his paper. Previous work ISSN: Issue 8, Volume 7, Augus 008

3 There are a number of DDoS and DoS aack sudies [57], Mos of hem address vulnerabiliies or possible counermeasures, bu few focus on aack deecion. More recen repors [814], In [8], Anderson e al. rely on he use of a send-permission-oken o resric DoS aacks. Kreibich e al. use a decoy compuer, paern-maching echniques, and proocol conformance checks echnologies o creae inrusion deecion signaures [9]. In [10], Allen e al. use esimaes of he Hurs parameer o idenify aacks ha cause a decrease in he raffic s self-similariy. This mehod requires saisics of nework raffic self-similariy before he aack. Yu e al. give a saisical mehod, namely, Logisic Regression wih separae proocols [11]. The mehod is a heoreical mehod for finding feaures in inrusion deecion. Using he Suppor Vecor Machine mehod, he separae proocol model provides beer resuls wih high classificaion accuracy and low false alarm rae. In [1], a general classificaion of DDoS aacks and mehods o deal wih hem is given. The mehods can deec each kind of DDoS aacks and choose an appropriae defense mechanism auomaically. Wih he grea developmen of wavele echnology, many papers use he echnology o build deecion DDoS models [180], In [18], Carl e al. modify CUSUM approach o deec aacks by wavele analysis. In he papers [19,0], hey find DDoS aack poins by wavele decomposiion of signals wih singulariies. In he paper [3], Feinsein e al. provide wo saisical mehods of analyzing nework raffic o find DDoS aacks. One moniors he enropy of he source addresses found in packe headers, while he oher moniors he average raffic raes of he mos acive addresses. Some papers, e.g., [1,, 4] use probabilisic echniques, such as covariance ec, o deec aacks. All DDoS deecion mehods define an aack as an abnormal and noiceable deviaion of some saisic of he moniored nework raffic workload. Clearly, he choice of saisic-based deecion echniques is criically imporan. In [15], Glenn Carl e al. give a conclusion abou mehods of deecing aacks. A presen, here are hree kinds of deecion echnologies such as aciviy profiling, change poin deecion, and wavele-based signal analysis, bu all hese echniques face he considerable challenge of discriminaing neworkbased flooding aacks from sudden increases in legiimae aciviy or flash evens. In order o mee he challenge, we have done many research works, e.g., [16, 17]. In paper [16], we give a deecion model wih low false alarm and low miss probabiliy. In paper [16], we apply he Hurs parameer esimae o deermine wheher he sysem is under aacks. Alhough each deecion echnique shows promise in limied esing, none compleely solves he deecion problem [15]. The major shorcoming of classic echniques is ha hey do no disinguish anomalies from aacks. For example, hey canno be differen anomalies from sudden changes a 08:00 a.m., which is he beginning of office hours [19]. In his paper, we ry o solve he problem above wih using known normal raffic before deecing aacks. 3 Deecion model Le y() denoe a sie oal raffic, which is he number of byes arriving a a sie (or server) a ime. Hereby, y() can be divided ino normal raffic n() and aack raffic a(), where aack raffic is generaed by aackers. Then y() can be absracly expressed by y( ) n( ) a( ) (1) Obviously, when a sie is no aacked, a() 0, his is o say, y() n(). When he sie is under aacks, a() will rapidly increase o high level. Therefore, if we can ge he value of a() during deecion, i should be very easy o discover aacks. Unforunaely, we have no way o ge a() direcly during deecing aacks. However, y() can be ISSN: Issue 8, Volume 7, Augus 008

4 capured wih sniffer sofware convenienly. According o Eq. (1), if we can ge he value of n(), hen he aforemenioned problem can be solved simply. Bu n() is also unknown in a period of deecion ye. Hence, how o ge n() becomes an essenial problem. 3.1 Feaure analysis of raffic In order o solve he problem above, i is necessary o know he feaures of nework raffic. For achieving he aim of DDoS aacks, he aackers mus sen large volume of garbash packes o vicim. Therefore, he aacks raffic is usually far more han normal raffic. This is a basic feaure of raffic. Abou raffic feaure, here are many lieraures o sudy i, e.g., [3, 58]. In [3], Feinsein e al. discusses wo kinds of deecion mehods. They define enropy H, and give a compuing formula following as: n H p log i, i1 i p where p i is probabiliy of n independen symbols, he symbols can be IP addresses. Hence, he enropy can be compued on a sample of consecuive packes. Through experimens, hey have observed ha while a nework is no under aack, he enropy value of user IP addresses falls in narrow range. According o he definiion of enropy, he value of enropy is acually he puriy of IP addresses. This means ha he number of new IP address is proporional o he one of old IP addresses. Acually, he old IP addresses represen common users of he sie, and new addresses can be regard as new users or random users. A he same ime, experimens also show ha he number of common users is far more han he one of he new users of he sie in normal sae [8], During sudying feaure of nework raffic, we have done many saisic experimens abou IP addresses, and also discovered he phenomenon above. We call he phenomenon he one of raffic feaures: In normal sae, he common users of a sie are sable, and he raio of he number of hem o he one of all he sie users is approximaely a consan. Tha is o say, if le n C () and n A () denoe he number of common users and all users a ime respecively, hen nc () n () a is almos a cons. In [6], Barford e al. give few raffic curves of weeks. These curves can clearly show similariy of raffic. Especially he raffic curves of he same day in differen weeks are so. This is anoher feaure of raffic: In he normal sae, raffic has daily and weekly cycles [6, 7].This is o say, he raffic is similar a he same ime of differen daes in a cerain period. If le C denoe ime cycle value of a day or a week, hen y() is almos equal o y(+c). Fig. 1 and Fig. can clearly show he feaure. For example, he raffic a 8 a.m. on Dec. 18 is similar o he one a same ime on Dec. 19. Fig. 1: The curve of raffic on Dec. 18, 007. Noe: The abrup changes around 3 p.m. include aack raffic in Fig. 1. The reason why raffic has hese feaures is mainly ha a sie provides sable services in a cerain period. On he one hand, he sable services cerainly consrain he requiremen of is users. On he oher hand, every user has sable requiremen for he server, and seady work habi. The wo elemens deermine a server has sable common users. ISSN: Issue 8, Volume 7, Augus 008

5 Fig. : The curve of raffic on Dec. 19, 007. Noe: The abrup changes around 9 a.m. include aack raffic in Fig.. Undoubedly, in normal sae, here are few random users o visi he sie, bu hey only browse he web accidenally. Hence, he raffic, which he random users generae, is far lower han he one of common users. Therefore, common users deerminae ha raffic of a sie has similar feaure. According o he similar feaure of nework raffic, we can use saisic raffic, which came from a sie under no aacks before deecion, insead of normal raffic during deecion. Le N() denoe he saisic raffic. So Eq. (1) can be rewrien as a( ) y( ) N( ) () Due o having known y() and N(), we can build a deecion model based on formula (). To his purpose, we inroduce a lemma as follows: Lemma 1. x i ( i 1,,, n) are n independen random variables, y x1 x... xn, For large n (e.g., n30), he disribuion of y approaches a normal disribuion. This lemma is jus he cenral limi heorem in probabiliy heory [9]. Theorem 1. In normal sae, if he number of a sie users is invarian, hen he disribuion of y() approaches he normal disribuion. Proof. According o he condiion of he heorem, we can assume ha he sie server has m users. Hereby, y() can be expressed by y( ) y ( ) y ( )... y ( ), 1 n where i1,,, m, y i () is he raffic generaed by he ih user. In normal sae, he sie users are independen of each oher, so heir raffic y 1 (), y (),, y m () are naurally independen. In addiion, he number of a sie users is generally far greaer han 30. Therefore, he raffic y() saisfies he condiion of lemma 1, he conclusion of he heorem is rue. I is naural o hink ha we can build a deecing model based on Theorem 1. Unforunaely, he condiion of Theorem 1 is no always saisfied, and someimes he number of sie users changes promply. For insance, he number of he sie users will be abrup increase a 08:00 a.m.. Because he ime is beginning of office hours, here are many users log in he sie, and lead o raffic increase rapidly, Fig. 1 and Fig. show i clearly. Obviously, he model relaes wih he saring ime of deecion. So if using he model o deec aacks, he resul may no be good. According o raffic similar feaure, in normal sae, y() N(), namely a(), can eliminae he majoriy of abrup changes. However, we canno use a() o build he deecing model ye, because he value of a() is mainly deermined by he random raffic. According o he second feaure of raffic, he random raffic is proporional o normal raffic, a he same ime, he number of common users relaes wih he deecing ime. Therefore, i is no beer ha only using he value of a() o build deecing modal. The second raffic feaure can be used o solve he problem above, we discover ha a () is a random variable, and is independen of he beginning ime of deecion in normal sae, So, we use i o build deecing model. In he res of his paper, le A() denoes a (). Theorem. In normal sae, he disribuion of A() approaches he normal disribuion wih mean 0, ISSN: Issue 8, Volume 7, Augus 008

6 and i is independen of he number of sie users. Proof. We assume he number of common users is m a ime, Le nm () and Nm () be raffic of he m common users respecively. Le r, and s be he number of he oher users of y() and N() a ime respecively, where he oher users are jus random users. Le nr () and Ns () be he random users raffic. Hence, we have N( ) N ( ) N ( ), y( ) n ( ) n ( ). Then a () A () s nm ( ) N ( ) m r nr ( ) N ( ) s. In normal sae, y() is jus n(), According o he similar feaure of raffic, n( ) N( ), n () is far greaer han nr (), namely, N() nm () nr (), where >> denoes far more han. Similarly, N() Nm () N (). This means ha n ( ) N ( ) r s s is almos zero. Hence, he disribuion of A() is deermined by he one of n ( ) N ( ). Since () m n and N () come from he same group of common users, hence he disribuion of n ( ) N ( ) Because of he raffic feaure, nm m m has mean zero. () and Nm () are almos consans which are independen of m, (i.e. he number of common users ), According o Theorem 1, he disribuion of n ( ) N ( ) approaches has normal disribuion wih mean Building deecion model When he sie is under aack, nr () includes aack raffic, his leads o nr ( ) N( ). Hence, he mean of A() is far greaer han zero. Using Theorem, we can ge a deecing mehod: if A() yields normal disribuion wih mean zero, we can deermine he server is secure, oherwise, here are aacks. We will build a model for deecing aacks wih he parameers esimae mehod of probabiliy heory. Le T and be he number of samples and he mean of random variable A() respecively, u(t) is he sample mean of A() wih T samples. For he variance of A() is unknown, in order o esimae he mean, we form he sample variance S(T): T 1 1 S ( T) [ A( ) u( T)]. T 1 0 In fac, he S (T) is an unbiased esimae of he variance of A() [9]. Thus, under he assumpion ha A() is normal, he raio ut ( ) S( T) / T has a Suden- disribuion wih T1 degrees of freedom [9]. Using he disribuion, we can esimae he mean η. If we have known he confidence coefficien P, hen η yields he approximae confidence inerval S( T) S( T) u( T) u( T), T where δ=1p, 1 T and 1 are he perceniles of he disribuion respecively. Appling acual daa o his model, we discover, if he sie is no under aack, he confidence inerval of η is included in (0.5, 0.5). Oherwise, he relaion above is no rue. Thus, we obain a model for deecing DDoS aacks. Using he model above, we give a run-ime deecing algorihm as follows ISSN: Issue 8, Volume 7, Augus 008

7 1) Assign P and T an iniial value respecively. he saring ime of deecion is 0. ) Open a daabase, which has sored saisic raffic of he sie. Fech daa from he daabase and load he daa ino array N(); These daa correspond wih he ime from 0 o T 1. 3) Se u(0) N(0); S(0) y(0), where y(0) is he raffic daum a saring ime 0. 4) Judge wheher he relaion T is saisfied. If he answer is rue, go o 8). 5) Capure he raffic of he sie a ime, and load i ino y(). 6) Compue u() and S(). 7) Le = 1, and go o 4). 8) Compue he confidence inerval of η, his is S( T) S( T) ( u( T), u( T) ), T 1 P (1 P) 1 T where u(t) and S(T) can be compued wih recursive algorihm below. 9) Judge wheher he confidence inerval of η is included in (0.5, 0.5), if he resul is Yes, hen he sie is safe; oherwise, gives an aack alarm. 10) End. For improving efficiency of deecion, u() and S() can be compued wih recursive algorihm. The recursive algorihm of u() is as follows: a ( ) u( ) A( s) A( s) N() s0 s0 1 1 y( ) u ( 1) [ 1]. N() The recursive algorihm of S() is 1 S ( ) [ A( s) u( )] 1 1 s [ ( ) ( ) ( ) ( ) A s u A s u ] 1 s0 s0 s0 1 1 A( s) u( ) u( ) 1 s0 1 1 [ A( ) u( ) ], 1 where y( ) A( ) A( s) A( 1) [ 1]. N() s0 Obviously, he algorihms ime complexiy is O(T). Hence, he recursive algorihms make he deecing modal has more high efficiency of execuion, and can help he modal finish run-ime deecion of DDoS aacks. 4 Model Applicaion For verifying he algorihm above, we sample a large of daa from a cenral server in Zaozhuang Universiy wih Sniffer sofware. The sample ime inerval is 10s. Fig. 3 shows he curve of he daa. Fig. 3 is componen of hree secions; he firs secion is saisic raffic, which was sampled before deecion wihou aacks. The oher wo secions represen he daa sampled on Dec. 1 and Dec. 19 in 007 respecively. Fig. 3: Saisic and deecion raffic From he Fig. 3, i is obvious o see ha he raffic has a similar characerisic. We can also discover some abnormal raffic in he figure. In fac, some of hem are generaed wih aack sofware. We apply aack sofware o aack he server hree imes. Two of hem occurred on Dec. 18, he firs aack was a :33 p.m., and he ime lengh of he aack is 10 minues. The second aack is a 3:36 p.m., he ime lengh of aack is 8 minues.. There was one aack o he sie on Dec.19, and he aack lased 11 minues. From Fig. 3, we can see he abrup changes of raffic a corresponding ime. ISSN: Issue 8, Volume 7, Augus 008

8 No. Saring ime Confidence inerval sae 1 8:30 (0.0151, ) no 10:01 (0.1409, ) no 3 14:33 (.3799,3.045) yes 4 15:36 (3.6149,5.0761) yes Fig. 4: The curve of A() on Dec. 18, 007 Fig. 4 is he curve of A() on Dec. 18, 007. From he figure, i can be easy o see ha he curve is independen of raffic scale. Tow abrup changes represen he sie is being under aacks a ha ime. Noe: In Table 1, yes represens he sie is under aacks, no means no. The able 1 shows ha wo Confidence inervals are no include in (0.5,05), his means ha he sie was under aacks a :33 p. m. and 3:36 p. m. on Dec. 18, 007 respecively. Table: The deecion resuls on Dec. 19, 007 No. Saring ime Confidence inerval sae 1 8:05 (0.973,0.3697) no 8: (0.0449,0.1684) no Fig. 5: The curve of A() on Dec. 19, 007 Fig. 5 represens he curve of A() on Dec. 19, 007. We can clear see a abrup change in he figure, he change is caused by aack raffic, and shows ha he sie is being under aacks a ha ime. In addiion, we can also see ha he curve is independen of nework raffic scale. For improving he efficiency of deecion, we se an alarm value. Once he raffic of he server reaches i, deecion program will sar auomaically. In his paper, he alarm value is ; he lengh of deecion ime is 10 minues; confidence coefficien P is 0.95; he sample ime inerval is 10s. On Dec. 18, 007, he deecion program was execued four imes; wo of hem gave aack alarm. On anoher day, he server was deeced five imes auomaically, we go wo aack alarms. Table 1 and Table show he resuls of deecion. Table 1: The deecion resuls on Dec. 18, :55 (3.534,3.743) yes 4 9:05 (0.954,0.759) yes 5 10:9 (0.1439,0.060) no Noe: The meaning of yes and no in Table is he same as he one in Table1. Table shows as if ha he sie server was under aacks wo imes on Dec. 19, 007. However, we acually aacked he sie one ime on ha day. This is because he lengh of aack ime is longer han he one of deecion ime. Thus, he fourh deecion used 1-minue aack daa. Therefore, we received wo alarms. The example shows ha our deecion algorihm can idenify wheher he server is under aacks. 5 Conclusion In his paper, by sudying he basic feaure of raffic, we give a model of deecing DDoS aacks. The model canno be influenced by abrup changes of ISSN: Issue 8, Volume 7, Augus 008

9 normal raffic, and is independen of he saring ime of deecion. Hence he modal do i s beer in deecing DDoS aack. During deecion, he modal do no used he signaures of DDoS aacks, so i can deec unknown DDoS aacks. This is o say he deecing modal is more robus. In order o realize run-ime deecion, we give an implemenaion algorihm of he model wih simple srucure, low complexiy, and low memory possession. Wih acual daa o es he algorihm, he resuls show he algorihm can rapidly idenify wheher he server is under aacks. However, he deecing modal is dependen on saisic raffic before deecion, he qualiy of he saisic raffic direcly affec on he resul of deecing. Thus, i is very imporan o know he normal sae of he sie, and capure nework raffic in ime. During he deecion of DDoS aacks, we use he confidence inerval (0.5,05), In fac, he confidence inerval is no invarian, i may vary wih he difference of sie, and relae wih he precision of deecion. If we require he modal can recognize he DDoS aacks ha have sligh aack raffic, he inerval should be se up more small. Usually, he confidence inerval (0.5,05) is good choice for deecion. In fuure, we will sudy he conrol funcion of firewalls and rouers abou raffic, and ry o build a managemen sysem, which can auomaically deec, conrol, and manage he server. Acknowledgemen This work was suppored in par by he Naional Naural Science Foundaion of China under he projec gran number , and by he Research and Developmen Projec of Shandong Provincial Educaion Deparmen under he projec number J07WJ9. Reference [1] Denial of Service Aacks hp:// /ech_ips/denial_of_service.hml, 008. [] Background on DDoS hp:// index.php?conen=producs/background.hml, 008. [3] Ediorial, Disribued denial-of-service and inrusion deecion, Journal of Nework and Compuer Applicaions, Vol. 30, 007, pp [4] K. Lee, K. Kim, e al., DDoS aack deecion mehod using cluser analysis, Exper Sysems wih Applicaions, 007, doi: /j.eswa [5] V. Paxson, Bro: a sysem for deecing nework inruders in realime, Compuer Neworks Vol. 31, 1999, pp [6] L. Ricciuli, P. Lincoln, P. Kakkar, TCP SYN flooding defense, Communicaion Neworks and Disribued Sysems Modeling and Simulaion (CNDS '99), 1999, pp [7] E. Sroher, Denial of service proecion he Nozzle, In: Proceedings of he 16h annual compuer securiy applicaions conference (ASAC 00), 000, pp [8] T. Anderson, T. Roscoe, D.Weherall, Prevening inerne denial-of-service wih capabiliies, Compuer Communicaions Review, Vol. 34, No. 1, 004, pp. 39 [9] C. Kreibich, J. Crowcrof, Honeycomb creaing inrusion deecion signaures using honeypos, Compuer Communicaion Review (ACM SIGCOMM), Vol. 34, No. 1, 004, pp [10] W. Allen, G. Marin, The LoSS echnique for deecing new denial of service aacks, SouheasCon, 004. Proceedings. IEEE, 004, pp [11] K. M. Yu, M. F. Wu, Proocol-Based Wih Feaure Selecion in Inrusion Deecion, WSEAS Transacions on Compuer, Vol. 3, No. 3, 008, pp [1] A. Asosheh, N. Ramezania, A comprehensive faxonomy of DDoS aacks and defense mechanism applying in a smar classificaion, WSEAS Transacions on Communic- aions, Vol 7, No. 4, 008, pp [13] H. Sun, B. Fang, H. Zhang, A new inrusion ISSN: Issue 8, Volume 7, Augus 008

10 Deecion Approach based on Nework Tomography, WSEAS Transacions on Informaion Science & Applicaions, Vol.3, No., 006, pp [14] D. H. Kang, B. K. Kim, J. T. Oh, Proocol anomaly and paern maching based inrusion deecion sysem, WSEAS Transacions on Communicaion, Vol.4, No. 10, 005, pp [15] G. Carl, e al., DenialofService Aack Deecion Techniques, IEEE Inerne Compu- ing, Vol. 10, No. 1, 006, pp [16] M. Li, An approach o reliably idenifying signs of DDoS flood aacks based on LRD raffic paern recogniion, Compuers & Securiy, Vol. 3, 004, pp [17] M. Li, Change rend of averaged Hurs parameer of raffic under DDoS flood aacks, Compuers & Securiy, Vol. 5, No. 3, 006, pp [18] G. Carl, R. R. Brook, S. Rai, Wavele based denial-of-service deecion, Compuers & Securiy, Vol. 5, 006, pp [19] M. Hamdi, N. Boudriga, Deecing denial- ofservice aacks using he wavele ransform, Compuer Communicaions. Vol. 30, 007, pp [0] A. Anoniadis, I. Gijbels, Deecing abrup changes by wavele mehods, Technical Repor, Laboraoire LMC-IMAG. France: Universie Joseph Fourier, [1] N. Ye, X. Li, e al., Probabilisic echniques for inrusion deecion based on compuer audi daa, IEEE Transacions on Sysems, Man and Cyberneics Par A: Sysems and Humans, Vol. 31, No.4, 001, pp [] S.Y. Jina, e al., Nework inrusion deecion in covariance feaure space, Paern Recogniion, Vol. 40, 007, pp [3] L. Feinsein, e al., Saisical approaches o DDoS aack deecion and response, In: DARPA informaion survivabiliy conference and exposiion proceedings, Vol. 1, 003, pp [4] C.Krugel, T.Toh, E.Kirda, Service specific anomaly deecion for nework inrusion deecion, ACM, 00. [5] P. Abry, R. Baraniuk, e al., Muliscale naure of nework raffic, IEEE Signal Processing, Vol. 19, No. 3, 00, pp [6] P. Barford, J. Kline, D. Plonka, A.Ron, A signal analysis of nework raffic anomalies, In: Proceedings of ACM SIGCOMM Inerne measuremen workshop, Marseilles, France, 00. [7] V. Paxson, Measuremens and analysis of end-o-end inerne dynamics, Ph.D. hesis, Universiy of California Berkeley, [8] T. Peng, C. Leckie and R. Koagiri. Deecing reflecor aacks by sharing beliefs, In: Proceedings of IEEE Global Conference, Globecom, 003. [9] A. Papuilis, S. U. Pillai, Probabiliy, Random Variables, sochasic Processes, McGraw-Hill Inc., 00. ISSN: Issue 8, Volume 7, Augus 008

TEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS

TEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS TEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS RICHARD J. POVINELLI AND XIN FENG Deparmen of Elecrical and Compuer Engineering Marquee Universiy, P.O.

More information

Mining Web User Behaviors to Detect Application Layer DDoS Attacks

Mining Web User Behaviors to Detect Application Layer DDoS Attacks JOURNAL OF SOFWARE, VOL. 9, NO. 4, APRIL 24 985 Mining Web User Behaviors o Deec Applicaion Layer DDoS Aacks Chuibi Huang Deparmen of Auomaion, USC Key laboraory of nework communicaion sysem and conrol

More information

Task is a schedulable entity, i.e., a thread

Task is a schedulable entity, i.e., a thread Real-Time Scheduling Sysem Model Task is a schedulable eniy, i.e., a hread Time consrains of periodic ask T: - s: saring poin - e: processing ime of T - d: deadline of T - p: period of T Periodic ask T

More information

Towards Intrusion Detection in Wireless Sensor Networks

Towards Intrusion Detection in Wireless Sensor Networks Towards Inrusion Deecion in Wireless Sensor Neworks Kroniris Ioannis, Tassos Dimiriou and Felix C. Freiling Ahens Informaion Technology, 19002 Peania, Ahens, Greece Email: {ikro,dim}@ai.edu.gr Deparmen

More information

USE OF EDUCATION TECHNOLOGY IN ENGLISH CLASSES

USE OF EDUCATION TECHNOLOGY IN ENGLISH CLASSES USE OF EDUCATION TECHNOLOGY IN ENGLISH CLASSES Mehme Nuri GÖMLEKSİZ Absrac Using educaion echnology in classes helps eachers realize a beer and more effecive learning. In his sudy 150 English eachers were

More information

Journal Of Business & Economics Research September 2005 Volume 3, Number 9

Journal Of Business & Economics Research September 2005 Volume 3, Number 9 Opion Pricing And Mone Carlo Simulaions George M. Jabbour, (Email: jabbour@gwu.edu), George Washingon Universiy Yi-Kang Liu, (yikang@gwu.edu), George Washingon Universiy ABSTRACT The advanage of Mone Carlo

More information

Duration and Convexity ( ) 20 = Bond B has a maturity of 5 years and also has a required rate of return of 10%. Its price is $613.

Duration and Convexity ( ) 20 = Bond B has a maturity of 5 years and also has a required rate of return of 10%. Its price is $613. Graduae School of Business Adminisraion Universiy of Virginia UVA-F-38 Duraion and Convexiy he price of a bond is a funcion of he promised paymens and he marke required rae of reurn. Since he promised

More information

Automatic measurement and detection of GSM interferences

Automatic measurement and detection of GSM interferences Auomaic measuremen and deecion of GSM inerferences Poor speech qualiy and dropped calls in GSM neworks may be caused by inerferences as a resul of high raffic load. The radio nework analyzers from Rohde

More information

DYNAMIC MODELS FOR VALUATION OF WRONGFUL DEATH PAYMENTS

DYNAMIC MODELS FOR VALUATION OF WRONGFUL DEATH PAYMENTS DYNAMIC MODELS FOR VALUATION OF WRONGFUL DEATH PAYMENTS Hong Mao, Shanghai Second Polyechnic Universiy Krzyszof M. Osaszewski, Illinois Sae Universiy Youyu Zhang, Fudan Universiy ABSTRACT Liigaion, exper

More information

A dynamic probabilistic modeling of railway switches operating states

A dynamic probabilistic modeling of railway switches operating states A dynamic probabilisic modeling of railway swiches operaing saes Faicel Chamroukhi 1, Allou Samé 1, Parice Aknin 1, Marc Anoni 2 1 IFSTTAR, 2 rue de la Bue Vere, 93166 Noisy-le-Grand Cedex, France {chamroukhi,same,aknin}@ifsar.fr

More information

4. The Poisson Distribution

4. The Poisson Distribution Virual Laboraories > 13. The Poisson Process > 1 2 3 4 5 6 7 4. The Poisson Disribuion The Probabiliy Densiy Funcion We have shown ha he k h arrival ime in he Poisson process has he gamma probabiliy densiy

More information

Trends in TCP/IP Retransmissions and Resets

Trends in TCP/IP Retransmissions and Resets Trends in TCP/IP Reransmissions and Reses Absrac Concordia Chen, Mrunal Mangrulkar, Naomi Ramos, and Mahaswea Sarkar {cychen, mkulkarn, msarkar,naramos}@cs.ucsd.edu As he Inerne grows larger, measuring

More information

Performance Center Overview. Performance Center Overview 1

Performance Center Overview. Performance Center Overview 1 Performance Cener Overview Performance Cener Overview 1 ODJFS Performance Cener ce Cener New Performance Cener Model Performance Cener Projec Meeings Performance Cener Execuive Meeings Performance Cener

More information

A Scalable and Lightweight QoS Monitoring Technique Combining Passive and Active Approaches

A Scalable and Lightweight QoS Monitoring Technique Combining Passive and Active Approaches A Scalable and Lighweigh QoS Monioring Technique Combining Passive and Acive Approaches On he Mahemaical Formulaion of CoMPACT Monior Masai Aida, Naoo Miyoshi and Keisue Ishibashi NTT Informaion Sharing

More information

Markov Models and Hidden Markov Models (HMMs)

Markov Models and Hidden Markov Models (HMMs) Markov Models and Hidden Markov Models (HMMs (Following slides are modified from Prof. Claire Cardie s slides and Prof. Raymond Mooney s slides. Some of he graphs are aken from he exbook. Markov Model

More information

Single-machine Scheduling with Periodic Maintenance and both Preemptive and. Non-preemptive jobs in Remanufacturing System 1

Single-machine Scheduling with Periodic Maintenance and both Preemptive and. Non-preemptive jobs in Remanufacturing System 1 Absrac number: 05-0407 Single-machine Scheduling wih Periodic Mainenance and boh Preempive and Non-preempive jobs in Remanufacuring Sysem Liu Biyu hen Weida (School of Economics and Managemen Souheas Universiy

More information

Measuring macroeconomic volatility Applications to export revenue data, 1970-2005

Measuring macroeconomic volatility Applications to export revenue data, 1970-2005 FONDATION POUR LES ETUDES ET RERS LE DEVELOPPEMENT INTERNATIONAL Measuring macroeconomic volailiy Applicaions o expor revenue daa, 1970-005 by Joël Cariolle Policy brief no. 47 March 01 The FERDI is a

More information

Market Liquidity and the Impacts of the Computerized Trading System: Evidence from the Stock Exchange of Thailand

Market Liquidity and the Impacts of the Computerized Trading System: Evidence from the Stock Exchange of Thailand 36 Invesmen Managemen and Financial Innovaions, 4/4 Marke Liquidiy and he Impacs of he Compuerized Trading Sysem: Evidence from he Sock Exchange of Thailand Sorasar Sukcharoensin 1, Pariyada Srisopisawa,

More information

ANALYSIS AND COMPARISONS OF SOME SOLUTION CONCEPTS FOR STOCHASTIC PROGRAMMING PROBLEMS

ANALYSIS AND COMPARISONS OF SOME SOLUTION CONCEPTS FOR STOCHASTIC PROGRAMMING PROBLEMS ANALYSIS AND COMPARISONS OF SOME SOLUTION CONCEPTS FOR STOCHASTIC PROGRAMMING PROBLEMS R. Caballero, E. Cerdá, M. M. Muñoz and L. Rey () Deparmen of Applied Economics (Mahemaics), Universiy of Málaga,

More information

TSG-RAN Working Group 1 (Radio Layer 1) meeting #3 Nynashamn, Sweden 22 nd 26 th March 1999

TSG-RAN Working Group 1 (Radio Layer 1) meeting #3 Nynashamn, Sweden 22 nd 26 th March 1999 TSG-RAN Working Group 1 (Radio Layer 1) meeing #3 Nynashamn, Sweden 22 nd 26 h March 1999 RAN TSGW1#3(99)196 Agenda Iem: 9.1 Source: Tile: Documen for: Moorola Macro-diversiy for he PRACH Discussion/Decision

More information

Making a Faster Cryptanalytic Time-Memory Trade-Off

Making a Faster Cryptanalytic Time-Memory Trade-Off Making a Faser Crypanalyic Time-Memory Trade-Off Philippe Oechslin Laboraoire de Securié e de Crypographie (LASEC) Ecole Polyechnique Fédérale de Lausanne Faculé I&C, 1015 Lausanne, Swizerland philippe.oechslin@epfl.ch

More information

Distributing Human Resources among Software Development Projects 1

Distributing Human Resources among Software Development Projects 1 Disribuing Human Resources among Sofware Developmen Proecs Macario Polo, María Dolores Maeos, Mario Piaini and rancisco Ruiz Summary This paper presens a mehod for esimaing he disribuion of human resources

More information

Analysis of Pricing and Efficiency Control Strategy between Internet Retailer and Conventional Retailer

Analysis of Pricing and Efficiency Control Strategy between Internet Retailer and Conventional Retailer Recen Advances in Business Managemen and Markeing Analysis of Pricing and Efficiency Conrol Sraegy beween Inerne Reailer and Convenional Reailer HYUG RAE CHO 1, SUG MOO BAE and JOG HU PARK 3 Deparmen of

More information

The Transport Equation

The Transport Equation The Transpor Equaion Consider a fluid, flowing wih velociy, V, in a hin sraigh ube whose cross secion will be denoed by A. Suppose he fluid conains a conaminan whose concenraion a posiion a ime will be

More information

Reporting to Management

Reporting to Management CHAPTER 31 Reporing o Managemen Inroducion The success or oherwise of any business underaking depends primarily on earning revenue ha would generae sufficien resources for sound growh. To achieve his objecive,

More information

Multiprocessor Systems-on-Chips

Multiprocessor Systems-on-Chips Par of: Muliprocessor Sysems-on-Chips Edied by: Ahmed Amine Jerraya and Wayne Wolf Morgan Kaufmann Publishers, 2005 2 Modeling Shared Resources Conex swiching implies overhead. On a processing elemen,

More information

Detection of DDoS Attack in SIP Environment with Non-parametric CUSUM Sensor

Detection of DDoS Attack in SIP Environment with Non-parametric CUSUM Sensor Deecion of DDoS Aac in SIP Environmen wih Non-parameric CUSUM Sensor Luigi Alcuri Universiy of Palermo Deparmen of Elecrical, Elecronic and Telecommunicaion Engineering luigi.alcuri@i.unipa.i Piero Cassarà

More information

Time variant processes in failure probability calculations

Time variant processes in failure probability calculations Time varian processes in failure probabiliy calculaions A. Vrouwenvelder (TU-Delf/TNO, The Neherlands) 1. Inroducion Acions on srucures as well as srucural properies are usually no consan, bu will vary

More information

Chapter 8: Regression with Lagged Explanatory Variables

Chapter 8: Regression with Lagged Explanatory Variables Chaper 8: Regression wih Lagged Explanaory Variables Time series daa: Y for =1,..,T End goal: Regression model relaing a dependen variable o explanaory variables. Wih ime series new issues arise: 1. One

More information

Distributed Echo Cancellation in Multimedia Conferencing System

Distributed Echo Cancellation in Multimedia Conferencing System Disribued Echo Cancellaion in Mulimedia Conferencing Sysem Balan Sinniah 1, Sureswaran Ramadass 2 1 KDU College Sdn.Bhd, A Paramoun Corporaion Company, 32, Jalan Anson, 10400 Penang, Malaysia. sbalan@kdupg.edu.my

More information

Research on Inventory Sharing and Pricing Strategy of Multichannel Retailer with Channel Preference in Internet Environment

Research on Inventory Sharing and Pricing Strategy of Multichannel Retailer with Channel Preference in Internet Environment Vol. 7, No. 6 (04), pp. 365-374 hp://dx.doi.org/0.457/ijhi.04.7.6.3 Research on Invenory Sharing and Pricing Sraegy of Mulichannel Reailer wih Channel Preference in Inerne Environmen Hanzong Li College

More information

The Application of Multi Shifts and Break Windows in Employees Scheduling

The Application of Multi Shifts and Break Windows in Employees Scheduling The Applicaion of Muli Shifs and Brea Windows in Employees Scheduling Evy Herowai Indusrial Engineering Deparmen, Universiy of Surabaya, Indonesia Absrac. One mehod for increasing company s performance

More information

Why Did the Demand for Cash Decrease Recently in Korea?

Why Did the Demand for Cash Decrease Recently in Korea? Why Did he Demand for Cash Decrease Recenly in Korea? Byoung Hark Yoo Bank of Korea 26. 5 Absrac We explores why cash demand have decreased recenly in Korea. The raio of cash o consumpion fell o 4.7% in

More information

DOES TRADING VOLUME INFLUENCE GARCH EFFECTS? SOME EVIDENCE FROM THE GREEK MARKET WITH SPECIAL REFERENCE TO BANKING SECTOR

DOES TRADING VOLUME INFLUENCE GARCH EFFECTS? SOME EVIDENCE FROM THE GREEK MARKET WITH SPECIAL REFERENCE TO BANKING SECTOR Invesmen Managemen and Financial Innovaions, Volume 4, Issue 3, 7 33 DOES TRADING VOLUME INFLUENCE GARCH EFFECTS? SOME EVIDENCE FROM THE GREEK MARKET WITH SPECIAL REFERENCE TO BANKING SECTOR Ahanasios

More information

Constant Data Length Retrieval for Video Servers with Variable Bit Rate Streams

Constant Data Length Retrieval for Video Servers with Variable Bit Rate Streams IEEE Inernaional Conference on Mulimedia Compuing & Sysems, June 17-3, 1996, in Hiroshima, Japan, p. 151-155 Consan Lengh Rerieval for Video Servers wih Variable Bi Rae Sreams Erns Biersack, Frédéric Thiesse,

More information

Analogue and Digital Signal Processing. First Term Third Year CS Engineering By Dr Mukhtiar Ali Unar

Analogue and Digital Signal Processing. First Term Third Year CS Engineering By Dr Mukhtiar Ali Unar Analogue and Digial Signal Processing Firs Term Third Year CS Engineering By Dr Mukhiar Ali Unar Recommended Books Haykin S. and Van Veen B.; Signals and Sysems, John Wiley& Sons Inc. ISBN: 0-7-380-7 Ifeachor

More information

Morningstar Investor Return

Morningstar Investor Return Morningsar Invesor Reurn Morningsar Mehodology Paper Augus 31, 2010 2010 Morningsar, Inc. All righs reserved. The informaion in his documen is he propery of Morningsar, Inc. Reproducion or ranscripion

More information

Information Theoretic Evaluation of Change Prediction Models for Large-Scale Software

Information Theoretic Evaluation of Change Prediction Models for Large-Scale Software Informaion Theoreic Evaluaion of Change Predicion Models for Large-Scale Sofware Mina Askari School of Compuer Science Universiy of Waerloo Waerloo, Canada maskari@uwaerloo.ca Ric Hol School of Compuer

More information

Mathematics in Pharmacokinetics What and Why (A second attempt to make it clearer)

Mathematics in Pharmacokinetics What and Why (A second attempt to make it clearer) Mahemaics in Pharmacokineics Wha and Why (A second aemp o make i clearer) We have used equaions for concenraion () as a funcion of ime (). We will coninue o use hese equaions since he plasma concenraions

More information

Option Put-Call Parity Relations When the Underlying Security Pays Dividends

Option Put-Call Parity Relations When the Underlying Security Pays Dividends Inernaional Journal of Business and conomics, 26, Vol. 5, No. 3, 225-23 Opion Pu-all Pariy Relaions When he Underlying Securiy Pays Dividends Weiyu Guo Deparmen of Finance, Universiy of Nebraska Omaha,

More information

An empirical analysis about forecasting Tmall air-conditioning sales using time series model Yan Xia

An empirical analysis about forecasting Tmall air-conditioning sales using time series model Yan Xia An empirical analysis abou forecasing Tmall air-condiioning sales using ime series model Yan Xia Deparmen of Mahemaics, Ocean Universiy of China, China Absrac Time series model is a hospo in he research

More information

Forecasting Malaysian Gold Using. GARCH Model

Forecasting Malaysian Gold Using. GARCH Model Applied Mahemaical Sciences, Vol. 7, 2013, no. 58, 2879-2884 HIKARI Ld, www.m-hikari.com Forecasing Malaysian Gold Using GARCH Model Pung Yean Ping 1, Nor Hamizah Miswan 2 and Maizah Hura Ahmad 3 Deparmen

More information

Relative velocity in one dimension

Relative velocity in one dimension Connexions module: m13618 1 Relaive velociy in one dimension Sunil Kumar Singh This work is produced by The Connexions Projec and licensed under he Creaive Commons Aribuion License Absrac All quaniies

More information

PROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART ONE

PROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART ONE Profi Tes Modelling in Life Assurance Using Spreadshees PROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART ONE Erik Alm Peer Millingon 2004 Profi Tes Modelling in Life Assurance Using Spreadshees

More information

Model-Based Monitoring in Large-Scale Distributed Systems

Model-Based Monitoring in Large-Scale Distributed Systems Model-Based Monioring in Large-Scale Disribued Sysems Diploma Thesis Carsen Reimann Chemniz Universiy of Technology Faculy of Compuer Science Operaing Sysem Group Advisors: Prof. Dr. Winfried Kalfa Dr.

More information

MACROECONOMIC FORECASTS AT THE MOF A LOOK INTO THE REAR VIEW MIRROR

MACROECONOMIC FORECASTS AT THE MOF A LOOK INTO THE REAR VIEW MIRROR MACROECONOMIC FORECASTS AT THE MOF A LOOK INTO THE REAR VIEW MIRROR The firs experimenal publicaion, which summarised pas and expeced fuure developmen of basic economic indicaors, was published by he Minisry

More information

Real-time Particle Filters

Real-time Particle Filters Real-ime Paricle Filers Cody Kwok Dieer Fox Marina Meilă Dep. of Compuer Science & Engineering, Dep. of Saisics Universiy of Washingon Seale, WA 9895 ckwok,fox @cs.washingon.edu, mmp@sa.washingon.edu Absrac

More information

Stochastic Optimal Control Problem for Life Insurance

Stochastic Optimal Control Problem for Life Insurance Sochasic Opimal Conrol Problem for Life Insurance s. Basukh 1, D. Nyamsuren 2 1 Deparmen of Economics and Economerics, Insiue of Finance and Economics, Ulaanbaaar, Mongolia 2 School of Mahemaics, Mongolian

More information

System Performance Improvement By Server Virtualization

System Performance Improvement By Server Virtualization Sysem Performance Improvemen By Server Virualizaion Hioshi Ueno, Tomohide Hasegawa, and Keiichi Yoshihama Absrac Wih he advance of semiconducor echnology, microprocessors become highly inegraed and herefore

More information

11/6/2013. Chapter 14: Dynamic AD-AS. Introduction. Introduction. Keeping track of time. The model s elements

11/6/2013. Chapter 14: Dynamic AD-AS. Introduction. Introduction. Keeping track of time. The model s elements Inroducion Chaper 14: Dynamic D-S dynamic model of aggregae and aggregae supply gives us more insigh ino how he economy works in he shor run. I is a simplified version of a DSGE model, used in cuing-edge

More information

GLAS Team Member Quarterly Report. June , Golden, Colorado (Colorado School of Mines)

GLAS Team Member Quarterly Report. June , Golden, Colorado (Colorado School of Mines) GLAS Team ember Quarerly Repor An Nguyen, Thomas A Herring assachuses Insiue of Technology Period: 04/01/2004 o 06/30//2004 eeings aended Tom Herring aended he eam meeing near GSFC a he end of June, 2004.

More information

Risk Modelling of Collateralised Lending

Risk Modelling of Collateralised Lending Risk Modelling of Collaeralised Lending Dae: 4-11-2008 Number: 8/18 Inroducion This noe explains how i is possible o handle collaeralised lending wihin Risk Conroller. The approach draws on he faciliies

More information

Newton's second law in action

Newton's second law in action Newon's second law in acion In many cases, he naure of he force acing on a body is known I migh depend on ime, posiion, velociy, or some combinaion of hese, bu is dependence is known from experimen In

More information

Renewal processes and Poisson process

Renewal processes and Poisson process CHAPTER 3 Renewal processes and Poisson process 31 Definiion of renewal processes and limi heorems Le ξ 1, ξ 2, be independen and idenically disribued random variables wih P[ξ k > 0] = 1 Define heir parial

More information

Example: scheduling using EDF

Example: scheduling using EDF EDA/DIT6 Real-Time Sysems, Chalmers/GU, 0/0 ecure #4 Updaed February, 0 Real-Time Sysems Specificaion Implemenaion Dynamic scheduling -- Earlies-deadline-firs scheduling Processor-demand analysis Verificaion

More information

Time-Expanded Sampling (TES) For Ensemble-based Data Assimilation Applied To Conventional And Satellite Observations

Time-Expanded Sampling (TES) For Ensemble-based Data Assimilation Applied To Conventional And Satellite Observations 27 h WAF/23 rd NWP, 29 June 3 July 2015, Chicago IL. 1 Time-Expanded Sampling (TES) For Ensemble-based Daa Assimilaion Applied To Convenional And Saellie Observaions Allen Zhao 1, Qin Xu 2, Yi Jin 1, Jusin

More information

Idealistic characteristics of Islamic Azad University masters - Islamshahr Branch from Students Perspective

Idealistic characteristics of Islamic Azad University masters - Islamshahr Branch from Students Perspective Available online a www.pelagiaresearchlibrary.com European Journal Experimenal Biology, 202, 2 (5):88789 ISSN: 2248 925 CODEN (USA): EJEBAU Idealisic characerisics Islamic Azad Universiy masers Islamshahr

More information

Understanding Sequential Circuit Timing

Understanding Sequential Circuit Timing ENGIN112: Inroducion o Elecrical and Compuer Engineering Fall 2003 Prof. Russell Tessier Undersanding Sequenial Circui Timing Perhaps he wo mos disinguishing characerisics of a compuer are is processor

More information

Process Optimization Time for a Service in 4G Network by SNMP Monitoring and IAAS Cloud Computing

Process Optimization Time for a Service in 4G Network by SNMP Monitoring and IAAS Cloud Computing Process Opimizaion Time for a Service in 4G Nework by SNMP Monioring and IAAS Cloud Compuing Yassine El Mahoi Laboraory of Compuer Science, Operaions Research and Applied Saisics. Téouan, Morocco Souad

More information

Improvement of a TCP Incast Avoidance Method for Data Center Networks

Improvement of a TCP Incast Avoidance Method for Data Center Networks Improvemen of a Incas Avoidance Mehod for Daa Cener Neworks Kazuoshi Kajia, Shigeyuki Osada, Yukinobu Fukushima and Tokumi Yokohira The Graduae School of Naural Science and Technology, Okayama Universiy

More information

An Agent-based Bayesian Forecasting Model for Enhanced Network Security

An Agent-based Bayesian Forecasting Model for Enhanced Network Security An Agen-based Forecasing Model for Enhanced Nework Securiy J. PIKOULAS, W.J. BUCHANAN, Napier Universiy, Edinburgh, UK. M. MANNION, Glasgow Caledonian Universiy, Glasgow, UK. K. TRIANTAFYLLOPOULOS, Universiy

More information

CHARGE AND DISCHARGE OF A CAPACITOR

CHARGE AND DISCHARGE OF A CAPACITOR REFERENCES RC Circuis: Elecrical Insrumens: Mos Inroducory Physics exs (e.g. A. Halliday and Resnick, Physics ; M. Sernheim and J. Kane, General Physics.) This Laboraory Manual: Commonly Used Insrumens:

More information

Predicting Stock Market Index Trading Signals Using Neural Networks

Predicting Stock Market Index Trading Signals Using Neural Networks Predicing Sock Marke Index Trading Using Neural Neworks C. D. Tilakarane, S. A. Morris, M. A. Mammadov, C. P. Hurs Cenre for Informaics and Applied Opimizaion School of Informaion Technology and Mahemaical

More information

Efficient One-time Signature Schemes for Stream Authentication *

Efficient One-time Signature Schemes for Stream Authentication * JOURNAL OF INFORMATION SCIENCE AND ENGINEERING, 611-64 (006) Efficien One-ime Signaure Schemes for Sream Auhenicaion * YONGSU PARK AND YOOKUN CHO + College of Informaion and Communicaions Hanyang Universiy

More information

Economics 140A Hypothesis Testing in Regression Models

Economics 140A Hypothesis Testing in Regression Models Economics 140A Hypohesis Tesing in Regression Models While i is algebraically simple o work wih a populaion model wih a single varying regressor, mos populaion models have muliple varying regressors 1

More information

Task-Execution Scheduling Schemes for Network Measurement and Monitoring

Task-Execution Scheduling Schemes for Network Measurement and Monitoring Task-Execuion Scheduling Schemes for Nework Measuremen and Monioring Zhen Qin, Robero Rojas-Cessa, and Nirwan Ansari Deparmen of Elecrical and Compuer Engineering New Jersey Insiue of Technology Universiy

More information

SELF-EVALUATION FOR VIDEO TRACKING SYSTEMS

SELF-EVALUATION FOR VIDEO TRACKING SYSTEMS SELF-EVALUATION FOR VIDEO TRACKING SYSTEMS Hao Wu and Qinfen Zheng Cenre for Auomaion Research Dep. of Elecrical and Compuer Engineering Universiy of Maryland, College Park, MD-20742 {wh2003, qinfen}@cfar.umd.edu

More information

Network Discovery: An Estimation Based Approach

Network Discovery: An Estimation Based Approach Nework Discovery: An Esimaion Based Approach Girish Chowdhary, Magnus Egersed, and Eric N. Johnson Absrac We consider he unaddressed problem of nework discovery, in which, an agen aemps o formulae an esimae

More information

Chapter 7: Estimating the Variance of an Estimate s Probability Distribution

Chapter 7: Estimating the Variance of an Estimate s Probability Distribution Chaper 7: Esimaing he Variance of an Esimae s Probabiliy Disribuion Chaper 7 Ouline Review o Clin s Assignmen o General Properies of he Ordinary Leas Squares (OLS) Esimaion Procedure o Imporance of he

More information

The Architecture of a Churn Prediction System Based on Stream Mining

The Architecture of a Churn Prediction System Based on Stream Mining The Archiecure of a Churn Predicion Sysem Based on Sream Mining Borja Balle a, Bernardino Casas a, Alex Caarineu a, Ricard Gavaldà a, David Manzano-Macho b a Universia Poliècnica de Caalunya - BarcelonaTech.

More information

Lecture III: Finish Discounted Value Formulation

Lecture III: Finish Discounted Value Formulation Lecure III: Finish Discouned Value Formulaion I. Inernal Rae of Reurn A. Formally defined: Inernal Rae of Reurn is ha ineres rae which reduces he ne presen value of an invesmen o zero.. Finding he inernal

More information

PROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART TWO

PROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART TWO Profi Tes Modelling in Life Assurance Using Spreadshees, par wo PROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART TWO Erik Alm Peer Millingon Profi Tes Modelling in Life Assurance Using Spreadshees,

More information

PRACTICES AND ISSUES IN OPERATIONAL RISK MODELING UNDER BASEL II

PRACTICES AND ISSUES IN OPERATIONAL RISK MODELING UNDER BASEL II Lihuanian Mahemaical Journal, Vol. 51, No. 2, April, 2011, pp. 180 193 PRACTICES AND ISSUES IN OPERATIONAL RISK MODELING UNDER BASEL II Paul Embrechs and Marius Hofer 1 RiskLab, Deparmen of Mahemaics,

More information

Individual Health Insurance April 30, 2008 Pages 167-170

Individual Health Insurance April 30, 2008 Pages 167-170 Individual Healh Insurance April 30, 2008 Pages 167-170 We have received feedback ha his secion of he e is confusing because some of he defined noaion is inconsisen wih comparable life insurance reserve

More information

Caring for trees and your service

Caring for trees and your service Caring for rees and your service Line clearing helps preven ouages FPL is commied o delivering safe, reliable elecric service o our cusomers. Trees, especially palm rees, can inerfere wih power lines and

More information

Evolutionary building of stock trading experts in real-time systems

Evolutionary building of stock trading experts in real-time systems Evoluionary building of sock rading expers in real-ime sysems Jerzy J. Korczak Universié Louis Paseur Srasbourg, France Email: jjk@dp-info.u-srasbg.fr Absrac: This paper addresses he problem of consrucing

More information

A Robust Exponentially Weighted Moving Average Control Chart for the Process Mean

A Robust Exponentially Weighted Moving Average Control Chart for the Process Mean Journal of Modern Applied Saisical Mehods Volume 5 Issue Aricle --005 A Robus Exponenially Weighed Moving Average Conrol Char for he Process Mean Michael B. C. Khoo Universii Sains, Malaysia, mkbc@usm.my

More information

INVESTIGATION OF THE INFLUENCE OF UNEMPLOYMENT ON ECONOMIC INDICATORS

INVESTIGATION OF THE INFLUENCE OF UNEMPLOYMENT ON ECONOMIC INDICATORS INVESTIGATION OF THE INFLUENCE OF UNEMPLOYMENT ON ECONOMIC INDICATORS Ilona Tregub, Olga Filina, Irina Kondakova Financial Universiy under he Governmen of he Russian Federaion 1. Phillips curve In economics,

More information

SPEC model selection algorithm for ARCH models: an options pricing evaluation framework

SPEC model selection algorithm for ARCH models: an options pricing evaluation framework Applied Financial Economics Leers, 2008, 4, 419 423 SEC model selecion algorihm for ARCH models: an opions pricing evaluaion framework Savros Degiannakis a, * and Evdokia Xekalaki a,b a Deparmen of Saisics,

More information

Load Prediction Using Hybrid Model for Computational Grid

Load Prediction Using Hybrid Model for Computational Grid Load Predicion Using Hybrid Model for Compuaional Grid Yongwei Wu, Yulai Yuan, Guangwen Yang 3, Weimin Zheng 4 Deparmen of Compuer Science and Technology, Tsinghua Universiy, Beijing 00084, China, 3, 4

More information

Cointegration: The Engle and Granger approach

Cointegration: The Engle and Granger approach Coinegraion: The Engle and Granger approach Inroducion Generally one would find mos of he economic variables o be non-saionary I(1) variables. Hence, any equilibrium heories ha involve hese variables require

More information

Why Do Real and Nominal. Inventory-Sales Ratios Have Different Trends?

Why Do Real and Nominal. Inventory-Sales Ratios Have Different Trends? Why Do Real and Nominal Invenory-Sales Raios Have Differen Trends? By Valerie A. Ramey Professor of Economics Deparmen of Economics Universiy of California, San Diego and Research Associae Naional Bureau

More information

Impact of Debt on Primary Deficit and GSDP Gap in Odisha: Empirical Evidences

Impact of Debt on Primary Deficit and GSDP Gap in Odisha: Empirical Evidences S.R. No. 002 10/2015/CEFT Impac of Deb on Primary Defici and GSDP Gap in Odisha: Empirical Evidences 1. Inroducion The excessive pressure of public expendiure over is revenue receip is financed hrough

More information

The Grantor Retained Annuity Trust (GRAT)

The Grantor Retained Annuity Trust (GRAT) WEALTH ADVISORY Esae Planning Sraegies for closely-held, family businesses The Granor Reained Annuiy Trus (GRAT) An efficien wealh ransfer sraegy, paricularly in a low ineres rae environmen Family business

More information

The Real Business Cycle paradigm. The RBC model emphasizes supply (technology) disturbances as the main source of

The Real Business Cycle paradigm. The RBC model emphasizes supply (technology) disturbances as the main source of Prof. Harris Dellas Advanced Macroeconomics Winer 2001/01 The Real Business Cycle paradigm The RBC model emphasizes supply (echnology) disurbances as he main source of macroeconomic flucuaions in a world

More information

Hedging with Forwards and Futures

Hedging with Forwards and Futures Hedging wih orwards and uures Hedging in mos cases is sraighforward. You plan o buy 10,000 barrels of oil in six monhs and you wish o eliminae he price risk. If you ake he buy-side of a forward/fuures

More information

Random Walk in 1-D. 3 possible paths x vs n. -5 For our random walk, we assume the probabilities p,q do not depend on time (n) - stationary

Random Walk in 1-D. 3 possible paths x vs n. -5 For our random walk, we assume the probabilities p,q do not depend on time (n) - stationary Random Walk in -D Random walks appear in many cones: diffusion is a random walk process undersanding buffering, waiing imes, queuing more generally he heory of sochasic processes gambling choosing he bes

More information

The Greek financial crisis: growing imbalances and sovereign spreads. Heather D. Gibson, Stephan G. Hall and George S. Tavlas

The Greek financial crisis: growing imbalances and sovereign spreads. Heather D. Gibson, Stephan G. Hall and George S. Tavlas The Greek financial crisis: growing imbalances and sovereign spreads Heaher D. Gibson, Sephan G. Hall and George S. Tavlas The enry The enry of Greece ino he Eurozone in 2001 produced a dividend in he

More information

Secure Election Infrastructures Based on IPv6 Clouds

Secure Election Infrastructures Based on IPv6 Clouds Secure Elecion Infrasrucures Based on IPv6 Clouds Firs IPv6-only OpenSack Cloud used o deliver producion services is de-ployed by Nephos6, Cikomm and SnT-Universiy of Luxembourg. Laif Ladid, Presiden,

More information

Mortality Variance of the Present Value (PV) of Future Annuity Payments

Mortality Variance of the Present Value (PV) of Future Annuity Payments Morali Variance of he Presen Value (PV) of Fuure Annui Pamens Frank Y. Kang, Ph.D. Research Anals a Frank Russell Compan Absrac The variance of he presen value of fuure annui pamens plas an imporan role

More information

II.1. Debt reduction and fiscal multipliers. dbt da dpbal da dg. bal

II.1. Debt reduction and fiscal multipliers. dbt da dpbal da dg. bal Quarerly Repor on he Euro Area 3/202 II.. Deb reducion and fiscal mulipliers The deerioraion of public finances in he firs years of he crisis has led mos Member Saes o adop sizeable consolidaion packages.

More information

Fourier Series Solution of the Heat Equation

Fourier Series Solution of the Heat Equation Fourier Series Soluion of he Hea Equaion Physical Applicaion; he Hea Equaion In he early nineeenh cenury Joseph Fourier, a French scienis and mahemaician who had accompanied Napoleon on his Egypian campaign,

More information

Stock Trading with Recurrent Reinforcement Learning (RRL) CS229 Application Project Gabriel Molina, SUID 5055783

Stock Trading with Recurrent Reinforcement Learning (RRL) CS229 Application Project Gabriel Molina, SUID 5055783 Sock raing wih Recurren Reinforcemen Learning (RRL) CS9 Applicaion Projec Gabriel Molina, SUID 555783 I. INRODUCION One relaively new approach o financial raing is o use machine learning algorihms o preic

More information

Botnet Detection by Monitoring Group Activities in DNS Traffic

Botnet Detection by Monitoring Group Activities in DNS Traffic Bone Deecion by Monioring Group Aciviies in DNS Traffic Hyunsang Choi, Hanwoo Lee, Heejo Lee, Hyogon Kim Korea Universiy {realchs, hanwoo, heejo, hyogon}@orea.ac.r Absrac Recen malicious aemps are inended

More information

Spectrum-Aware Data Replication in Intermittently Connected Cognitive Radio Networks

Spectrum-Aware Data Replication in Intermittently Connected Cognitive Radio Networks Specrum-Aware Daa Replicaion in Inermienly Conneced Cogniive Radio Neworks Absrac The opening of under-uilized specrum creaes an opporuniy for unlicensed users o achieve subsanial performance improvemen

More information

Graphing the Von Bertalanffy Growth Equation

Graphing the Von Bertalanffy Growth Equation file: d:\b173-2013\von_beralanffy.wpd dae: Sepember 23, 2013 Inroducion Graphing he Von Beralanffy Growh Equaion Previously, we calculaed regressions of TL on SL for fish size daa and ploed he daa and

More information

Research Question Is the average body temperature of healthy adults 98.6 F? Introduction to Hypothesis Testing. Statistical Hypothesis

Research Question Is the average body temperature of healthy adults 98.6 F? Introduction to Hypothesis Testing. Statistical Hypothesis Inroducion o Hypohesis Tesing Research Quesion Is he average body emperaure of healhy aduls 98.6 F? HT - 1 HT - 2 Scienific Mehod 1. Sae research hypoheses or quesions. µ = 98.6? 2. Gaher daa or evidence

More information

Stability. Coefficients may change over time. Evolution of the economy Policy changes

Stability. Coefficients may change over time. Evolution of the economy Policy changes Sabiliy Coefficiens may change over ime Evoluion of he economy Policy changes Time Varying Parameers y = α + x β + Coefficiens depend on he ime period If he coefficiens vary randomly and are unpredicable,

More information

Lecture 18. Serial correlation: testing and estimation. Testing for serial correlation

Lecture 18. Serial correlation: testing and estimation. Testing for serial correlation Lecure 8. Serial correlaion: esing and esimaion Tesing for serial correlaion In lecure 6 we used graphical mehods o look for serial/auocorrelaion in he random error erm u. Because we canno observe he u

More information

An Online Learning-based Framework for Tracking

An Online Learning-based Framework for Tracking An Online Learning-based Framework for Tracking Kamalika Chaudhuri Compuer Science and Engineering Universiy of California, San Diego La Jolla, CA 9293 Yoav Freund Compuer Science and Engineering Universiy

More information