Journal of Computer Science and Information Security (IJCSIS ISSN 1947-5500) is an open access, international, peer-reviewed, scholarly journal with a focused aim of promoting and publishing original high quality research dealing with theoretical and scientific aspects in all disciplines of Computing and Information Security. The journal is published monthly, and articles are accepted for review on a continual basis. Papers that can provide both theoretical analysis, along with carefully designed computational experiments, are particularly welcome. IJCSIS editorial board consists of several internationally recognized experts and guest editors. Wide circulation is assured because libraries and individuals, worldwide, subscribe and reference to IJCSIS. The Journal has grown rapidly to its currently level of over 1,100 articles published and indexed; with distribution to librarians, universities, research centers, researchers in computing, and computer scientists. Other field coverage includes: security infrastructures, network security: Internet security, content protection, cryptography, steganography and formal methods in information security; multimedia systems, software, information systems, intelligent systems, web services, data mining, wireless communication, networking and technologies, innovation technology and management. (See monthly Call for Papers) Since 2009, IJCSIS is published using an open access publication model, meaning that all interested readers will be able to freely access the journal online without the need for a subscription. We wish to make IJCSIS a first-tier journal in Computer science field, with strong impact factor. On behalf of the Editorial Board and the IJCSIS members, we would like to express our gratitude to all authors and reviewers for their sustained support. The acceptance rate for this issue is 32%. I am confident that the readers of this journal will explore new avenues of research and academic excellence.
(IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 7, July 2011 Recovery function of Components of Additive Model of Biometric System Reliability in UML Bihać, Bosnia and Hercegovina Zoran Ćosić(Author) firstname.lastname@example.org director Statheros d.o.o. Kaštel Stari, Croatia Miroslav Bača (Author) email@example.com professor Faculty of Organisational and Informational science Varaždin, Croatia Jasmin Ćosić (Author) firstname.lastname@example.org IT Section of Police Administration Ministry of Interior of Una-sana canton number of successful tasks and the total number of tasks in the time specified for the operation of the system: Abstract- Approaches The development of biometric systems is undoubtedly on the rise in the number and the application areas. n1 (t ) Modelling of system reliability and system data analysis after R(t ) failure and the time of re-establishing the operating regime is of n (t ) (1) crucial importance for users of the system and also for producers of certain components. This paper gives an overview of the mathematical model of biometric system function recovery and its where :R(t ) - assessment of reliability, application through the UML model. n1 (t ) - number of successful assignments in time t, Keywords- Additive model, Biometric system, reliability, recovery n (t ) - total number of tasks performed in time t, function, UML, component, t - time specified for the operation of the system. I. INTRODUCTION The value R(t ) represents the estimated reliability due to the Many models of reliability of biometric systems are applicable only to specific parts or components of that same system. For fact that the number of tasks n(t) is final. Therefore, the actual more complex considerations must be taken into account reliability R(t) is obtained when the number of tasks n(t) tends models based on Markov processes that consider the reliability to infinity. of the system as a whole, which includes components of the system. In this paper the approach to restoring the functions of R(t ) lim R t a biometric system that had failure at some of its components is n ( t ) (2) elaborated. The basic model is an additive model which R(t) =1–F(t)=P(T>t) (3) assumes a serial dependence between the components  (Xie & Wohlin). Where the R ( t ) 1indicates the reliability function. Thus F ( t ) UML is also becoming standard in the process of system can be called non reliability function. Approximate form of the design so the manufacture of component systems greatly function F ( t ), is shown in Figure 1. It is a continuous and benefits from the UML view. The authors introduce the monotonically increasing function: concept of UML modelling in the process of restoring function analysis of biometric systems. The paper defines the conceptual F(0)=0 class diagram in UML, which provides a framework for F ( t ) →1, when t → ∞ analyzing the function recovery of biometric systems. Density failure function is marked with F( t ), and from II. ADDITIVE RELIABILITY MODEL probability theory we know that: Reliability  as the probability  (number between 0 and 1 or 0% and 100%) can be represented as a ratio between the 1 R ( t ) is function of reliability 1 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 7, July 2011 dF (t ) G (t ) P ( > t) = 1 F1 (t ) (10) f (t ) dt (4) where F(t) is probability distribution function. Failure intensity , , λ ( t ) represents the density of F1 (t ) is the probability density function of conditional probability of failure at time t provided that until Refresh frequency that moment there was no failure. random variable : f (t ) dF1 (t ) dG (t ) (t ) f1 (t ) (11) R (t ) (5) dt dt Or according to the model of Xie and Wohlin: From here it follows that: d (t ) (t ) ,t 0 t dt (6) F1 (t ) f1 (t ) dt (12) where µ(t) is mean value of the expected system failure. 0 It is also assumed that the intensity of the failure of the entire t system is the sum of the intensity of failures of its components: G (t ) 1 f1 (t ) dt (13) (7) 0 So it follows that the expectations of failure of the system are (6): B. Intensity of recovery function (8) (t ) is the conditional probability density function2 of completion of recovery of components (repair) within time t, provided that recovery is not completed until the moment t. III. BIOMETRIC SYSTEM RECOVERY FUNCTION Intensity recovery function is conditional probability density Term recovery consider biometric system as a system that is function of the end of the recovery in time t, provided that recovery maintained after a long period of use or recovered after failure of not complete until that moment t, we have: is particular components. Biometric system components, after the failure, are maintained or exchanged and then continue to be part of the system. When considering the reliability problems of generic G(t ) F1 (t ) f1 (t ) biometric system along with a random event that includes the (t ) (14) G (t ) G (t ) G (t ) appearance of failure within the system, it is necessary to consider other random event and that is recovery the system after failure. To this event corresponds a new random variable that indicates t t dG (t ) (t )dt G (t ) (15) the time of recovery. As a characteristic of random variable 0 0 indicators similar to those being considered for the analysis of time t without failure are used. ln G (t ) t0 (t )dt (16) 0 t A. Distribution recovery function, refresh frequency ( t ) dt 0 G (t ) e (17) is a random variable ,  which marks the time of recovery t of the components in failure, then the probability of recovery is as a ( t ) dt function of time: F1 (t)= 1- e 0 (18) P ( < t ) F1 (t ) (9) F 1 (t) probability distribution function of random variable . The probability of non-recovery G(t) is defined as: 2 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 7, July 2011 Condition 1 represents a functional system and condition 2 represents a system that has been repaired after a failure. C. Time recovery function The transition of system from condition 1 to 2 is represented a. Mean recovery time with failure intensity function λ, the transition from condition 2 to condition 1 is defined with recovery intensity function µ. Mean time of recovery M ( ) is the mathematical expectation of IV. RECOVERY FUNCTION OF BIOMETRIC SYSTEM IN UML random variable whose probability density function is f1 (t ) , A. Generalized biometric system i.e. Generalized biometric system model, ,  is a schematic view M ( ) tf1 (t ) dt (19) of Wyman biometric system model that depicts serial dependence 0 of a system components and can be summarized, in this exploitation period of time, as shown on Figure 2. M ( ) G (t )dt (20) 0 b. Recovery time variance Recovery time variance 02 is characterized by deviation of Figure 2 duration of recovery from his mean recovery time. The system shown in Figure 2 works in time t0 without failure. After the failure the system is recovered in time t1, after recovery 2 occurs time period of re-operation t2. 02 V E E ( ) 2 tG (t ) dt G (t )dt 2 Parameter which defines the conditions created by failure is 0 0 intensity of failure of particular component . (21) The intensity of the component failure can be expressed as: c. Availability of system after recovery time 1 1 Probability , that the system after time t will be available for EL (23) functioning is the expression (10). n n Where is: Where intensity recovery function µ can be defined as: n- number of correct parts of the confidence interval (1 ) 0, 75 (22) - lower limit of confidence for the mean time between failures. Recovery time of the system is the function of the recovery MTTR –mean time to repair intensity as described by the expression (22). The process of transition from the state of failure to the state of availability can be represented as in Figure 1: B. The conceptual class-diagram model of system recovery During the study  of the problem of reliability of generic biometric system, object-relational approach of description of the problem provides easier and clearer description of the sequence analysis of events within the system during the verification of Figure 1 failure. Figure 3 shows the diagram of classes of the recovery of biometric system model: 3 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 7, July 2011 REFERENCES  An additive reliability model for the modular software failure data – M.Xie, C.Wohlin - 2007  Teorija pouzdanosti tehničkih sistema, Vojnoizdavački novinski centar, Beograd 2005,  Pouzdanost brodskih sustava – Ante Bukša, Ivica Šegulja – Pomorstvo - 2008  Pouzdanost tehničkog sustava brodskog kompresora – Zoran Ćosić – magistarski rad - 2007  Eksploatacija i razvitak telekomunikacijskog sustava, Mr.sc Juraj Buzolić , Split 2006  Zasnivanje otvorene ontologije odabranih segmenata biometrijske znanosti - Markus Schatten– Magistarski rad – FOI 2007 Figure 3  Early reliability assessment of UML based software models – Vittorio Cortellessa, Harshinder Singh, Bojan Cukic – WOSP’02 , July 24-26, 2002 Rome Italy Class Biometric system is a set of components of that system  Modelling biometric systems in UML – Miroslav Bača, Markus and is in relation to class Failure which contains data on the Schatten, Bernardo Golenja, JIOS 2007 FOI Varaždin Component in failure, time of occurrence of failure and failure  Reliability, Availability and Maintainability in Biometric Applications– intensity. © 2003-2007 Optimum Biometric Labs A WHITE PAPER Version r1.0, Class Recovery is in relation to class Biometrical system Date of release: January 2, 2008, SWEDEN because it contains information about the component, the time AUTHORS PROFILE of recovery of component and calculated recovery intensity of component. Class Recovery is in relation to the class Zoran Ćosić, CEO at Statheros ltd, and business consultant in business process standardization field. He received BEng degree at Faculty of nautical Availability, which is a function of data on failure intensity and science , Split (HR) in 1990, MSc degree at Faculty of nautical science , the recovery intensity, with the class Mean time which contains Split (HR) in 2007 , actually he is a PhD candidate at Faculty of data of recovery start time, duration and results of recovery, informational and Organisational science Varaždin Croatia. He is a member of various professional societies and program with the class Recovery intensity. Furthermore it is possible, at committee members. He is author or co- the level of class diagrams to present and other factors of author more than 20 scientific and professional papers. His main reliability and facilitate access to their prediction based on fields of interest are: Informational security, biometrics and privacy, historical data (logs) of the system functioning. business process reingeenering, Jasmin Ćosić has received his BE (Economics) degree from University of V. CONCLUSION AND FURTHER RESEARCH Bihać, B&H in 1997. He completed his study in Information Technology field (dipl.ing.Information Technlogy) in Mostar, University of Džemal Information about the system failure must be considered in the Bijedić, B&H. Currently he is PhD candidate in Faculty of Organization context of the whole biometric system and its performance in and Informatics in Varaždin, University of Zagreb, Croatia. He is working in Ministry of the Interior of Una-sana canton, B&H. He is a time. ICT Expert Witness, and is a member of Association of Informatics of In accordance with the above information on the exploitation B&H, Member of IEEE and ACM. His areas of interests are Digital of biometric systems must be part of a comprehensive analysis Forensic, Computer Crime, Information Security and DBM Systems. He of the functioning and also information on recovery of the has presented and published over 20 conference proceedings and journal articles in his research area system and its functionality at any given time. The time to put Miroslav Bača is currently an Associate professor, University of Zagreb, the system into operation condition is often placed in clearly Faculty of Organization and Informatics. He is defined time frames that are stipulated in contracts or SLA a member of various professional societies and program addenda to the contract. The parameters monitoring processes committee members, and he is reviewer of several international journals and conferences. He is also the head of the Biometrics centre in associated with the reliability of the system are often Varaždin, Croatia. He is author or co- complicated and laborious so UML approach to description of author more than 70 scientific and professional papers and two books. problem simplifies the same. UML also imposes as general or His main research fields are computer forensics, biometrics and privacy universal standard for descriptions of appearance. professor at Faculty of informational and Organisational science Varaždin Croatia Further work of the authors will be directed toward specialization of model taking into consideration the other models of reliability dependence and different system failure probability distributions. 4 http://sites.google.com/site/ijcsis/ ISSN 1947-5500
Pages to are hidden for
"function of Components of Additive Model of Biometric System Reliability in UML"Please download to view full document