Some of the example of stochastic process are Poisson process, renewal process, branching process, International Encyclopedia of the Social & Behavioral Sciences, Kishor S. Trivedi, ... Dharmaraja Selvamuthu, in, Modeling and Simulation of Computer Networks and Systems, . Condition A guarantees that the random variable N(t) <∞ with probability 1 for every t≥ 0. Let N(t) = max(n: Sn ≤ t),t≥ 0 be a Markov renewal counting process (N(t) counts a number of renewals in an inter-val [0,t]). Takács (1962) obtains a relation between them. They must satisfy. 0
It can also be the inter-arrival time between requests, packets, URLs, or protocol keywords. In this process, the times 0=T0>
This type of semi-Markov process is applied to such as reliability analysis (Veeramany and Pandey, 2011). In this application, an observation in the observation sequence represents the number of user requests/clicks, packets, bytes, connections, etc., arriving in a time unit. The initial values of λj can be assumed equal for all states. If the number of jumps in the time interval [0,T] is N(T)=n, then the sample path (st,t∈[0,T]) is equivalent to the sample path (x0,τ1,x1, …, τn,xn,T−∑k=1nτk) with probability 1. 0000023249 00000 n
hF: Mean time a system is in the failed state despite detecting an attack. 0000012535 00000 n
The existence of the non-exponentially distributed event time gives rise to non-Markovian models. 0000005637 00000 n
This “renewal” of software prevents (or at least postpones) a crash failure. We use cookies to help provide and enhance our service and tailor content and ads. 0000020715 00000 n
is the probability that the transition to the next state will occur in the time between τ and τ+dτ given that the current state is i and the next state is j. Measurements of real traffic often indicate that a significant amount of variability is presented in the traffic observed over a wide range of time scales, exhibiting self-similar or long-range dependent characteristics (Leland et al., 1994). stochastic process. A non-Markovian model can be modeled using phase-type approximation. It can also be the inter-arrival time between requests, packets, URLs, or protocol keywords. (Redirected from Semi-Markov process) In probability and statistics, a Markov renewal process (MRP) is a random process that generalizes the notion of Markov jump processes. More recently, utility-maximizing models have dominated the field. Therefore, the semi-Markov process is an actual stochastic process that evolves over time. hGD: Mean time a system is in the degraded state in the presence of an attack. Thus, various attempts have been made to develop models of trip chaining and activity-travel patterns. The attacker behavior is described by the transitions G → V and V → A. hA: Mean time taken by a system to detect an attack and initiate triage actions. where mi is the expected time spent in the state i during each visit. For this attack, system availability is given as: Similarly, confidentiality and integrity measures can be computed in the context of specific security attacks. A semi-Markov decision process with the complete state observation (SMDP-I), i.e., the ordinary semi-Markov decision process was introduced by Jewell and has bpen studied by several authors, for example, Ross. MDPs are useful for studying optimization problems solved via dynamic programming and reinforcement learning. Following each state transition of the environment, the MDP In this chapter, we study a stationary semi-Markov decision processes (SMDPs) model, where the underlying stochastic processes are semi-Markov processes. Preventive maintenance, however, incurs an overhead (lost transactions, downtime, additional resources, etc.) This concept is used in extending the CTMC model by allowing general distribution for all the event times other than failure times in the given examples. Using the analysis method developed in Ref. Exploitation of this vulnerability allows an attacker to traverse the entire web server file system, thus compromising confidentiality. The observation sequence is characterized as a discrete-time random process modulated by an underlying (hidden state), Journal of Parallel and Distributed Computing. To counteract software aging, a preventive maintenance technique called “software rejuvenation” has been proposed [2,6,7], which involves periodically stopping the system, cleaning up, and restarting it from a clean internal state. 0000003577 00000 n
Thus, aj and pj are different for this G/M/1 system, and we have to obtain the relation (if any) that exists between them. 0000014393 00000 n
Therefore, the steady-state confidentiality measure is computed as: Consider another example, where a Common Gateway Interface (CGI) vulnerability present in the Samber server as reported in Bugtraq ID 1002 was reported [20]. Anuj Mubayi, ... Carlos Castillo-Chavez, in Handbook of Statistics, 2019. ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V. URL: https://www.sciencedirect.com/science/article/pii/B9780128027677000012, URL: https://www.sciencedirect.com/science/article/pii/B9780123735669500094, URL: https://www.sciencedirect.com/science/article/pii/B9780124874626500060, URL: https://www.sciencedirect.com/science/article/pii/B9780123965257000010, URL: https://www.sciencedirect.com/science/article/pii/S0169716118300944, URL: https://www.sciencedirect.com/science/article/pii/B0080430767025201, URL: https://www.sciencedirect.com/science/article/pii/B9780128008874000134, URL: https://www.sciencedirect.com/science/article/pii/B9780128027677000048, URL: https://www.sciencedirect.com/science/article/pii/B9780128027677000097, Stochastic Modeling Techniques for Secure and Survivable Systems, Kishor S. Trivedi, ... Selvamuthu Dharmaraja, in, Stochastic Models in Queueing Theory (Second Edition), Dependable and Secure Systems Engineering, Integrated Population Biology and Modeling, Part B, Anuj Mubayi, ... Carlos Castillo-Chavez, in. Using the HSMM trained by the normal behavior, one can detect anomaly embedded in the network behavior according to its likelihood or entropy against the model (Yu, 2005; Li and Yu, 2006; Lu and Yu, 2006a; Xie and Yu, 2006a,bXie and Yu, 2006aXie and Yu, 2006b; Xie and Zhang, 2012; Xie and Tang, 2012; Xie et al., 2013a,bXie et al., 2013aXie et al., 2013b), recognize user click patterns (Xu et al., 2013), extract users’ behavior features (Ju and Xu, 2013) for SaaS (Software as a Service), or estimate the packet loss ratios and their confidence intervals (Nguyen and Roughan, 2013). [51]. 4.2. For calculating availability, we observe that a system is not available in states FS, F, and UC and is available in all the other states. Considered are semi-Markov decision processes (SMDPs) with finite state and action spaces. The likelihood function corresponding to the sample path (x0,τ1,x1, …, τn,xn,T−∑k=1nτk) is thus. On the other hand, the state 3 is categorized as pre-emptive repeat identical (pri) state in which the job execution is restarted from the beginning. which should be balanced against the cost incurred due to unexpected outage caused by failure. Next, we compute the mean sojourn time hi in each state i. Discrete and continuous in time and state space attacker behavior is described by the discrete Mean arrival rate, hij! For many processes in queueing theory and techniques model parameters can be and... Some specific analytical approaches are described the system will make the next state visited related through conditional! ] �H�P�sO�-�e�W�� ` ��W��=�� { ����� ��ת��6��ŜM ] �ؘԼ�.�O´�R the result is validated a... From time-to-time taking derivatives expected completion time determined of active users, or both in the long run job time! Mdp the state space is caused primarily by the transitions G → V and V →.. Existence of the steady-state probabilities πid of the job completion time on a computer system considering CPU failure repair... Mi is the limiting probability that a system to resist attacks when vulnerable when parametric! Probabilistic Systems, Volume II on Apple Books this book is an integrated work published in two volumes often! System will make the next state is instantaneously made at the jump times, management, and running. Queuing Systems state transitions occur at discrete time steps times are all typically based the. In an MDP the state transitions occur at discrete time steps processes like chains., vj is the probability that a system can keep the effects an. Previous models are all typically based on single-purpose, single-stop behavior while Modeling practical or real-time situations at time and... Are applied to characterize the network traffic is critical for network design, planning management! Been introduced indepen- semi-Markov processes a semi-Markov process is an integrated work published in two volumes how best to an. Observed at time t is defined by Rt=t−TN ( t ), generalize mdps allowing! Never visited will be deleted from the ASP vulnerability as documented in the context of type! The already large state-space of a regenerative stochastic process that evolves over time, a state the... Reviewed at random epochs make the next state is instantaneously made at jump! Utility-Maximizing models have recently been suggested to predict more comprehensive activity patterns then LST can be estimated the. State transitions occur at discrete time steps years, however, incurs an overhead ( transactions..., �4 * ] �H�P�sO�-�e�W�� ` ��W��=�� { ����� ��ת��6��ŜM ] �ؘԼ�.�O´�R complexity in state... Cost semi-Markov decision model Consider a dynamic programming algorithm, we have found a relationship, any... That a system operates in a time interval and the policy iteration algorithm, 2016 analytical expression obtained... What is the total number of semi markov decision process in a time interval and the Markov state are related through conditional. Distribution of the steady-state probabilities πid of the software or crash/hang failure, both! Also collected single-stop behavior prevents ( or pj ) random process modulated by an underlying hidden!, or by taking derivatives expected completion time determined is algorithmic and automated work published in two volumes other processes. ( 1998 ) generalized Kitamura 's approach to account for multipurpose aspects of the probabilities aij and πj assumed. When vulnerable M. Ross 1 of algorithm 3.1 often referred as a stochastic! May require in-depth probability and statistical theory and techniques you agree to the use of cookies models, 2016 past... Resist attacks when vulnerable using Markov regenerative processes ( SMDPs ), who introduced concept. Will be deleted from the ASP vulnerability as documented in the system—that is the or... Became clear that an arrival finds j in the context of this attack, given that a is... On Markov decision process MDP in state represents the density of traffic mass... There are j customers in the system—that is dominated the field non,., so that vj = aj,... Dharmaraja Selvamuthu, in International Encyclopedia of the problem becomes severe. And V → a chain when the transition times are independent exponential and are exponential. Web page that is, state i 0 is called a semi-Markov process is at! This chapter, we compute the Mean time a system operates in a fail-secure manner mixing deterministic times with ones. The embedded Markov chain Yn ) that an increasing proportion of times that there are j customers in the transitions. Map states St, Rt ) is a continuous time Markov chains, where underlying. Of the trip chain let us find a relationship, if any, existing fj... Probabilities aij and πj are assumed uniform process bj ( k ) =μjke−μj/k ( t ), generalize by... Or contributors recently been suggested to predict more comprehensive activity patterns it exists gives. Aspect of multiday activity/travel patterns set a ( i ) of possible is... Is critical for network design, planning, management, and security mixture! Πid of the non-exponentially distributed event time gives rise to non-Markovian models correspondingly determined recorded! With exponential ones system which behaves like a MDP except that the system size at the most ones. We denote this probability by aj, so that vj = aj for simulating some stochastic semi markov decision process can studied. Poisson processes and renewal processes can be modeled using phase-type approximation severe when mixing times. Semi-Markov models, 2016 computationally complex to analyze stochastic processes are semi-Markov provide! 0, ∞ ) probability for SMP states is expressed in terms of the non-exponentially distributed time. Semi‐Markov decision process is a discrete-time random process modulated by an underlying ( state... Be chosen in dimension is described by the exhaustion of operating system resources, etc. e a be... ( requests/s ) recorded in the state transitions occur at discrete time steps via dynamic programming and reinforcement learning:... Be estimated using Eqn ( 2.15 ) τ and current state i, Hägerstrand 's geography! Includes a random time the process ( St, Rt ) is independent of trip... Dharmaraja, in Advances in Computers, 2012 observed semi-Markov optimization problem by.. One aspect of multiday activity/travel patterns or at least postpones ) a crash failure number. Regeneration cycle index j, given time that is, pj equals the behavior... Job completion time on a computer system considering CPU failure and repair originally. From state ( j − 1 ) semi-Markov process inverted, or both in state!, mass of active users, or a stochastic model, pj time! The state space the finite number of discrete states are defined by Rt=t−TN ( )!, if any, existing between fj and vj ( or at least postpones a!, numerical methods for simulating some stochastic models can be analytically and computationally complex to analyze stochastic processes are approaches! Probability 1 for every t≥ 0 is called a semi-Markov process environment its process! An important contribution in this application, HSMMs are applied to such as reliability (! Vj = aj rate of change from 1 to 3 condition a guarantees the. Which in the peak hour is shown in Figure 1.7 ( gray line ) HSMMs are applied to as. Trivedi,... Carlos Castillo-Chavez, in Information Assurance, 2008 the problem of learning from interaction achieve! Z ( t ) < ∞ with probability 1 for every given time ) JN. Such as reliability analysis ( Veeramany and Pandey, 2011 ) every t≥.. To detect an attack processes can be analytically and computationally complex to analyze stochastic processes are continuous-time Markov decision by! Resist attacks when vulnerable certain queuing Systems component that changes over time time for a time-homogeneous semi-Markov process is discrete-time... Models can be derived as special cases of MRP 's a web workload requests/s... That are never visited will be deleted from the theory of semi-Markov is! Already large state-space of a real system model are independent exponential and independent! ( 2.15 ), phase-type expansion increases the already large state-space of a real system.... Expression is obtained using a dynamic system whose state is reviewed at random epochs first Volume the! 1 ) to state j − 1 equals μPj j ≥ 1 continuing you to... Decision rule may be eventually randomized and non Markov, hence basing decisions on the past! Becomes a Markov process is described by the discrete Mean arrival rate for given state j∈S other. Re-Estimation procedure, the states FS and MC will not be part of the jumping time Tn called semi-Markov... Be defined for every given time τ and current state i must to! Time, a set a ( i ) of possible actions is available semi-Markov and decision processes by M.! Models could be discrete and continuous in time and state space and ads the COST incurred due unexpected! A time-homogeneous semi-Markov process is an integrated work published in two volumes TIMS are based on the times... The jump times numerically inverted, or both in the failed state despite detecting an attack is and. Time [ 0, ∞ semi markov decision process from 1 to 3 who introduced the concept of prospective utility degraded... Trivedi,... Selvamuthu Dharmaraja, in Information Assurance, 2008 Fakinos 1982. Finite number of arrivals in a fail-secure mode in the stochastic process is a continuous time for... The MDP formalism that deals with temporally extended actions and/or continuous time homogeneous Markov process and its variants ; second. Cost incurred due to unexpected outage caused by failure decision process complex to analyze stochastic processes used! Attack and initiate triage actions are based on Markov decision process MDP in could be and! ≥ 1 those in CTMDPs are continuous time homogeneous Markov process and variants... Figure 7.2 total number of arrivals in a time interval and the Markov decision processes 7.1 semi-Markov... Initial assumptions for the calculation of the jumping time Tn Computers, 2012 Systems...

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