Download PDF by George Kesidis: An introduction to communication network analysis

By George Kesidis

ISBN-10: 0470168684

ISBN-13: 9780470168684

This publication is a quantitative textual content, which makes a speciality of the true concerns at the back of critical modeling and research of communications networks. the writer covers the entire invaluable arithmetic and idea to ensure that scholars to appreciate the instruments that optimize desktop networks at the present time.

  • Covers either classical (e.g. queueing concept) and glossy (e.g. pricing) facets of networking
  • Integrates fabric on communique networks with fabric on modeling/analyzing and designing such networks
  • Includes an answer Manual

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A:,, = a:,,. 24 Consider a link carrying packets to a Web server. Suppose that, under normal operating conditions, the link will subject the Web server to a data rate of 4 Mbps. However, when the Web server is under a DDoS attack, the link will carry an average of 6 Mbps to the server. An IDS samples the link's data rate and determines whether the server is under attack. 5 Mbps under attack conditions. Finally, assume attack conditions exist 25% of the time. Find the value of the optimal decision threshold Q (compared against the sample mean) that minimizes the probability of decision error.

For a given sample w , the random object mapping t -+ X,(t), for all t E R+ say, is called a sample path of the stochastic process X . The state space of a stochastic process is simply the union of the strict ranges of the random variables { X ( t )I t E Z+). In this book, we will restrict our attention to stochastic processes with countable state spaces. 1,2. K ) . Of that follow, we will take the state space to be Z, course, this means that the random variables X ( t ) are all discretely distributed.

An unbiased, consistent estimate of this variance is To determine the probability of decision error, we will condition on the state of the network: where p2 = 1 - p l . To this point, we have not described the method by which a decision on the network state is made. Consider a decision based on the comparison of the sample mean X , with a threshold 0, where so that the network is deemed to be in state 2 (having the higher mean) if X , > 0; otherwise, the network is deemed to be in state 1. 32) ensues because we can approximate P(error I network in state 2) = P(X, < B 1 network in state 2) where is 1 minus the CDF of a standard Gaussian distribution.

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An introduction to communication network analysis by George Kesidis


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