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always be possible for various reasons, including the signi cant costs associated with replacing existing systems with more attack resistant solutions, the diverse capabilities of different devices (eg CPU, data storage, bandwidth), the inability of devices to support attack resistant solutions (which is often more of a problem for wireless networks), the inef cient con guration (eg users do not change default security settings or apply patches), and so on For example, SCTP is rarely used today due to the cost of changing all of the existing systems using TCP In addition, processor intensive preventive mechanisms (such as some cryptographic operations) intended to protect the enterprise systems might also be used against the system For example, such technologies may be used for launching DoS attacks by overloading the available CPU resources The second way of protecting enterprises from such intrusions is to monitor events occurring in a computer system and detect attempts to either leverage the vulnerabilities of preventive mechanisms or misuse preventive mechanisms This is called intrusion detection and is the focus of this chapter Intrusion detection per se does not prevent users from obtaining unauthorized access to data or systems but rather focuses on monitoring the behavior of users Following detection, efforts might be made to stop such attempts from succeeding It might also be necessary to have the system recover from the effects of any attack Some aspects of recovery will be the focus of the next chapter There are several advantages to implementing intrusion detection systems (IDS) in a network An IDS acts as a deterrent for attackers It also can detect attacks that bypass other security measures It further provides information about the actual intrusions that can be used for responding to such attacks An IDS is also helpful in performing quality control of security design and administration In this chapter we will focus on IDS In Section 511 we focus on discussing the IDS concept and how it is applied today in a traditional enterprise environment We then focus on the challenges of adapting existing IDS techniques to MANET and discuss IDS approaches that are well suited to MANET.

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These are just some of the relevant risks that firms must take into consideration when evaluating the delivery process for a product Combined with the notion of the product concept, they paint a comprehensive overview of the challenge involved in selling a client on a proposed project Equally importantly, we need to recognize that the mix of factors considered by the contracting firm will change when dealing with differing firms and project opportunities The inherent risk associated with a project is a direct function of the relationship the two groups or organizations have formed When I have a positive relationship based on trust and a record of

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[AYA 99] AYACHE A, L VY V HEL J, Generalised multifractional Brownian motion: de nition and preliminary results , in D EKKING M, L VY V HEL J, L UTTON E, T RICOT C (Eds), Fractals: Theory and Applications in Engineering, Springer-Verlag, p 17 32, 1999 [AYA 00] AYACHE A, C OHEN S, L VY V HEL J, The covariance structure of multifractional Brownian motion, with application to long range dependence , in Proceedings of ICASSP (Istanbul, Turkey), 2000 [AYA 04a] AYACHE A, B ENASSI A, C OHEN S, L VY V HEL J, Regularity and identi cation of generalized multifractional Gaussian processes , in S minaire de Probabilit s XXXVIII Lecture Notes in Mathematics, Springer-Verlag Heidelberg, vol 1857, p 290 312, 2004 [AYA 04b] AYACHE A, L VY V HEL J, On the identi cation of the pointwise H lder exponent of the generalized multifractional Brownian motion , in Stoch Proc Appl, vol 111, p 119 156, 2004 [BEN 94] B ENASSI A, C OHEN S, JAFFARD S, Identi cation de processus Gaussiens elliptiques , C R Acad Sc Paris, series, vol 319, p 877 880, 1994 [BEN 97a] B ENASSI A, C OHEN S, I STAS J, JAFFARD S, Identi cation of elliptic Gaussian random processes , in L VY V HEL J, T RICOT C (Eds), Fractals and Engineering, Springer-Verlag, p 115 123, 1997 [BEN 97b] B ENASSI A, JAFFARD S, ROUX D, Gaussian processes and pseudodifferential elliptic operators , Revista Mathematica Iberoamericana, vol 13, no 1, p 19 89, 1997 [BEN 98a] B ENASSI A, C OHEN S, I STAS J, Identifying the multifractional function of a Gaussian process , Statistic and Probability Letters, vol 39, p 337 345, 1998 [BEN 98b] B ENASSI A, C OHEN S, I STAS J, JAFFARD S, Identi cation of ltered white noises , Stoch Proc Appl, vol 75, p 31 49, 1998 [BEN 00] B ENASSI A, B ERTRAND P, C OHEN S, I STAS J, Identi cation of the Hurst exponent of a step multifractional Brownian motion , Statistical Inference for Stochastic Processes, vol 3, p 101 110, 2000 [BER 00] B ERTRAND P, A local method for estimating change points: the hat-function , Statistics, vol 34, no 3, p 215 235, 2000.

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