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measure; this is done by multiplication with a so-called likelihood ratio IS has received substantial theoretical attention; see inter alia [13, 12, 26] and, in the rare event context, [ 15] or the more up-to-date tutorial [16] IS s one of the most widely used variance reduction technique in general, and for rare event estimation in particular Typical and speci c applications will be more extensively described in Part II of this book The goal of this chapter is to give an overview of the technical framework and the main underlying ideas It is organized as follows Section 22 reviews the very basic notions of IS It describes what the ideal (zero-variance) estimator looks like, and why it is, except in situations where simulation is not needed, infeasible to implement it exactly That section also provides illustrative examples and outlines some properties leading to a good IS estimator, the main message being that the zero-variance estimator has to be approximated as closely as possible In Section 23 the focus is on application of IS in the context of a Markov chain model Since every discrete-event simulation model can be seen as a Markov chain (albeit over a high-dimensional state space), this setting is very general We show how to de ne a zero-variance change of probabilities in that context It is noted that, in general, the zero-variance change of probabilities must depend on the state of the chain We compare this type of change of probabilities with a more restricted class of IS called state-independent, in which the probabilities are changed independently of the current state of the chain This type of state-independent IS originates mainly from asymptotic approximations based on large-deviations theory [3, 15, 25], and has been developed in applications areas such as queuing and nance [10, 15, 16, 19] However, in many situations, any good IS scheme must be state-dependent [3] (as state-independent IS leads to estimators with large, or even in nite, variance) Note that in computational physics (the application area from which it originates) and in reliability, IS has traditionally been state-dependent [5, 14, 16] Finally, Section 24 describes various methods used to approximate the zero-variance (ie, optimal) change of measure Some just use intuitive approximations, whereas others are based on the asymptotic behavior of the system when the events of interest become rarer and rarer (this includes methods based on large-deviations theory, and other techniques as well) Another option is to use adaptive techniques that learn (and use) approximations of the zero-variance change of measure, or optimal parameter values within a class of parameterized IS strategies: the results of completed runs can be used as inputs of strategies for the next runs, but those IS strategies can also be updated at each step of a given run [16, 22] The accuracy assessment of the resulting con dence interval, and the robustness properties of the estimator with respect to rarity, are the focus of the next chapter To avoid overlap, we keep our discussion of those aspects to a minimum here.

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Just as f-stops measure exposure level, lens length defines an image s size, apparent depth, and proportion Every video lens has a length, which gives what we think of as a normal view a picture closest to the size, point of view (POV), and proportion of the image our eyes see from the camera s POV Any lens setting shorter than normal (less than 50 mm) creates a wideangle view and a greater sense of depth A lens length setting longer than normal (more than 50 mm) makes an image that is narrow (magnified) in iew and flattened in depth. Universal Product Code Version A Generation In .NET Using Barcode maker for ASP.NET .Related: Intelligent Mail Generation C# , Intelligent Mail Generation Java , Code 128 Generator Java

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While more detail can be extracted by many of the tools presented in this chapter, these six elements are the core of any session data collection strategy In the context of this chapter, we care about session data for its ability to track intruder activities in a content-neutral way Unlike alert data tools (eg, intrusion detection systems), which rely on triggering a threshold or matching a pattern, session data applications collect everything they see The ability to track everything, or everything up to the limits of your session application, is invaluable for tracking stealthy intruders From a network investigation standpoint, full content data is more valuable than session data Full content data can be sliced and diced in any number of ways by multiple tools But because collecting full content data can be nearly impossible on high-traffic links, we turn to session data as the next best approximation of conversations between networked parties Support for session data collection is built in to many common pieces of networking infrastructure Session data has seen more use for accounting and billing purposes, but many analysts are realizing the security aspects of this important form of network audit data, especially as traffic loads increase Some thoughtful pioneers have been using session data for years to detect intruders Since the mid-1990s the Air Force Computer Emergency Response Team's (AFCERT) Automated Security Incident Measurement (ASIM) sensors have collected session data Analysts have queried or manually perused records of exchanges between Air Force systems and the world, finding novel intrusions not detected by ASIM's signature-matching logic In the late 1990s, Paul Myrick, then an officer in the Air Force Communications Agency, wrote a tool called Profiler to independently collect session data at Air Force bases In 1993, Carter Bullard, then with Carnegie Mellon, began work on the Audit Record Generation and Utilization System (Argus) program There are many ways to use session data on operational networks, but this book focuses on security We take an even more narrow approach when e use session data to track the activities of intruders We care about the intruder's IP address and the protocols he or she uses; we don't care so much about the bandwidth the intruder consumed I present enough information to get the tools running under normal circumstances, and then help decipher their output.

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