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[LEL 94] L ELAND WE, TAQQU MS, W ILLINGER W, W ILSON DV, On the self-similar nature of Ethernet traf c (extended version) , IEEE/ACM Trans on Networking, vol 2, p 1 15, 1994 [MAL 92] M ALLAT SG, H WANG WL, Singularity detection and processing with wavelets , IEEE Trans on Info Theory, vol 38, no 2, p 617 643, 1992 [MAL 98] M ALLAT SG, A Wavelet Tour of Signal Processing, Academic Press, San Diego, California, 1998 [MAN 68] M ANDELBROT BB, VAN N ESS JW, Fractional Brownian motions, fractional noises, and applications , SIAM Review, vol 10, no 4, p 422 437, 1968 [MAN 97] M ANDELBROT BB, Fractals and Scaling in Finance, Springer, New York, 1997 [MAS 93] M ASRY E, The wavelet transform of stochastic processes with stationary increments and its application to fractional Brownian motion , IEEE Trans on Info Theory, vol 39, no 1, p 260 264, 1993 [PAR 00] PARK K, W ILLINGER W (Eds), Self-similar Network Traf c and Performance Evaluation, John Wiley & Sons (Interscience Division), 2000 [PES 99] P ESQUET-P OPESCU B, Statistical properties of the wavelet decomposition of certain non-Gaussian self-similar processes , Signal Processing, vol 75, no 3, 1999 [ROU 99] ROUGHAN M, V EITCH D, Measuring long-range dependence under changing traf c conditions , in IEEE INFOCOM 99 (Manhattan, New York), IEEE Computer Society Press, Los Alamitos, California, p 1513 1521, March 1999 [ SAM 94] S AMORODNITSKY G, TAQQU MS, Stable Non aussian Random Processes: Stochastic Models with In nite Variance, Chapman and Hall, New York and London, 1994 [TEI 00] T EICH M, L OWEN S, J OST B, V IBE -R HEYMER K, H ENEGHAN C, Heart rate variability: measures and models , Nonlinear Biomedical Signal Processing, vol II, Dynamic Analysis and Modeling (M Akay, Ed), Ch 6, p 159 213, IEEE Press, 2001 [TEW 92] T EWFIK AH, K IM M, Correlation structure of the discrete wavelet coef cients of fractional Brownian motions , IEEE Trans on Info Theory, vol IT-38, no 2, p 904 909, 1992 [VEI 99] V EITCH D, A BRY P, A wavelet based joint estimator of the parameters of long-range dependence , IEEE Transactions on Information Theory (special issue on Multiscale statistical signal analysis and its applications ), vol 45, no 3, p 878 897, 1999 [VEI 00] V EITCH D, A BRY P, F LANDRIN P, C HAINAIS P, In nitely divisible cascade analysis of network traf c data , in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (Istanbul, Turkey), June 2000 [VEI 01] V EITCH D, A BRY P, A statistical test for the constancy of scaling exponents , IEEE Trans on Sig Proc, vol 49, no 10, p 2325 2334, 2001 [WOR 96] W ORNELL GW, Signal Processing with Fractals A Wavelet-based Approach, Prentice-Hall, 1996.

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17 presented tools that intruders use to confuse and evade NSM operations We learned about applications that intruders employ to perform reconnaissance, exploitation, and other nefarious deeds This chapter discusses strategies and tactics that intruders use to accomplish the same goal Here the targets are the people and processes that perform NSM operations While this chapter mentions tools, they are not analyzed to the extent they were in 17 Rather, tools are discussed if they represent a technique to defeat he people and processes that detect intrusions 1 introduced the idea of structured and unstructured threats This chapter applies more to the former than the latter Structured threats are very concerned with quietly compromising a victim and maintaining the unauthorized access They will employ one or more of the techniques described here to ensure an NSM operation fails to discover their presence Unstructured threats typically do not care if they are discovered If the victim detects their presence, unstructured threats move on to the next ill-prepared organization Structured threats typically compromise specific victims to achieve well-defined goals like financial crime or theft of intellectual property Unstructured threats value access, bandwidth, and hard drive space Systems with plenty of each are great places to store pirated software, trade music, or launch bandwidth consumption attacks against rival Internet gang members Because the line between structured and unstructured threats blurs, some of this chapter's tactics are employed by less sophisticated intruders This chapter is divided into four major sections about attackers In order of increasing sophistication, an intruder wishes to promote anonymity, evade detection, or, best of all, simply appear normal If all else fails, the intruder will degrade or deny collection of evidence, which complicates network- and host-based investigations I examine where attackers come from, what they do, and how they do it I explain many of these techniques in light of the five phases of compromise introduced in 1 (reconnaissance, exploitation, reinforcement, consolidation, and pillage) I conclude with a fifth section that describes poor NSM operational choices and processes Managers influence this realm, but intruders exploit the consequences of poor management, inadequate funding, and a lack of training.

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When IDSs were first contemplated in the 1970s and 1980s, many focused on anomaly detection Researchers continue to devote energy to this method, but pattern-based detection has been the dominant strategy for the last decade Pattern-based detection prevailed for a few simple reasons First, it was easier for developers to implement Searching a packet or reassembled stream for a string of characters is conceptually simple Ever-increasing traffic loads and deployment of encryption frustrate this approach, but pattern matching survives Second, it is easier for analysts to understand If an analyst knows the detection product is watching for a certain pattern, it makes sense when the IDS reports seeing that pattern If the pattern does not indicate a malicious event occurred, it's not the IDS's fault Verification has been lacking, but NSM approaches and integration of assessment techniques make pattern matching increasingly effective Third, it is much easier to deploy a rule based on a new pattern than it is to implement a new algorithm for anomaly-based intrusion detection Snort rules are released within hours of the discovery of a new worm, exploit, and other malicious code Engineers and analysts can plug the new rule into their product and validate or tune it as necessary Anomaly detection is not dead, however Pattern-matching systems are frequently compared to signature-based antivirus products If an antivirus product strictly relied on patterns to intercept and defeat malware, users would suffer infections much more often Antivirus products supplement their pattern-matching features with heuristic detection algorithms Heuristic techniques concentrate more on the acts the malware take, like opening network sockets, sending e-mail, and so on, and less on the code used to bring these acts to life Implementing heuristic detection or anomaly-based detection requires knowing what is normal, suspicious, and malicious We encountered those terms earlier in the book with respect to manual inspection of security events In some sense a human analyst is an anomaly detection device An experienced human analyst develops a sense of what is normal for the organizations he or she monitors Seeing an alert or session that falls outside those boundaries of normality prompts the analyst to investigate Validating these unusual events requires access to high-fidelity network traffic, as is provided by session data and full content data Network profiling is related to anomaly detection It's the idea that organizations should develop a formal sense of the devices and traffic patterns required for normal operational use Introduction of new devices or traffic types should raise an alarm Marcus Ranum calls this technique first seen nomaly detection: "[A first seen anomaly detection] system treats the first time it sees an instance of a thing as an anomaly It's accurate that something never seen before is always anomalous, in at least a few senses of the word When I was looking at statistical anomaly detection for IDS I realized that the first seen anomaly was 99% of what you'd get [with statistical anomaly detection products]".

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The goal of this chapter is to discuss typelibs as they relate directly to VB It is not a reference manual for the use of typelib compilation tools, such as MkTypLib or MIDL These are well-documented in MSDN and in numerous samples To complement the typelib compilers that ship with Visual Studio, the book's CD contains three typelib manipulation add-ins that you can use to accomplish everything discussed in this chapter The PowerVB Type Library Editor (EditTlbDII) allows you create and edit typelibs from within VB's IDE, PowerVB Binary Compatibility Editor (EditCompatDII) lets you make a restricted set of modifications to a project's compatibility file, and PowerVB Post-Build Type Library Modifier (PostBuildDII) lets you modify the VB-generated typelib This chapter focuses on the need for these tools as opposed to how you can use them: You'll find documentation with the tools on the CD Type-library editing is possible on any Win32 development platform However,! editing the binary compatibility file and the built executable equires full support! for the resource replacement API set, which is available only on the NT 4,0; and Windows 2000 operating systems. Make UPC A In Java Using Barcode maker for Java . Draw Data Matrix ECC200 In Java Using Barcode .Related: RDLC Barcode Generating , Generate Barcode Crystal how to, ASP.NET VB Barcode Generation