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s l = ma1 loc(bi g) f o r each combination of t e s t parameters n, c , and offset: set a l l of SO and s l t o known pattern run slow memset(s0 + o f f s e t , c , n) run f a s t memset(s1 + o f f s e t , c , n) check return values compare a l l of SO and sl byte by byte An error that causes rnemset to write outside the limits of its array is most likely to affect bytes near the beginning or the end of the array, so leaving a buffer zone makes it easier to see damaged bytes and makes it less likely that an error will overwrite some other part of the program To check for writing out of bounds, we compare all the bytes of SO and s l , not just the n bytes that should be written Thus a reasonable set of tests might include all combinations of: offset = 10, 11, , 20 c = 0, 1 , Ox7F, 0x80, OxFF, Ox11223344

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n=0,1,2,3,4,5,7,8,9,15,16,17,

I compile and test to confirm that all is well 6 At this point, I wantextractText() to call accept() polymorphically, rather than having to type-castnode to call the appropriate accept() method for each accumulation source To make that possible, I apply Unify Interfaces (343) on the superclass and associated interface for StringNode, LinkTag, Tag, and EndTag:

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The HTML Parser (http://sourceforgenet/projects/htmlparser) is an open source library that allows programs to easily parse HTML It is the most popular HTML parser on SourceForge (http://sourceforgenet) and is used by many people throughout the world This project was initiated by Somik Raha Somik and others on the project made sure to write many tests as they developed the parser When I joined the project, I found areas in the code that required design improvements So I began refactoring, often while

31, 32, 33,

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pair-programming with Somik This work yielded many interesting refactorings, including numerous refactorings to patterns The following refactorings contain examples from the parser:

, 65535,

Move Embellishment to Decorator (144) Move Creation Knowledge to Factory(68) Extract Composite (214) Move Accumulation to Visitor (320)

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 we 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.

65536, 65537

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