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In this section we introduce a decoding technique applicable to a channel model which differs significantly from other channel models used throughout this book. Because of the close connection of turbo equalization with turbo decoding, it is deemed to be an appropriate topic to include here. In the rest of the book, the channel model has been a discrete memoryless model, specifically, an adhtive noise channel in which the received signal rr is simply the transmitted signal st corrupted by additive noise (typically either Gaussian or Bernoulli): rt = st nt . In this section, however, we consider the case that the channel has a response h ~ z - so that the received ~ , characterized by a transfer function H ( z ) = ho hlz- +. signal is

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9/21/05

June 12, 2004

This phase comes after a verification that has concluded that a piece of equipment is beyond permissible error limits. It makes it possible to restore conformity to this equipment by using its fitting devices, if it is equipped with any. Metrologists make a distinction between: fitting: bring an equipment as close to zero as possible , and adjusting: fitting it by using only the devices that are at the user s disposal. It is to be noted that after any fitting (and therefore any adjustment), a new calibration and a new verification must take place which will make it possible to confirm that the equipment can be used (and is back within maximum permissible errors ).

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Understanding the Risks 659 Theft of con dential information 659 Vandalism and defacement 659 Denial of service 660 Loss of data 660 Loss of assets 660 Loss of credibility and reputation 660 Litigation 661 Web Site Security Issues 661 File permissions 661 Unused but open ports 662 CGI scripts 662 Buffer over ows 663 Compromised systems 663 Overview of Web Security Methods 664 Drafting a comprehensive security policy 664 Checking online security warnings 664 Excluding search engines 665 Using secure servers 665 Summary 666.

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We have made theoretical advances, from reference monitors through trusted computing bases to access control models We have made practical advances from security components like firewalls through security protocols like IPSec to infrastructure products like Kerberos or PKI We must face the reality that all our security advances have not helped us as much as they should, however, because buggy code is the downfall of them all Code often fails because it does not anticipate all possible inputs The Garbage In, Garbage Out (GIGO) principle is firmly entrenched in coding practice, which has had the unfortunate side effect of leaving behavior on bad input unspecified The code fails, but with what consequences Miller et al, in [Mill00], provide some empirical evidence on the input validation issue They use a fuzz generator to throw randomly generated values at system utilities on several flavors of Unix.

$ 4,500 $ 1,400 $ 2,400 $ 600 $ 1,200 $ 1,100 $11,200

( 4.5)

Many utilities fail because they mishandle one of several input validation tests, causing the majority of crashed, hung, or otherwise failed end results The problems identified include pointer handling without NULL tests, array bounds checks, the use of bad input functions, the use of char to represent symbolic and numeric data, division by zero checks, and end-of-file checks These activities can also lead to security bugs and therefore are targets for code review One outcome of the Miller survey is the discovery that open source is often best in class for speed and security: quality drives security This common viewpoint is also expressed in [KP00], [COP97], and [VFTOSM99] These authors note the curious paradox of why open source is of such high quality despite being developed with very little software process by a diverse group of programmers distributed over time and space.

This situation is true, perhaps, because the popular open-source projects tend to be more visible and better understood Open source code is often written by expert programmers, reviewed and debugged by a wide and vocal group of testers, and ported to many platforms weeding out OS and hardware-specific design quirks Ownership and pride in craftsmanship are also cited as drivers of quality in open source Although open source might not be an option for your application, external code review can help improve quality..

$700 $600 18% $500 Originations (Bn) 20% 20% 25.0%