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.NET code is vulnerable to reverse engineering, certainly more so than native IA-32 code or native code for most other processor architectures. The combination of metadata and highly detailed IL code makes it possible to decompile IL methods into remarkably readable high-level language code. Obfuscators aim at reducing this vulnerability by a number of techniques, but they have a limited effect that will only slow down determined reversers. There are two potential strategies for creating more powerful obfuscators that will have a serious impact on the vulnerability of .NET executables. One is to enhance the encryption concept used by Remotesoft Protector and actually use separate keys for different areas in the program. The decryption should be done by programmatically generated IL code that is never the same in two obfuscated programs (to prevent automated unpacking), and should use keys that come from a variety of places (regions of metadata, constants within the code, parameters passed to methods, and so on). Another approach is to invest in more advanced obfuscating transformations such as the ones discussed in 10. These are transformations that significantly alter the structure of the code so as to make comprehension considerably more difficult. Such transformations might not be enough to prevent decompilation, but the objective is to dramatically reduce the readability of the decompiled output, to the point where the decompiled output is no longer useful to reversers. Version 3.0 of PreEmptive Solution s DotFuscator product (not yet released at the time of writing) appears to take this approach, and I would expect other developers of obfuscation tools to follow suit.



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This chapter differs from the rest of this book in the sense that it does not discuss any practical reversing techniques, but instead it focuses on the inner workings of one of the most interesting reversing tools: the decompiler. If you are only interested in practical hands-on reversing techniques, this chapter is not for you. It was written for those who already understand the practical aspects of reversing and who would like to know more about how decompilers translate low-level representations into high-level representations. I personally think any reverser should have at least a basic understanding of decompilation techniques, and if only for this reason: Decompilers aim at automating many of the reversing techniques I ve discussed throughout this book. This chapter discusses both native code decompilation and decompilation of bytecode languages such as MSIL, but the focus is on native code decompilation because unlike bytecode decompilation, native code decompilation presents a huge challenge that hasn t really been met so far. The text covers the decompilation process and its various stages, while constantly demonstrating some of the problems typically encountered by native code decompilers.

(21.42)

t = 5000

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Compilation is a more or less well-defined task. A program source file is analyzed and is checked for syntactic validity based on (hopefully) very strict

t = 7500

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where wij stands for the Clifford weights and oi and oi for the actual and target outputs respectively. As usual, in each iteration the standard genetic operations (mutation, crossover) are carried out and the fitness of the population is evaluated. The procedure carries on and on until little progress is noticed. The qualified genotype guarantees a good performance of the neural net. Interestingly enough, this procedure takes into account the involved Clifford product. We trained a geometric MLP using the genetic algorithm for the problem of pattern classification. For this case, we utilized as preprocessing the hypercomplex moments. As expected, this kind of training proved not to be trapped in a local minimum. This experiment is discussed in detail in Section 9.

Figure 15.12. Snapshots of the network showing alive particles when executing LTP at different time instances (n = 5000).

language specifications From this high-level representation, the compiler generates an intermediate representation of the source program that attempts to classify exactly what the program does, in compiler-readable form The program is then analyzed and optimized to improve its efficiency as much as possible, and it is then converted into the target platform s assembly language There are rarely question marks with regard to what the program is trying to do because the language specifications were built so that compilers can easily read and understand the program source code This is the key difference between compilers and decompilers that often makes decompilation a far more indefinite process Decompilers read machine language code as input, and such input can often be very difficult to analyze With certain higher-level representations such as Java bytecode or .

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