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For this bug to be reached, userinput1 must be set to a string (known to the authors of the protocol, but not to our fuzzer), and userinput2 must be a very long string You can divide this bug into two factors: 1 Userinput1 must be a particular string 2 Userinput2 must be a long string For example, assume the program is an SMTP server, which supports HELO, EHLO, and HELL as Hello commands Perhaps some bugs are triggered only when the server sees HELL, which is an undocumented feature used only by this SMTP server Even assuming the fuzzer has a list of special strings, as you get above a few factors you quickly notice that the process becomes exponentially more expensive A good fuzzer has a list of strings it will try That means that for each variable you are fuzzing, you must try N strings.

channel modeling usually relies on the availability of a three-dimensional (3-D) database In addition to the geometry, the electromagnetic parameters of the materials also need to be included in the database The major advantage of these models is that they offer great accuracy with site-speci c results Any site can be modeled if its physical characteristics are available, and any parameter can be calculated by adjusting these models However, these models have several disadvantages Firstly, the topographical and building data is always tied to a particular site and thus a huge amount of such data is required in order to obtain a comprehensive set of different propagation environments Secondly, they are usually computationally intensive, especially when the environment is complex Thus, detailed physical characteristics of the simulated environment must be known beforehand which is often time-consuming and impractical Numerous UWB channel models have been developed under this category.

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For example, in [20] the ray tracing approach is combined with the uniform theory of diffraction (UTD) to model the received signal as a superposition of rays This model has the advantage of being versatile and of general validity However, it has high complexity in the channel modeling which leads to a high computational load Another UWB channel model based on UTD technique is reported in [21, 22] This model consists of three basic ray mechanisms of geometrical optics and time-domain UTD (ie, directed ray, multire ected rays from lossy surfaces and diffracted ray from lossy edge) This approach can determine both the signal attenuation and the waveform distortion in terms of pulse shape and pulse duration A different approach has been pursued in [23], where the frequency and distance characteristics of a two-ray propagation model have been modeled using a deterministic approach.

And if you want to match that against another variable, that s N*M strings, and so on (For the fuzzer, an integer is just a short binary string) These are the main two weaknesses in fuzzers Generally, people compensate for these weaknesses by also using static analysis or by doing runtime binary analysis against the target program These techniques can increase code coverage and hopefully find bugs hidden by traditional fuzzing As you come to use fuzzers of various sorts, you ll discover that different fuzzers also have other weaknesses Perhaps this is due to their underlying infrastructure SPIKE, for example, is built heavily on C and is not object oriented Or you ll find that some target programs are ill-suited to fuzzing.

As often as possible and when appropriate follow up with employers via email rather than racking up big expenses from long-distance calls and faxes, or from postage and stationery.

Examples of other UWB deterministic channel models reported in the literature are in [24 29] 8222 Statistical Models Statistical models are normally less complex than the deterministic models, and can provide suf ciently accurate channel information Statistical models attempt to generate synthetic channel responses that are representative of real propagation channels Typically, such models can be tuned to imitate various propagation environments by setting appropriate values for the channel model parameters Note that xed parameter settings do not produce identical outputs on each simulation run but stochastic processes are used to create variability within a xed environment type For example, a particular set of parameters might generate a representative set of propagation scenarios found in indoor environments.

Statistical models may be formed based on the basic principals of wave propagation for random communications channels and by assuming a statistical distribution of the channel parameters, and computing the required statistical moments from the data collected from the real-time measurements This category of models has the ability to provide accurate statistical information, without the complexity of detailed deterministic approaches Several statistical-based UWB channels have been proposed recently For example, Ghassemzadeh et al [30] proposes a model based on the frequencydomain channel measurements results in 23 residential environments (based on extensive propagation studies in 23 homes) Measurements were conducted in the.

Perhaps they are very slow, or perhaps they crash with nearly any bad input, making it difficult to find an exploitable bug among all the crashes (iMail and rpcttdbserverd come to mind) Or perhaps you ll find that the protocol is just too complex to decipher from network traces Luckily, however, these are not common cases..

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