Download PDF by Jason Andress, Ryan Linn: Coding for Penetration Testers: Building Better Tools

By Jason Andress, Ryan Linn

ISBN-10: 1597497290

ISBN-13: 9781597497299

Tools used for penetration trying out are usually bought or downloaded from the net. every one software is predicated on a programming language resembling Perl, Python, or Ruby. If a penetration tester desires to expand, increase, or switch the performance of a device to accomplish a attempt in a different way than the default configuration, the tester needs to be aware of the fundamentals of coding for the similar programming language. Coding for Penetration Testers presents the reader with an realizing of the scripting languages which are usual while constructing instruments for penetration checking out. It additionally courses the reader via particular examples of customized software improvement and the occasions the place such instruments will be used. whereas constructing a greater figuring out of every language, the reader is guided via real-world situations and power improvement that may be integrated right into a tester's toolkit.
* Discusses using a variety of scripting languages in penetration testing
* offers step by step directions on find out how to construct personalized penetration trying out instruments utilizing Perl, Ruby, Python, and different languages
* offers a primer on scripting together with, yet no longer restricted to, net scripting, scanner scripting, and exploitation scripting

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Read e-book online Coding for Penetration Testers: Building Better Tools PDF

Tools used for penetration checking out are usually bought or downloaded from the net. each one software is predicated on a programming language equivalent to Perl, Python, or Ruby. If a penetration tester desires to expand, increase, or swap the performance of a device to accomplish a try otherwise than the default configuration, the tester needs to be aware of the fundamentals of coding for the comparable programming language. Coding for Penetration Testers offers the reader with an figuring out of the scripting languages which are normal while constructing instruments for penetration checking out. It additionally courses the reader via particular examples of customized instrument improvement and the occasions the place such instruments may be used. whereas constructing a greater realizing of every language, the reader is guided via real-world situations and gear improvement that may be integrated right into a tester's toolkit.
* Discusses using quite a few scripting languages in penetration testing
* provides step by step directions on the way to construct custom-made penetration checking out instruments utilizing Perl, Ruby, Python, and different languages
* offers a primer on scripting together with, yet no longer restricted to, internet scripting, scanner scripting, and exploitation scripting

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Additional info for Coding for Penetration Testers: Building Better Tools

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You've successfully installed scikit-learn. We will use a few of panda's convenient tools for importing data and calculating summary statistics. html. Semisupervised approaches make use of both labeled and unlabeled training data. We also reviewed the bias-variance trade-off and discussed common performance measures for different machine learning tasks. In the next chapter, we will discuss the regression task in more detail, and build our first machine learning model with scikitlearn. We will work through a toy problem to learn how the models and learning algorithms work before discussing an application with a larger dataset.

When the system incorrectly classifies a benign tumor as being malignant, the prediction is a false positive. These four outcomes can be used to calculate several common measures of classification performance, including accuracy, precision, and recall. Recall measures the fraction of truly malignant tumors that were detected. A different classifier with lower accuracy and higher recall might be better suited to the task, since it will detect more of the malignant tumors. In the next chapter, we will discuss some common performance measures for regression tasks.

Unsupervised learning problems do not have an error signal to measure; instead, performance metrics for unsupervised learning problems measure some attributes of the structure discovered in the data. Most performance measures can only be calculated for a specific type of task. While this may seem obvious, the following example describes the use of a performance measure that is appropriate for the task in general but not for its specific application. In this problem, however, failing to identify malignant tumors is likely to be a more severe error than mistakenly classifying benign tumors as being malignant.

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Coding for Penetration Testers: Building Better Tools by Jason Andress, Ryan Linn


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