By Jason Andress, Ryan Linn
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
Read or Download Coding for Penetration Testers: Building Better Tools PDF
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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
Mobiles and pills have introduced with them a dramatic switch within the application of purposes. Compatibility has develop into crucial, and this has elevated the type of interplay that clients count on: gestures, multi-touches, animations, and magic pens. Kivy is an open resource Python answer that covers those industry wishes with an easy-to-learn and swift improvement technique.
Python is readily changing into the go-to language for analysts and knowledge scientists because of its simplicity and suppleness, and in the Python information house, scikit-learn is the unequivocal selection for computer studying. Its constant API and plethora of good points aid resolve any computing device studying challenge it comes throughout.
Follow powerful studying algorithms to real-world difficulties utilizing scikit-learn
About This booklet
• layout and troubleshoot desktop studying platforms for universal projects together with regression, type, and clustering
• Acquaint your self with renowned desktop studying algorithms, together with selection bushes, logistic regression, and help vector machines
• a realistic example-based consultant that can assist you achieve services in enforcing and comparing desktop studying platforms utilizing scikit-learn
[ Who This booklet Is For
If you're a software program developer who desires to find out how desktop studying versions paintings and the way to use them successfully, this e-book is for you. Familiarity with desktop studying basics and Python could be beneficial, yet isn't really crucial.
What you are going to examine
• overview primary techniques together with supervised and unsupervised reviews, universal projects, and function metrics
• expect the values of constant variables utilizing linear regression
• Create representations of records and pictures that may be utilized in computing device studying versions
• Categorize records and textual content messages utilizing logistic regression and help vector machines
• Classify photos via their topics
• notice hidden buildings in information utilizing clustering and visualize complicated facts utilizing decomposition
• overview the functionality of computing device studying platforms in universal projects
• Diagnose and redress issues of types as a result of bias and variance
This booklet examines laptop studying types together with logistic regression, choice bushes, and help vector machines, and applies them to universal difficulties corresponding to categorizing files and classifying pictures. It starts off with the basics of desktop studying, introducing you to the supervised-unsupervised spectrum, the makes use of of teaching and try out facts, and comparing versions. you'll easy methods to use generalized linear versions in regression difficulties, in addition to remedy issues of textual content and specific features.
You may be familiar with using logistic regression, regularization, and a number of the loss services which are utilized by generalized linear types. The ebook also will stroll you thru an instance undertaking that activates you to label the main doubtful education examples. additionally, you will use an unmonitored Hidden Markov version to foretell inventory prices.
By the tip of the ebook, you may be a professional in scikit-learn and should be good versed in desktop studying
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Additional info for Coding for Penetration Testers: Building Better Tools
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.
Coding for Penetration Testers: Building Better Tools by Jason Andress, Ryan Linn