Learning Python for Forensics

Learn the art of designing, developing, and deploying innovative forensic solutions through Python About This Book This practical guide will help you solve forensic dilemmas through the development of Python scripts Analyze Python scripts ...

Author: Preston Miller

Publisher: Packt Publishing Ltd

ISBN: 9781783285242

Category: Computers

Page: 488

View: 614

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Learn the art of designing, developing, and deploying innovative forensic solutions through Python About This Book This practical guide will help you solve forensic dilemmas through the development of Python scripts Analyze Python scripts to extract metadata and investigate forensic artifacts Master the skills of parsing complex data structures by taking advantage of Python libraries Who This Book Is For If you are a forensics student, hobbyist, or professional that is seeking to increase your understanding in forensics through the use of a programming language, then this book is for you. You are not required to have previous experience in programming to learn and master the content within this book. This material, created by forensic professionals, was written with a unique perspective and understanding of examiners who wish to learn programming What You Will Learn Discover how to perform Python script development Update yourself by learning the best practices in forensic programming Build scripts through an iterative design Explore the rapid development of specialized scripts Understand how to leverage forensic libraries developed by the community Design flexibly to accommodate present and future hurdles Conduct effective and efficient investigations through programmatic pre-analysis Discover how to transform raw data into customized reports and visualizations In Detail This book will illustrate how and why you should learn Python to strengthen your analysis skills and efficiency as you creatively solve real-world problems through instruction-based tutorials. The tutorials use an interactive design, giving you experience of the development process so you gain a better understanding of what it means to be a forensic developer. Each chapter walks you through a forensic artifact and one or more methods to analyze the evidence. It also provides reasons why one method may be advantageous over another. We cover common digital forensics and incident response scenarios, with scripts that can be used to tackle case work in the field. Using built-in and community-sourced libraries, you will improve your problem solving skills with the addition of the Python scripting language. In addition, we provide resources for further exploration of each script so you can understand what further purposes Python can serve. With this knowledge, you can rapidly develop and deploy solutions to identify critical information and fine-tune your skill set as an examiner. Style and approach The book begins by instructing you on the basics of Python, followed by chapters that include scripts targeted for forensic casework. Each script is described step by step at an introductory level, providing gradual growth to demonstrate the available functionalities of Python.

Learning Python for Forensics

You will learn how to develop Python scripts through an iterative design. This book will also help you strengthen your analysis skills and efficiency as you creatively solve real-world problems through instruction-based tutorials.

Author: Preston Miller

Publisher: Packt Publishing Ltd

ISBN: 9781789342765

Category: Computers

Page: 476

View: 563

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Learning Python for Forensics, Second Edition begins by introducing you to the fundamentals of Python. You will learn how to develop Python scripts through an iterative design. This book will also help you strengthen your analysis skills and efficiency as you creatively solve real-world problems through instruction-based tutorials.

Python Digital Forensics Cookbook

Over 60 recipes to help you learn digital forensics and leverage Python scripts to amplify your examinations About This Book Develop code that extracts vital information from everyday forensic acquisitions.

Author: Preston Miller

Publisher: Packt Publishing Ltd

ISBN: 9781783987474

Category: Computers

Page: 412

View: 628

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Over 60 recipes to help you learn digital forensics and leverage Python scripts to amplify your examinations About This Book Develop code that extracts vital information from everyday forensic acquisitions. Increase the quality and efficiency of your forensic analysis. Leverage the latest resources and capabilities available to the forensic community. Who This Book Is For If you are a digital forensics examiner, cyber security specialist, or analyst at heart, understand the basics of Python, and want to take it to the next level, this is the book for you. Along the way, you will be introduced to a number of libraries suitable for parsing forensic artifacts. Readers will be able to use and build upon the scripts we develop to elevate their analysis. What You Will Learn Understand how Python can enhance digital forensics and investigations Learn to access the contents of, and process, forensic evidence containers Explore malware through automated static analysis Extract and review message contents from a variety of email formats Add depth and context to discovered IP addresses and domains through various Application Program Interfaces (APIs) Delve into mobile forensics and recover deleted messages from SQLite databases Index large logs into a platform to better query and visualize datasets In Detail Technology plays an increasingly large role in our daily lives and shows no sign of stopping. Now, more than ever, it is paramount that an investigator develops programming expertise to deal with increasingly large datasets. By leveraging the Python recipes explored throughout this book, we make the complex simple, quickly extracting relevant information from large datasets. You will explore, develop, and deploy Python code and libraries to provide meaningful results that can be immediately applied to your investigations. Throughout the Python Digital Forensics Cookbook, recipes include topics such as working with forensic evidence containers, parsing mobile and desktop operating system artifacts, extracting embedded metadata from documents and executables, and identifying indicators of compromise. You will also learn to integrate scripts with Application Program Interfaces (APIs) such as VirusTotal and PassiveTotal, and tools such as Axiom, Cellebrite, and EnCase. By the end of the book, you will have a sound understanding of Python and how you can use it to process artifacts in your investigations. Style and approach Our succinct recipes take a no-frills approach to solving common challenges faced in investigations. The code in this book covers a wide range of artifacts and data sources. These examples will help improve the accuracy and efficiency of your analysis—no matter the situation.

Mastering Python Forensics

Master the art of digital forensics and analysis with Python About This Book Learn to perform forensic analysis and investigations with the help of Python, and gain an advanced understanding of the various Python libraries and frameworks ...

Author: Dr. Michael Spreitzenbarth

Publisher: Packt Publishing Ltd

ISBN: 9781783988051

Category: Computers

Page: 192

View: 692

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Master the art of digital forensics and analysis with Python About This Book Learn to perform forensic analysis and investigations with the help of Python, and gain an advanced understanding of the various Python libraries and frameworks Analyze Python scripts to extract metadata and investigate forensic artifacts The writers, Dr. Michael Spreitzenbarth and Dr. Johann Uhrmann, have used their experience to craft this hands-on guide to using Python for forensic analysis and investigations Who This Book Is For If you are a network security professional or forensics analyst who wants to gain a deeper understanding of performing forensic analysis with Python, then this book is for you. Some Python experience would be helpful. What You Will Learn Explore the forensic analysis of different platforms such as Windows, Android, and vSphere Semi-automatically reconstruct major parts of the system activity and time-line Leverage Python ctypes for protocol decoding Examine artifacts from mobile, Skype, and browsers Discover how to utilize Python to improve the focus of your analysis Investigate in volatile memory with the help of volatility on the Android and Linux platforms In Detail Digital forensic analysis is the process of examining and extracting data digitally and examining it. Python has the combination of power, expressiveness, and ease of use that makes it an essential complementary tool to the traditional, off-the-shelf digital forensic tools. This book will teach you how to perform forensic analysis and investigations by exploring the capabilities of various Python libraries. The book starts by explaining the building blocks of the Python programming language, especially ctypes in-depth, along with how to automate typical tasks in file system analysis, common correlation tasks to discover anomalies, as well as templates for investigations. Next, we'll show you cryptographic algorithms that can be used during forensic investigations to check for known files or to compare suspicious files with online services such as VirusTotal or Mobile-Sandbox. Moving on, you'll learn how to sniff on the network, generate and analyze network flows, and perform log correlation with the help of Python scripts and tools. You'll get to know about the concepts of virtualization and how virtualization influences IT forensics, and you'll discover how to perform forensic analysis of a jailbroken/rooted mobile device that is based on iOS or Android. Finally, the book teaches you how to analyze volatile memory and search for known malware samples based on YARA rules. Style and approach This easy-to-follow guide will demonstrate forensic analysis techniques by showing you how to solve real-word-scenarios step by step.

Python Digital Forensics Cookbook

Over 60 recipes to help you learn digital forensics and leverage Python scripts to amplify your examinationsAbout This Book* Develop code that extracts vital information from everyday forensic acquisitions.* Increase the quality and ...

Author: Preston Miller

Publisher:

ISBN: 1783987464

Category: Computers

Page: 412

View: 328

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Over 60 recipes to help you learn digital forensics and leverage Python scripts to amplify your examinationsAbout This Book* Develop code that extracts vital information from everyday forensic acquisitions.* Increase the quality and efficiency of your forensic analysis.* Leverage the latest resources and capabilities available to the forensic community.Who This Book Is ForIf you are a digital forensics examiner, cyber security specialist, or analyst at heart, understand the basics of Python, and want to take it to the next level, this is the book for you. Along the way, you will be introduced to a number of libraries suitable for parsing forensic artifacts. Readers will be able to use and build upon the scripts we develop to elevate their analysis.What You Will Learn* Understand how Python can enhance digital forensics and investigations* Learn to access the contents of, and process, forensic evidence containers* Explore malware through automated static analysis* Extract and review message contents from a variety of email formats* Add depth and context to discovered IP addresses and domains through various Application Program Interfaces (APIs)* Delve into mobile forensics and recover deleted messages from SQLite databases* Index large logs into a platform to better query and visualize datasetsIn DetailTechnology plays an increasingly large role in our daily lives and shows no sign of stopping. Now, more than ever, it is paramount that an investigator develops programming expertise to deal with increasingly large datasets.By leveraging the Python recipes explored throughout this book, we make the complex simple, quickly extracting relevant information from large datasets. You will explore, develop, and deploy Python code and libraries to provide meaningful results that can be immediately applied to your investigations. Throughout the Python Digital Forensics Cookbook, recipes include topics such as working with forensic evidence containers, parsing mobile and desktop operating system artifacts, extracting embedded metadata from documents and executables, and identifying indicators of compromise. You will also learn to integrate scripts with Application Program Interfaces (APIs) such as VirusTotal and PassiveTotal, and tools such as Axiom, Cellebrite, and EnCase.By the end of the book, you will have a sound understanding of Python and how you can use it to process artifacts in your investigations.Style and approachOur succinct recipes take a no-frills approach to solving common challenges faced in investigations. The code in this book covers a wide range of artifacts and data sources. These examples will help improve the accuracy and efficiency of your analysis-no matter the situation.

Digital Forensics and Incident Response

Other Books You May Enjoy If you enjoyed this book, you may be interested in these other books by Packt: Learning Python for Forensics - Second Edition Preston Miller, Chapin Bryce ISBN: 978-1-78934-169-0 Learn how to develop Python ...

Author: Gerard Johansen

Publisher: Packt Publishing Ltd

ISBN: 9781838644086

Category: Computers

Page: 448

View: 612

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An understanding of how digital forensics integrates with the overall response to cybersecurity incidents is a must for all organizations. This book offers concrete and detailed guidance on how to conduct the full spectrum of incident response and digital forensic activities.

Practical Mobile Forensics

Learning Python for Forensics Preston Miller, Chapin Bryce ISBN: 978-1-78328-523-5 Discover how to perform Python script development Update yourself by learning the best practices in forensic programming Build scripts through an ...

Author: Rohit Tamma

Publisher: Packt Publishing Ltd

ISBN: 9781838644420

Category: Computers

Page: 400

View: 529

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Covering up-to-date mobile platforms, this book focuses on teaching you the most recent tools and techniques for investigating mobile devices. Readers will delve into a variety of mobile forensics techniques for iOS 11-13, Android 8-10 devices, and Windows 10.

Integrating Python with Leading Computer Forensics Platforms

Learn about the concepts of basic programming like lists, classes, and dictionaries, and use them to write and test your code. Python Forensics: A Workbench for Inventing and Sharing Digital Forensic Technology By Chet Hosmer ISBN-10: ...

Author: Chet Hosmer

Publisher: Syngress

ISBN: 9780128099506

Category: Computers

Page: 216

View: 901

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Integrating Python with Leading Computer Forensic Platforms takes a definitive look at how and why the integration of Python advances the field of digital forensics. In addition, the book includes practical, never seen Python examples that can be immediately put to use. Noted author Chet Hosmer demonstrates how to extend four key Forensic Platforms using Python, including EnCase by Guidance Software, MPE+ by AccessData, The Open Source Autopsy/SleuthKit by Brian Carrier and WetStone Technologies, and Live Acquisition and Triage Tool US-LATT. This book is for practitioners, forensic investigators, educators, students, private investigators, or anyone advancing digital forensics for investigating cybercrime. Additionally, the open source availability of the examples allows for sharing and growth within the industry. This book is the first to provide details on how to directly integrate Python into key forensic platforms. Provides hands-on tools, code samples, detailed instruction, and documentation that can be immediately put to use Shows how to integrate Python with popular digital forensic platforms, including EnCase, MPE+, The Open Source Autopsy/SleuthKit, and US-LATT Presents complete coverage of how to use Open Source Python scripts to extend and modify popular digital forensic Platforms

Python Digital Forensics

"The course starts with network forensics, an important aspect of any investigation.

Author: Daryl Bennett

Publisher:

ISBN: OCLC:1137152815

Category:

Page:

View: 594

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"The course starts with network forensics, an important aspect of any investigation. You will learn to read, sort, and sniff raw packets and also analyze network traffic. These techniques will help you drive your host analysis. You will learn about tools you'll need to perform a complete investigation with the utmost efficiency in both Windows and GNU/Linux environments with Python. Next, you will learn more advanced topics such as viewing data in PE and ELF binaries. It's vital to analyze volatile memory during an investigation as it provides details about what is actually running on a given system. So, you will learn the best tools to obtain and analyze volatile memory images. Finally, you will learn how to use Python in order to think like an attacker. You will complete enumeration, exploitation, and data exfiltration. By the end of the course, you will be able to make the most of Python processes and tackle varied, challenging, forensics-related problems. So, grab this course and think like an attacker!"--Resource description page.

Python Forensics

Python. Forensics? 1. CHAPTER CONTENTS Introduction . ... The Python programming language and environment has proven to be easy to learn and use and is adaptable to virtually any domain or challenge problem.

Author: Chet Hosmer

Publisher: Elsevier

ISBN: 9780124186835

Category: Computers

Page: 352

View: 372

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Python Forensics provides many never-before-published proven forensic modules, libraries, and solutions that can be used right out of the box. In addition, detailed instruction and documentation provided with the code samples will allow even novice Python programmers to add their own unique twists or use the models presented to build new solutions. Rapid development of new cybercrime investigation tools is an essential ingredient in virtually every case and environment. Whether you are performing post-mortem investigation, executing live triage, extracting evidence from mobile devices or cloud services, or you are collecting and processing evidence from a network, Python forensic implementations can fill in the gaps. Drawing upon years of practical experience and using numerous examples and illustrative code samples, author Chet Hosmer discusses how to: Develop new forensic solutions independent of large vendor software release schedules Participate in an open-source workbench that facilitates direct involvement in the design and implementation of new methods that augment or replace existing tools Advance your career by creating new solutions along with the construction of cutting-edge automation solutions to solve old problems Provides hands-on tools, code samples, and detailed instruction and documentation that can be put to use immediately Discusses how to create a Python forensics workbench Covers effective forensic searching and indexing using Python Shows how to use Python to examine mobile device operating systems: iOS, Android, and Windows 8 Presents complete coverage of how to use Python scripts for network investigation

IMPLEMENTASI MACHINE LEARNING DENGAN PYTHON GUI

Buku ini merupakan versi bahasa Indonesia dari buku kami yang berjudul “LEARN FROM SCRATCH MACHINE LEARNING WITH PYTHON GUI”.

Author: Vivian Siahaan

Publisher: BALIGE PUBLISHING

ISBN:

Category: Computers

Page: 651

View: 432

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Buku ini merupakan versi bahasa Indonesia dari buku kami yang berjudul “LEARN FROM SCRATCH MACHINE LEARNING WITH PYTHON GUI”. Anda bisa mengaksesnya di Amazon maupun di Google Books. Pada buku ini, Anda akan mempelajari cara menggunakan NumPy, Pandas, OpenCV, Scikit-Learn, dan pustaka lain untuk memplot grafik dan memproses citra digital. Kemudian, Anda akan mempelajari cara mengklasifikasikan fitur menggunakan model Perceptron, Adaline, Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), dan K-Nearest Neighbor (KNN). Anda juga akan belajar cara mengekstraksi fitur menggunakan algoritma Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel Principal Component Analysis (KPCA) dan menggunakannya dalam pembelajaran mesin (machine learning). Pada Bab 1, Anda akan mempelajari dasar-dasar penggunakan Python GUI dengan Qt Designer. Pada Bab 2, Anda akan mempelajari: Langkah-Langkah Menciptakan Grafik Garis Sederhana; Langkah-Langkah Menampilkan Grafik Garis dengan Python GUI: Bagian 1; Langkah-Langkah Menampilkan Grafik Garis dengan Python GUI: Bagian 2; Langkah-Langkah Menampilkan Dua atau Lebih Grafik pada Sumbu yang Sama; Langkah-Langkah Menciptakan Dua Sumbu pada Satu Canvas; Langkah-Langkah Menggunakan Dua Widget; Langkah-Langkah Menggunakan Dua Widget, Masing-Masing Memiliki Dua Sumbu; Langkah-Langkah Menggunakan Sumbu dengan Tingkat Keburaman Tertentu; Langkah-Langkah Memilih Warna Garis dari Combo Box; Langkah-Langkah Menghitung Fast Fourier Transform; Langkah-Langkah Menciptakan GUI untuk FFT; Langkah-Langkan Menciptakan GUI untuk FFT atas Sinyal-Sinyal Masukan Lain; Langkah-Langkah Menciptakan GUI untuk Sinyal Berderau; Langkah-Langkah Menciptakan GUI untuk Penapisan Sinyal Berderau; Langkah-Langkah Mencipakan GUI untuk Penapisan Sinyal Wav; Langkah-Langkah Mengkonversi Citra RGB Menjadi Keabuan; Langkah-Langkah Mengkonversi Citra RGB Menjadi Citra YUV; Langkah-Langkah Mengkonversi Citra RGB Menjadi Citra HSV; Langkah-Langkah Menapis Citra; Langkah-Langkah Menampilkan Histogram Citra ; Langkah-Langkah Menampilkan Histogram Citra Tertapis; Langkah-Langkah Menapis Citra: Memanfaatkan CheckBox; Langkah-Langkah Mengimplementasikan Ambang Batas Citra; dan Langkah-Langkah Mengimplementasikan Ambang Batas Adaptif. Pada Bab 3, Anda akan mempelajari: Langkah-Langkah Implementasi Perceptron; Langkah-Langkah Implementasi Perceptron dengan PyQt; Langkah-Langkah Implementasi Adaline (ADAptive LInear NEuron); dan Langkah-Langkah Implementasi Adaline dengan PyQt. Pada Bab 4, Anda akan mempelajari: Langkah-Langkah Implementasi Perceptron Menggunakan Scikit-Learn dengan PyQt; Langkah-Langkah Implementasi Model Logistic Regression (LR); Langkah-Langkah Implementasi Model Logistic Regression dengan PyQt; Langkah-Langkah Implementasi Model Logistic Regression Menggunakan Scikit-Learn dengan PyQt; Langkah-Langkah Implementasi Mode Support Vector Machine (SVM) Menggunakan Scikit-Learn; Langkah-Langkah Implementasi Decision Tree (DT) Menggunakan Scikit-Learn; Langkah-Langkah Implementasi Model Random Forest (RF) Menggunakan Scikit-Learn; dan Langkah-Langkah Implementasi Model K-Nearest Neighbor (KNN) Menggunakan Scikit-Learn. Pada Bab 5, Anda akan mempelajari: Langkah-Langkah Implementasi Principal Component Analysis (PCA); Langkah-Langkah Implementasi Principal Component Analysis (PCA); Menggunakan Scikit-Learn; Langkah-Langkah Implementasi Principal Component Analysis (PCA) Menggunakan Scikit-Learn dengan PyQt; Langkah-Langkah Implementasi Linear Discriminant Analysis (LDA); Langkah-Langkah Implementasi Linear Discriminant Analysis (LDA) dengan scikit-learn; Langkah-Langkah Implementasi Linear Discriminant Analysis (LDA); Menggunakan Scikit-Learn dengan PyQt; Langkah-Langkah Implementasi Kernel Principal Component Analysis (KPCA) Menggunakan Scikit-Learn; dan Langkah-Langkah Implementasi Kernel Principal Component Analysis (KPCA) Menggunakan Scikit-Learn dengan PyQt. Pada Bab 6, Anda akan mempelajari: Langkah-Langkah Memuat Dataset MNIST; Langkah-Langkah Memuat Dataset MNIST dengan PyQt; Langkah-Langkah Implementasi Perceptron dengan Ekstraktor Fitur PCA pada Dataset MNIST Menggunakan PyQt; Langkah-Langkah Implementasi Perceptron dengan Ekstraktor Fitur LDA pada Dataset MNIST Menggunakan PyQt; Langkah-Langkah Implementasi Perceptron dengan Ekstraktor Fitur KPCA pada Dataset MNIST Menggunakan PyQt; Langkah-Langkah Implementasi Logistic Regression (LR) dengan Ekstraktor Fitur PCA pada Dataset MNIST Menggunakan PyQt; Langkah-Langkah Implementasi Logistic Regression (LR) dengan Ekstraktor Fitur LDA pada Dataset MNIST Menggunakan PyQt; Langkah-Langkah Implementasi Logistic Regression (LR) dengan Ekstraktor Fitur KPCA pada Dataset MNIST Menggunakan PyQt; Langkah-Langkah Implementasi Support Vector Machine (SVM) dengan Ekstraktor Fitur PCA pada Dataset MNIST Menggunakan PyQt; Langkah-Langkah Implementasi Support Vector Machine (SVM) dengan Ekstraktor Fitur LDA pada Dataset MNIST Menggunakan PyQt; Langkah-Langkah Implementasi Support Vector Machine (SVM) dengan Ekstraktor Fitur KPCA pada Dataset MNIST Menggunakan PyQt; Langkah-Langkah Implementasi Decision Tree (DT) dengan Ekstraktor Fitur PCA pada Dataset MNIST Menggunakan PyQt; Langkah-Langkah Implementasi Decision Tree (DT) dengan Ekstraktor Fitur LDA pada Dataset MNIST Menggunakan PyQt; Langkah-Langkah Implementasi Decision Tree (DT) dengan Ekstraktor Fitur KPCA pada Dataset MNIST Menggunakan PyQt; Langkah-Langkah Implementasi Random Forest (RF) dengan Ekstraktor Fitur PCA pada Dataset MNIST Menggunakan PyQt; Langkah-Langkah Implementasi Random Forest (RF) dengan Ekstraktor Fitur LDA pada Dataset MNIST Menggunakan PyQt; Langkah-Langkah Implementasi Random Forest (RF) dengan Ekstraktor Fitur KPCA pada Dataset MNIST Menggunakan PyQt; Langkah-Langkah Implementasi K-Nearest Neighbor (KNN) dengan Ekstraktor Fitur PCA pada Dataset MNIST Menggunakan PyQt; Langkah-Langkah Implementasi K-Nearest Neighbor (KNN) dengan Ekstraktor Fitur LDA pada Dataset MNIST Menggunakan PyQt; dan Langkah-Langkah Implementasi K-Nearest Neighbor (KNN) dengan Ekstraktor Fitur KPCA pada Dataset MNIST Menggunakan PyQt. Pada Bab 7, Anda akan mempelajari: Langkah-Langkah Membangkitkan dan Menampilkan Citra Berderau; Langkah-Langkah Mengimplemantasikan Deteksi Tepi pada Citra; Langkah-Langkah Mengimplementasikan Segmentasi Menggunakan Ambang Batas Jamak dan Algoritma K-Means; Langkah-Langkah Mengimplementasikan Penekanan Derau pada Citra; Langkah-Langkah Mendeteksi Wajah, Mata, dan Mulut dengan Haar Cascades; Langkah-Langkah Mendeteksi Wajah Menggunakan Haar Cascades dengan PyQt; Langkah-Langkah Mendeteksi Mata dan Mulut Menggunakan Haar Cascades dengan PyQt; Langkah-Langkah Mengekstraksi Objek-Objek Terdeteksi; Langkah-Langkah Mendeteksi Fitur Citra dengan Harris Corner Detection; Langkah-Langkah Mendeteksi Fitur Citra dengan Shi-Tomasi Corner Detection; Langkah-Langkah Mendeteksi Fitur Citra dengan Scale-Invariant Feature Transform (SIFT) ; dan Langkah-Langkah Mendeteksi Fitur Citra dengan Accelerated Segment Test (FAST).

LEARN FROM SCRATCH MACHINE LEARNING WITH PYTHON GUI

In this book, you will learn how to use NumPy, Pandas, OpenCV, Scikit-Learn and other libraries to how to plot graph and to process digital image.

Author: Vivian Siahaan

Publisher: BALIGE PUBLISHING

ISBN:

Category: Computers

Page: 624

View: 601

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In this book, you will learn how to use NumPy, Pandas, OpenCV, Scikit-Learn and other libraries to how to plot graph and to process digital image. Then, you will learn how to classify features using Perceptron, Adaline, Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN) models. You will also learn how to extract features using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel Principal Component Analysis (KPCA) algorithms and use them in machine learning. In Chapter 1, you will learn: Tutorial Steps To Create A Simple GUI Application, Tutorial Steps to Use Radio Button, Tutorial Steps to Group Radio Buttons, Tutorial Steps to Use CheckBox Widget, Tutorial Steps to Use Two CheckBox Groups, Tutorial Steps to Understand Signals and Slots, Tutorial Steps to Convert Data Types, Tutorial Steps to Use Spin Box Widget, Tutorial Steps to Use ScrollBar and Slider, Tutorial Steps to Use List Widget, Tutorial Steps to Select Multiple List Items in One List Widget and Display It in Another List Widget, Tutorial Steps to Insert Item into List Widget, Tutorial Steps to Use Operations on Widget List, Tutorial Steps to Use Combo Box, Tutorial Steps to Use Calendar Widget and Date Edit, and Tutorial Steps to Use Table Widget. In Chapter 2, you will learn: Tutorial Steps To Create A Simple Line Graph, Tutorial Steps To Create A Simple Line Graph in Python GUI, Tutorial Steps To Create A Simple Line Graph in Python GUI: Part 2, Tutorial Steps To Create Two or More Graphs in the Same Axis, Tutorial Steps To Create Two Axes in One Canvas, Tutorial Steps To Use Two Widgets, Tutorial Steps To Use Two Widgets, Each of Which Has Two Axes, Tutorial Steps To Use Axes With Certain Opacity Levels, Tutorial Steps To Choose Line Color From Combo Box, Tutorial Steps To Calculate Fast Fourier Transform, Tutorial Steps To Create GUI For FFT, Tutorial Steps To Create GUI For FFT With Some Other Input Signals, Tutorial Steps To Create GUI For Noisy Signal, Tutorial Steps To Create GUI For Noisy Signal Filtering, and Tutorial Steps To Create GUI For Wav Signal Filtering. In Chapter 3, you will learn: Tutorial Steps To Convert RGB Image Into Grayscale, Tutorial Steps To Convert RGB Image Into YUV Image, Tutorial Steps To Convert RGB Image Into HSV Image, Tutorial Steps To Filter Image, Tutorial Steps To Display Image Histogram, Tutorial Steps To Display Filtered Image Histogram, Tutorial Steps To Filter Image With CheckBoxes, Tutorial Steps To Implement Image Thresholding, and Tutorial Steps To Implement Adaptive Image Thresholding. You will also learn: Tutorial Steps To Generate And Display Noisy Image, Tutorial Steps To Implement Edge Detection On Image, Tutorial Steps To Implement Image Segmentation Using Multiple Thresholding and K-Means Algorithm, Tutorial Steps To Implement Image Denoising, Tutorial Steps To Detect Face, Eye, and Mouth Using Haar Cascades, Tutorial Steps To Detect Face Using Haar Cascades with PyQt, Tutorial Steps To Detect Eye, and Mouth Using Haar Cascades with PyQt, Tutorial Steps To Extract Detected Objects, Tutorial Steps To Detect Image Features Using Harris Corner Detection, Tutorial Steps To Detect Image Features Using Shi-Tomasi Corner Detection, Tutorial Steps To Detect Features Using Scale-Invariant Feature Transform (SIFT), and Tutorial Steps To Detect Features Using Features from Accelerated Segment Test (FAST). In Chapter 4, In this tutorial, you will learn how to use Pandas, NumPy and other libraries to perform simple classification using perceptron and Adaline (adaptive linear neuron). The dataset used is Iris dataset directly from the UCI Machine Learning Repository. You will learn: Tutorial Steps To Implement Perceptron, Tutorial Steps To Implement Perceptron with PyQt, Tutorial Steps To Implement Adaline (ADAptive LInear NEuron), and Tutorial Steps To Implement Adaline with PyQt. In Chapter 5, you will learn how to use the scikit-learn machine learning library, which provides a wide variety of machine learning algorithms via a user-friendly Python API and to perform classification using perceptron, Adaline (adaptive linear neuron), and other models. The dataset used is Iris dataset directly from the UCI Machine Learning Repository. You will learn: Tutorial Steps To Implement Perceptron Using Scikit-Learn, Tutorial Steps To Implement Perceptron Using Scikit-Learn with PyQt, Tutorial Steps To Implement Logistic Regression Model, Tutorial Steps To Implement Logistic Regression Model with PyQt, Tutorial Steps To Implement Logistic Regression Model Using Scikit-Learn with PyQt, Tutorial Steps To Implement Support Vector Machine (SVM) Using Scikit-Learn, Tutorial Steps To Implement Decision Tree (DT) Using Scikit-Learn, Tutorial Steps To Implement Random Forest (RF) Using Scikit-Learn, and Tutorial Steps To Implement K-Nearest Neighbor (KNN) Using Scikit-Learn. In Chapter 6, you will learn how to use Pandas, NumPy, Scikit-Learn, and other libraries to implement different approaches for reducing the dimensionality of a dataset using different feature selection techniques. You will learn about three fundamental techniques that will help us to summarize the information content of a dataset by transforming it onto a new feature subspace of lower dimensionality than the original one. Data compression is an important topic in machine learning, and it helps us to store and analyze the increasing amounts of data that are produced and collected in the modern age of technology. You will learn the following topics: Principal Component Analysis (PCA) for unsupervised data compression, Linear Discriminant Analysis (LDA) as a supervised dimensionality reduction technique for maximizing class separability, Nonlinear dimensionality reduction via Kernel Principal Component Analysis (KPCA). You will learn: 6.1 Tutorial Steps To Implement Principal Component Analysis (PCA), Tutorial Steps To Implement Principal Component Analysis (PCA) Using Scikit-Learn, Tutorial Steps To Implement Principal Component Analysis (PCA) Using Scikit-Learn with PyQt, Tutorial Steps To Implement Linear Discriminant Analysis (LDA), Tutorial Steps To Implement Linear Discriminant Analysis (LDA) with Scikit-Learn, Tutorial Steps To Implement Linear Discriminant Analysis (LDA) Using Scikit-Learn with PyQt, Tutorial Steps To Implement Kernel Principal Component Analysis (KPCA) Using Scikit-Learn, and Tutorial Steps To Implement Kernel Principal Component Analysis (KPCA) Using Scikit-Learn with PyQt. In Chapter 7, you will learn how to use Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on handwritten digits using MNIST dataset. You will learn: Tutorial Steps To Load MNIST Dataset, Tutorial Steps To Load MNIST Dataset with PyQt, Tutorial Steps To Implement Perceptron With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Perceptron With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Perceptron With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement , Tutorial Steps To Implement Support Vector Machine (SVM) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Support Vector Machine (SVM) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, and Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt.

PowerShell and Python Together

What You’ll Learn Leverage the internals of PowerShell for: digital investigation, incident response, and forensics Leverage Python to exploit already existing PowerShell CmdLets and aliases to build new automation and analysis ...

Author: Chet Hosmer

Publisher: Apress

ISBN: 9781484245040

Category: Computers

Page: 216

View: 217

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Bring together the Python programming language and Microsoft’s PowerShell to address digital investigations and create state-of-the-art solutions for administrators, IT personnel, cyber response teams, and forensic investigators. You will learn how to join PowerShell's robust set of commands and access to the internals of both the MS Windows desktop and enterprise devices and Python's rich scripting environment allowing for the rapid development of new tools for investigation, automation, and deep analysis. PowerShell and Python Together takes a practical approach that provides an entry point and level playing field for a wide range of individuals, small companies, researchers, academics, students, and hobbyists to participate. What You’ll Learn Leverage the internals of PowerShell for: digital investigation, incident response, and forensics Leverage Python to exploit already existing PowerShell CmdLets and aliases to build new automation and analysis capabilities Create combined PowerShell and Python applications that provide: rapid response capabilities to cybersecurity events, assistance in the precipitous collection of critical evidence (from the desktop and enterprise), and the ability to analyze, reason about, and respond to events and evidence collected across the enterprise Who This Book Is For System administrators, IT personnel, incident response teams, forensic investigators, professors teaching in undergraduate and graduate programs in cybersecurity, students in cybersecurity and computer science programs, and software developers and engineers developing new cybersecurity defenses

Python Programming

Python Programming is a great start for everyone who is interested in learning the Python language. This beginner's guide is full of all the needed information to get you familiar with Python.

Author: David Yang

Publisher: Createspace Independent Publishing Platform

ISBN: 154701170X

Category:

Page: 120

View: 620

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Python Programming is a great start for everyone who is interested in learning the Python language. This beginner's guide is full of all the needed information to get you familiar with Python. If you've been thinking about digging into Python, Python Programming will get you up to speed and have you writing code in no time! As you work through Python Programming, you will learn: - What is Python? - Installing Python on Windows and Mac Systems - Setting up an environment with Python - Syntax used with Python - Python data types and variables - User-Defined functions of Python - Lists in Python - Tuples in Python - Working with strings in Python - Using Python to build a website - Active surveillance with Python - Passive forensics with Python - Packet sniffing with Python - Attacks using Man in the Middle methods

Learning iOS Forensics

Its usage is as simple as running a single-line command as follows: $ python sqlparse.py -f mmssms.db -r -o report.txt You can find it on her website and GitHub repository; she has also provided a GUI version of the tool (refer to ...

Author: Mattia Epifani

Publisher: Packt Publishing Ltd

ISBN: 9781785887680

Category: Computers

Page: 330

View: 255

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A practical guide to analyzing iOS devices with the latest forensics tools and techniques About This Book This book is a comprehensive update to Learning iOS Forensics This practical book will not only cover the critical aspects of digital forensics, but also mobile forensics Whether you're a forensic analyst or an iOS developer, there's something in this book for you The authors, Mattia Epifani and Pasquale Stirparo, are respected members of the community, they go into extensive detail to cover critical topics Who This Book Is For The book is for digital forensics analysts, incident response analysts, IT security experts, and malware analysts. It would be beneficial if you have basic knowledge of forensics What You Will Learn Identify an iOS device between various models (iPhone, iPad, iPod Touch) and verify the iOS version installed Crack or bypass the protection passcode chosen by the user Acquire, at the most detailed level, the content of an iOS Device (physical, advanced logical, or logical) Recover information from a local backup and eventually crack the backup password Download back-up information stored on iCloud Analyze system, user, and third-party information from a device, a backup, or iCloud Examine malicious apps to identify data and credential thefts In Detail Mobile forensics is used within many different domains, but is chiefly employed in the field of information security. By understanding common attack vectors and vulnerability points, security professionals can develop measures and examine system architectures to harden security on iOS devices. This book is a complete manual on the identification, acquisition, and analysis of iOS devices, updated to iOS 8 and 9. You will learn by doing, with various case studies. The book covers different devices, operating system, and apps. There is a completely renewed section on third-party apps with a detailed analysis of the most interesting artifacts. By investigating compromised devices, you can work out the identity of the attacker, as well as what was taken, when, why, where, and how the attack was conducted. Also you will learn in detail about data security and application security that can assist forensics investigators and application developers. It will take hands-on approach to solve complex problems of digital forensics as well as mobile forensics. Style and approach This book provides a step-by-step approach that will guide you through one topic at a time. This intuitive guide focuses on one key topic at a time. Building upon the acquired knowledge in each chapter, we will connect the fundamental theory and practical tips by illustrative visualizations and hands-on code examples.

Digital Forensics for Handheld Devices

must learn every aspect of the product and be able to demonstrate to potential customers in law enforcement, ... many vendors of forensic tools recommended that academics learn Python scripting to create their own applications or modify ...

Author: Eamon P. Doherty

Publisher: CRC Press

ISBN: 9781439898772

Category: Computers

Page: 336

View: 766

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Approximately 80 percent of the world’s population now owns a cell phone, which can hold evidence or contain logs about communications concerning a crime. Cameras, PDAs, and GPS devices can also contain information related to corporate policy infractions and crimes. Aimed to prepare investigators in the public and private sectors, Digital Forensics for Handheld Devices examines both the theoretical and practical aspects of investigating handheld digital devices. This book touches on all areas of mobile device forensics, including topics from the legal, technical, academic, and social aspects of the discipline. It provides guidance on how to seize data, examine it, and prepare it as evidence for court. This includes the use of chain of custody forms for seized evidence and Faraday Bags for digital devices to prevent further connectivity and tampering of evidence. Emphasizing the policies required in the work environment, the author provides readers with a clear understanding of the differences between a corporate investigation and a criminal investigation. The book also: Offers best practices for establishing an incident response policy and seizing data from company or privately owned digital devices Provides guidance in establishing dedicated examinations free of viruses, spyware, and connections to other devices that could taint evidence Supplies guidance on determining protocols for complicated crime scenes with external media and devices that may have connected with the handheld device Considering important privacy issues and the Fourth Amendment, this book facilitates an understanding of how to use digital forensic tools to investigate the complete range of available digital devices, including flash drives, cell phones, PDAs, digital cameras, and netbooks. It includes examples of commercially available digital forensic tools and ends with a discussion of the education and certifications required for various careers in mobile device forensics.

Violent Python

This book demonstrates how to write Python scripts to automate large-scale network attacks, extract metadata, and investigate forensic artifacts.

Author: TJ O'Connor

Publisher: Syngress

ISBN: 1597499579

Category: Computers

Page: 288

View: 498

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Violent Python shows you how to move from a theoretical understanding of offensive computing concepts to a practical implementation. Instead of relying on another attacker's tools, this book will teach you to forge your own weapons using the Python programming language. This book demonstrates how to write Python scripts to automate large-scale network attacks, extract metadata, and investigate forensic artifacts. It also shows how to write code to intercept and analyze network traffic using Python, craft and spoof wireless frames to attack wireless and Bluetooth devices, and how to data-mine popular social media websites and evade modern anti-virus. Demonstrates how to write Python scripts to automate large-scale network attacks, extract metadata, and investigate forensic artifacts Write code to intercept and analyze network traffic using Python. Craft and spoof wireless frames to attack wireless and Bluetooth devices Data-mine popular social media websites and evade modern anti-virus

Learning Android Forensics

The following is a simple example of Python code that will perform this function: from subprocess import Popen from os import getcwd command = "adb pull /data/data " + getcwd() + "\data_from_device" p = Popen(command) p.communicate() ...

Author: Oleg Skulkin

Publisher: Packt Publishing Ltd

ISBN: 9781789137491

Category: Computers

Page: 328

View: 192

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A comprehensive guide to Android forensics, from setting up the workstation to analyzing key artifacts Key Features Get up and running with modern mobile forensic strategies and techniques Analyze the most popular Android applications using free and open source forensic tools Learn malware detection and analysis techniques to investigate mobile cybersecurity incidents Book Description Many forensic examiners rely on commercial, push-button tools to retrieve and analyze data, even though there is no tool that does either of these jobs perfectly. Learning Android Forensics will introduce you to the most up-to-date Android platform and its architecture, and provide a high-level overview of what Android forensics entails. You will understand how data is stored on Android devices and how to set up a digital forensic examination environment. As you make your way through the chapters, you will work through various physical and logical techniques to extract data from devices in order to obtain forensic evidence. You will also learn how to recover deleted data and forensically analyze application data with the help of various open source and commercial tools. In the concluding chapters, you will explore malware analysis so that you’ll be able to investigate cybersecurity incidents involving Android malware. By the end of this book, you will have a complete understanding of the Android forensic process, you will have explored open source and commercial forensic tools, and will have basic skills of Android malware identification and analysis. What you will learn Understand Android OS and architecture Set up a forensics environment for Android analysis Perform logical and physical data extractions Learn to recover deleted data Explore how to analyze application data Identify malware on Android devices Analyze Android malware Who this book is for If you are a forensic analyst or an information security professional wanting to develop your knowledge of Android forensics, then this is the book for you. Some basic knowledge of the Android mobile platform is expected.

Learn Computer Forensics

The Base16 numbering system uses the alphanumeric characters 0–9 and A – F, while the Base32 numbering system uses that alphanumeric characters A – Z and 2 – 7. Chris Hurst posted how to use Python to convert ...

Author: William Oettinger

Publisher: Packt Publishing Ltd

ISBN: 9781838641092

Category: Computers

Page: 368

View: 506

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Get up and running with collecting evidence using forensics best practices to present your findings in judicial or administrative proceedings Key Features Learn the core techniques of computer forensics to acquire and secure digital evidence skillfully Conduct a digital forensic examination and document the digital evidence collected Analyze security systems and overcome complex challenges with a variety of forensic investigations Book Description A computer forensics investigator must possess a variety of skills, including the ability to answer legal questions, gather and document evidence, and prepare for an investigation. This book will help you get up and running with using digital forensic tools and techniques to investigate cybercrimes successfully. Starting with an overview of forensics and all the open source and commercial tools needed to get the job done, you'll learn core forensic practices for searching databases and analyzing data over networks, personal devices, and web applications. You'll then learn how to acquire valuable information from different places, such as filesystems, e-mails, browser histories, and search queries, and capture data remotely. As you advance, this book will guide you through implementing forensic techniques on multiple platforms, such as Windows, Linux, and macOS, to demonstrate how to recover valuable information as evidence. Finally, you'll get to grips with presenting your findings efficiently in judicial or administrative proceedings. By the end of this book, you'll have developed a clear understanding of how to acquire, analyze, and present digital evidence like a proficient computer forensics investigator. What you will learn Understand investigative processes, the rules of evidence, and ethical guidelines Recognize and document different types of computer hardware Understand the boot process covering BIOS, UEFI, and the boot sequence Validate forensic hardware and software Discover the locations of common Windows artifacts Document your findings using technically correct terminology Who this book is for If you're an IT beginner, student, or an investigator in the public or private sector this book is for you.This book will also help professionals and investigators who are new to incident response and digital forensics and interested in making a career in the cybersecurity domain.

Learning Python Networking

A complete guide to build and deploy strong networking capabilities using Python 3.7 and Ansible , 2nd Edition José ... Yakov Goldberg is a Masters-trained, InfoSec professional focusing on digital forensics, incident response (DFIR), ...

Author: José Manuel Ortega

Publisher: Packt Publishing Ltd

ISBN: 9781789952445

Category: Computers

Page: 490

View: 929

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Achieve improved network programmability and automation by leveraging powerful network programming concepts, algorithms, and tools Key Features Deal with remote network servers using SSH, FTP, SNMP and LDAP protocols. Design multi threaded and event-driven architectures for asynchronous servers programming. Leverage your Python programming skills to build powerful network applications Book Description Network programming has always been a demanding task. With full-featured and well-documented libraries all the way up the stack, Python makes network programming the enjoyable experience it should be. Starting with a walk through of today's major networking protocols, through this book, you'll learn how to employ Python for network programming, how to request and retrieve web resources, and how to extract data in major formats over the web. You will utilize Python for emailing using different protocols, and you'll interact with remote systems and IP and DNS networking. You will cover the connection of networking devices and configuration using Python 3.7, along with cloud-based network management tasks using Python. As the book progresses, socket programming will be covered, followed by how to design servers, and the pros and cons of multithreaded and event-driven architectures. You'll develop practical clientside applications, including web API clients, email clients, SSH, and FTP. These applications will also be implemented through existing web application frameworks. What you will learn Execute Python modules on networking tools Automate tasks regarding the analysis and extraction of information from a network Get to grips with asynchronous programming modules available in Python Get to grips with IP address manipulation modules using Python programming Understand the main frameworks available in Python that are focused on web application Manipulate IP addresses and perform CIDR calculations Who this book is for If you're a Python developer or a system administrator with Python experience and you're looking to take your first steps in network programming, then this book is for you. If you're a network engineer or a network professional aiming to be more productive and efficient in networking programmability and automation then this book would serve as a useful resource. Basic knowledge of Python is assumed.