Python Digital Forensics Cookbook
Cód:
491_9781783987467
De: R$ 0,00Por: R$ 402,52ou X de
Over 60 recipes to help you learn digital forensics and leverage Python scripts to amplify your examinations Key Features 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. Book DescriptionTechnology 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. What you will learnUnderstand how Python can enhance digital forensics and investigationsLearn to access the contents of, and process, forensic evidence containersExplore malware through automated static analysisExtract and review message contents from a variety of email formatsAdd 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 databasesIndex large logs into a p
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