The presentation will discuss practical applications of Python in technologies used in forensic science. In an era of growing cybercrime and digitalization of evidence, Python has become an essential tool for forensics specialists, offering powerful libraries for data analysis, process automation and processing complex information structures. Participants will gain insights into spanning multiple domains of forensic investigation, including:
We will analyze how Python libraries parse SQLite databases in messaging applications like WhatsApp and iMessage, also examine tools for analyzing iOS file formats including PLIST and XML structures and techniques for extracting data from disk images using pytsk3 or libewf. Network analysis will showcase Scapy for packet analysis and Dpkt for parsing capture files, demonstrating how Python analyzes Windows Event Logs and Linux syslog to reconstruct activity timelines.
Memory forensics will be explored through the Volatility Framework for analyzing RAM dumps and recovering volatile data. We will discuss recovering deleted files, extracting metadata, and analyzing browser artifacts. Cryptographic analysis using hashlib will demonstrate integrity verification, encrypted file analysis, and cipher breaking techniques essential for maintaining chain of custody. Data visualization using Matplotlib, Seaborn, and NetworkX will show how to create compelling visual representations of timelines and connection networks. Automated report generation with ReportLab and python-docx streamlines professional expert report creation.
The presentation emphasizes real-world applications with dusscussion about Python scripts processing realistic datasets, illustrating how multiple Python tools integrate into comprehensive investigation workflows, demonstrating the synergistic effects of combining different analytical approaches for actionable forensic intelligence.
SQLite parsing for messaging apps, PLIST/XML for iOS artifacts, disk image extraction via pytsk3/libewf, packet analysis with Scapy, PCAP parsing with Dpkt, event log analysis for timelines, Volatility for RAM dumps, and automated PDF/DOCX reporting.
A clear picture of how Python modules can be combined into repeatable, evidence-preserving workflows that transform raw artifacts into verified findings and actionable forensic intelligence.