Skill UI

This comprehensive guide details the process of creating forensically sound, bit-for-bit disk images using industry-standard tools like `dd` and `dcfldd` on Linux systems. It covers essential steps including enabling write protection, documenting source metadata, performing the acquisition, and crucially, verifying the image integrity using cryptographic hashing (MD5/SHA-256). This methodology is vital for digital forensics, incident response, and legal proceedings to ensure evidence remains unaltered and admissible.

This guide outlines the process for forensically acquiring digital evidence from major cloud storage services, including Google Drive, OneDrive, Dropbox, and Box. The method combines API-based remote data extraction (such as audit logs and revision histories) with the collection of local endpoint sync-client artifacts. It is essential for conducting thorough incident response, security assessments, and e-discovery investigations involving cloud-synced data.

A comprehensive guide for conducting deep disk forensics investigations. This method covers secure evidence acquisition, creating bit-for-bit forensic images, analyzing file system structures (MFT, inodes), recovering deleted files, and reconstructing timelines using industry-standard tools like Autopsy, FTK Imager, and The Sleuth Kit. Essential for legal, HR, and security incident response.

This skill guides comprehensive digital forensics investigations on compromised endpoints. It covers evidence collection (memory acquisition, disk imaging) and deep artifact analysis, including volatile data, MFT records, and prefetch files. Use it for incident response, legal evidence collection, and determining the scope of a breach by reconstructing attacker timelines.

A comprehensive guide for authors targeting the International Conference on Knowledge Capture (K-CAP). It assists in positioning the manuscript, re-framing the contribution, and verifying the evidence bar. The tool emphasizes that the work must focus on knowledge acquisition, engineering, and operationalizing structured knowledge systems, rather than merely using them in a general context.