Digital-forensics research developed in an academic context, extending the LEAPP family of tools (iLEAPP/ALEAPP/vLEAPP), open-source forensic artifact parsers for iOS, Android and device logs.
The contribution is a Python module, `callFrequent.py` (with the variant `callFrequent_aleapp.py`), that plugs into the tools' artifact directory and analyses the call history of mobile devices. The module reads the `.storedata` files from the device image, extracts the most frequently dialled and received numbers, and produces structured reports in HTML and TSV for later review and correlation.
The work demonstrates the full forensic cycle: acquisition of the device image, identification and parsing of the relevant artifact, and generation of readable evidence in an auditable format. It reuses the parsing and reporting infrastructure of the LEAPP tools, preserving licence compatibility and the existing execution flow.
device image
│
▼
┌───────────────────┐
│ .storedata │
│ files │
└─────────┬─────────┘
▼
┌───────────────────┐
│ callFrequent.py │
│ (LEAPP parser) │
└─────────┬─────────┘
▼
┌───────────────────┐
│ frequent calls │
│ → report │
│ HTML / TSV │
└───────────────────┘
This is a research and tooling project, not a client engagement.
