Veronica Penza

17 papers A* 1A 3Journal 10Unranked 2
YearRankTypeTitle / Venue / Authors
2025 J jnl
Frontiers Robotics AI
Alberto Neri, Veronica Penza, Nazim Haouchine, Leonardo S. Mattos
2025 J jnl
Frontiers Digit. Health
Serena Ricci, Veronica Penza, Francesco Neri
2025 J jnl
CoRR
Alberto Neri, Maximilian Fehrentz, Veronica Penza, Leonardo S. Mattos, Nazim Haouchine
2025 J jnl
Comput. Medical Imaging Graph.
Alberto Neri, Veronica Penza, Chiara Baldini, Leonardo S. Mattos
2025 J jnl
Int. J. Comput. Assist. Radiol. Surg.
Alberto Neri, Maximilian Fehrentz, Veronica Penza, Leonardo S. Mattos, Nazim Haouchine
2025 conf
MICCAI (9)
Alberto Neri, Nazim Haouchine, Veronica Penza, Leonardo S. Mattos
2024 J jnl
Frontiers Digit. Health
Alberto Neri, Mara Coduri, Veronica Penza, Andrea Santangelo, Alessandra Oliveri, Enrico Turco, Mattia Pizzirani, Elisa Trinceri, Domenico Soriero, Federico Boero, Serena Ricci, Leonardo S. Mattos
2023 A conf
IROS
Veronica Penza, Alberto Neri, Maria Koskinopoulou, Enrico Turco, Domenico Soriero, Stefano Scabini, Domenico Prattichizzo, Leonardo S. Mattos
2023 A* conf
ICRA
Maria Koskinopoulou, Alperen Acemoglu, Veronica Penza, Leonardo S. Mattos
2021 A conf
IROS
Andre A. Geraldes, Veronica Penza, Leonardo S. Mattos
2021 J jnl
Frontiers Robotics AI
Leonardo S. Mattos, Alperen Acemoglu, Andre A. Geraldes, Andrea Laborai, Andreas Schoob, Brahim Tamadazte, Brian Davies, Bruno Wacogne, Christian Pieralli, Corina Barbalata, Darwin G. Caldwell, Dennis Kundrat, Diego Pardo, Edward Grant, Francesco Mora, Giacinto Barresi, Giorgio Peretti, Jesús Ortiz, Kanty Rabenorosoa, Laurent Tavernier, Lionel Pazart, Loris Fichera, Luca Guastini, Lüder A. Kahrs, Micky Rakotondrabe, Nicolas Andreff, Nikhil Deshpande, Olivier Gaiffe, Rupert Renevier, Sara Moccia, Sergio Lescano, Tobias Ortmaier, Veronica Penza
2019 A conf
IROS
Veronica Penza, Damiano Salerno, Alperen Acemoglu, Jesús Ortiz, Leonardo S. Mattos
2018 J jnl
Medical Image Anal.
Veronica Penza, Xiaofei Du, Danail Stoyanov, Antonello Forgione, Leonardo S. Mattos, Elena De Momi
2017 J jnl
Frontiers Robotics AI
Veronica Penza, Elena De Momi, Nima Enayati, Thibaud Chupin, Jesús Ortiz, Leonardo S. Mattos
2017
Veronica Penza
2016 conf
EMBC
Sara Moccia, Veronica Penza, Gabriele Omodeo Vanone, Elena De Momi, Leonardo S. Mattos
2016 J jnl
Int. J. Comput. Assist. Radiol. Surg.
Veronica Penza, Jesús Ortiz, Leonardo S. Mattos, Antonello Forgione, Elena De Momi
yara/README.md
← Index yara/README.md markdown
# YARA Rules Directory

This folder contains YARA rules for scanning binary samples.

## Setting Up YARA-Forge Rules

To use the YARA-Forge rules from [https://github.com/YARAHQ/yara-forge](https://github.com/YARAHQ/yara-forge):

```bash
# Download the latest release
cd /path/to/redb/yara
# wget https://github.com/YARAHQ/yara-forge/releases/latest/download/yara-forge-rules-core.zip
wget https://github.com/YARAHQ/yara-forge/releases/latest/download/yara-forge-rules-extended.zip

# Extract rules
# unzip yara-forge-rules-core.zip
unzip yara-forge-rules-extended.zip
```

Available packages:
- `yara-forge-rules-core.zip` - Core rules (~5,000 rules)
- `yara-forge-rules-extended.zip` - Extended rules (~10,000 rules)
- `yara-forge-rules-full.zip` - Full rules (~11,000+ rules)

## Pre-compiling Rules (Recommended for Production)

For large rulesets like YARA-Forge, pre-compiling rules significantly improves startup time:

```bash
# Pre-compile all rules into a single .yarac file
python -m redb.extractors.yara --compile

# Or specify custom paths
python -m redb.extractors.yara --compile --rules-path /path/to/rules --output /path/to/output.yarac
```

This creates `yara/compiled_rules.yarac` which is loaded automatically on subsequent runs.

### Performance Comparison

| Method | First Scan Startup | Subsequent Scans |
|--------|-------------------|------------------|
| Source files (.yar) | ~10-30 seconds (11k rules) | Instant (cached) |
| Pre-compiled (.yarac) | ~1-2 seconds | Instant (cached) |

## Directory Structure

```
yara/
├── README.md
├── .gitkeep
├── compiled_rules.yarac    # (optional) Pre-compiled rules
├── packages/               # YARA-Forge packages
│   └── core/
│       └── *.yar
└── custom/                 # Your custom rules
    └── my_rules.yar
```

Rules are loaded in this priority:
1. `compiled_rules.yarac` (if exists) - fastest
2. All `.yar` and `.yara` files recursively - compiles on first run

## Usage

### Scan with YARA only

```bash
# Scan local files
python start.py --path /path/to/samples -y --repo my_repo --index_prefix redb

# Scan S3 samples
python start.py --s3 --repo bazaar -y --index_prefix redb

# Dry-run (print results instead of storing in ClickHouse)
python start.py --path /path/to/samples -y --dry-run --repo test --index_prefix redb
```

### Scan already-analyzed samples

Run YARA on samples that were previously analyzed (already in `basic_properties`).
Deduplication is handled by the `yara_matches` table — samples already scanned are
automatically excluded before processing begins:

```bash
# Scan all analyzed macho samples with YARA
python start.py --analyzed --magika macho -y --index_prefix redb

# Scan all analyzed PE samples with YARA
python start.py --analyzed --magika pe -y --index_prefix redb

# Scan all analyzed samples (no filetype filter)
python start.py --analyzed -y --index_prefix redb
```

### Partition large YARA runs by date

Combine `--analyzed` with `--range` to partition millions of samples into
manageable batches. Only samples in `basic_properties` AND within the date
range (by `first_seen` in `catalog_samples`) are processed:

```bash
# Scan analyzed PE samples from Feb 2025
python start.py --range 2025-02-01 2025-02-28 --analyzed --magika pebin -y --index_prefix redb

# Scan analyzed PE samples from first week of March 2025
python start.py --range 2025-03-01 2025-03-08 --analyzed --magika pebin -y --index_prefix redb
```

YARA dedup still applies — re-running a range safely skips already-scanned samples.

### Combined Features + YARA

Run feature extraction and YARA scanning together on the same samples:

```bash
# Local files with features + YARA
python start.py --path /path/to/samples --with-yara --repo my_repo --index_prefix redb

# S3 samples with features + YARA
python start.py --s3 --repo bazaar --with-yara --index_prefix redb
```

### Pre-compile Rules

```bash
# Compile and save to default location (yara/compiled_rules.yarac)
python -m redb.extractors.yara --compile

# Compile with custom paths
python -m redb.extractors.yara --compile --rules-path ./my_rules --output ./compiled.yarac
```

### Sync Rules to Database

Before batch scanning, sync rules to ensure all rule metadata is stored:

```bash
# Sync rules to database
python -m redb.extractors.yara --sync-rules

# Sync with custom source collection name
python -m redb.extractors.yara --sync-rules --source-collection yara-forge-core

# Compile and sync in one command
python -m redb.extractors.yara --compile --sync-rules
```

## ClickHouse Table Schema

YARA data uses a **normalized schema** with two tables for efficient storage.

### Matches Table: `yara_matches`

Stores one row per sample-rule match (optimized with binary sha256 and rule_id):

| Column | Type | Description |
|--------|------|-------------|
| sha256 | FixedString(32) | Binary SHA256 (32 bytes, use `hex(sha256)` to display) |
| rule_id | UInt64 | Unique rule identifier (xxHash64 of canonical rule content) |
| rule_name | LowCardinality(String) | YARA rule name (denormalized for convenience) |
| scan_date | DateTime64(3, 'UTC') | Scan timestamp |
| match_strings | Array(String) | Matched string identifiers |

### Rules Table: `yara_rules`

Stores rule metadata once per unique rule (deduplicated by rule_id):

| Column | Type | Description |
|--------|------|-------------|
| rule_id | UInt64 | Unique rule identifier (xxHash64 of canonical rule content) |
| rule_name | String | YARA rule name |
| source_collection | LowCardinality(String) | Source collection (e.g., 'yara-forge-core', 'malpedia') |
| ingested_at | DateTime64(3, 'UTC') | When this rule was ingested |
| rule_text | String | Full rule source code |
| rule_meta | JSON | Rule metadata (author, description, reference, etc.) |
| rule_tags | Array(LowCardinality(String)) | Rule tags |

### Schema Benefits

- **Binary SHA256**: 32 bytes vs 64 bytes (50% storage savings on hash columns)
- **UInt64 rule_id**: Fast joins and lookups via integer key
- **Content-based rule_id**: xxHash64 of canonical rule content (excluding metadata) for deduplication
- **Denormalized rule_name**: Allows queries without joins for common use cases

### Example Queries

```sql
-- Get matches with hex sha256
SELECT
    hex(m.sha256) as sha256,
    m.rule_name,
    m.match_strings
FROM yara_matches m
WHERE m.sha256 = unhex('abc123...')

-- Join with rules for full metadata
SELECT
    hex(m.sha256) as sha256,
    m.rule_name,
    m.match_strings,
    r.rule_meta,
    r.source_collection
FROM yara_matches m
JOIN yara_rules r ON m.rule_id = r.rule_id
WHERE m.sha256 = unhex('abc123...')

-- Find all samples matching a specific rule
SELECT hex(sha256), scan_date
FROM yara_matches
WHERE rule_name = 'APT_Lazarus_Loader'
ORDER BY scan_date DESC
```

## Environment Variables

| Variable | Description | Default |
|----------|-------------|---------|
| `YARA_RULES_PATH` | Override the YARA rules directory | `yara/` |
| `YARA_COMPILED_RULES` | Compiled rules filename | `compiled_rules.yarac` |
| `YARA_SOURCE_COLLECTION` | Default source collection name | `default` |