Oscar Koller

40 papers A* 5A 5B 4C 2Misc 1Journal 12Unranked 10
YearRankTypeTitle / Venue / Authors
2023 conf
WMT
Mathias Müller, Malihe Alikhani, Eleftherios Avramidis, Richard Bowden, Annelies Braffort, Necati Cihan Camgöz, Sarah Ebling, Cristina España-Bonet, Anne Göhring, Roman Grundkiewicz, Mert Inan, Zifan Jiang, Oscar Koller, Amit Moryossef, Annette Rios, Dimitar Shterionov, Sandra Sidler-Miserez, Katja Tissi, Davy Van Landuyt
2023 C conf
EAMT
Mathias Müller, Sarah Ebling, Eleftherios Avramidis, Alessia Battisti, Michèle Berger, Richard Bowden, Annelies Braffort, Necati Cihan Camgöz, Cristina España-Bonet, Roman Grundkiewicz, Zifan Jiang, Oscar Koller, Amit Moryossef, Regula Perrollaz, Sabine Reinhard, Annette Rios Gonzales, Dimitar Shterionov, Sandra Sidler-Miserez, Katja Tissi, Davy Van Landuyt
2023 J jnl
CoRR
Abhilash Pal, Stephan Huber, Cyrine Chaabani, Alessandro Manzotti, Oscar Koller
2022 conf
WMT
Subhadeep Dey, Abhilash Pal, Cyrine Chaabani, Oscar Koller
2022 J jnl
CoRR
Subhadeep Dey, Abhilash Pal, Cyrine Chaabani, Oscar Koller
2022 conf
WMT
Mathias Müller, Sarah Ebling, Eleftherios Avramidis, Alessia Battisti, Michèle Berger, Richard Bowden, Annelies Braffort, Necati Cihan Camgöz, Cristina España-Bonet, Roman Grundkiewicz, Zifan Jiang, Oscar Koller, Amit Moryossef, Regula Perrollaz, Sabine Reinhard, Annette Rios, Dimitar Shterionov, Sandra Sidler-Miserez, Katja Tissi
2021 conf
SwissText
Yuriy Arabskyy, Aashish Agarwal, Subhadeep Dey, Oscar Koller
2021 J jnl
CoRR
Yuriy Arabskyy, Aashish Agarwal, Subhadeep Dey, Oscar Koller
2021 J jnl
ACM Trans. Access. Comput.
Danielle Bragg, Naomi Caselli, Julie A. Hochgesang, Matt Huenerfauth, Leah Katz-Hernandez, Oscar Koller, Raja S. Kushalnagar, Christian Vogler, Richard E. Ladner
2020 A conf
ASSETS
Danielle Bragg, Oscar Koller, Naomi Caselli, William Thies
2020 conf
ECCV Workshops (4)
Necati Cihan Camgöz, Oscar Koller, Simon Hadfield, Richard Bowden
2020 J jnl
CoRR
Necati Cihan Camgöz, Oscar Koller, Simon Hadfield, Richard Bowden
2020 J jnl
CoRR
Oscar Koller
2020 A* conf
CVPR
Necati Cihan Camgöz, Oscar Koller, Simon Hadfield, Richard Bowden
2020 J jnl
CoRR
Necati Cihan Camgöz, Oscar Koller, Simon Hadfield, Richard Bowden
2020
Oscar Koller
2020 J jnl
IEEE Trans. Pattern Anal. Mach. Intell.
Oscar Koller, Necati Cihan Camgöz, Hermann Ney, Richard Bowden
2019 A conf
BMVC
Hamid Reza Vaezi Joze, Oscar Koller
2019 A conf
ASSETS
Danielle Bragg, Oscar Koller, Mary Bellard, Larwan Berke, Patrick Boudreault, Annelies Braffort, Naomi Caselli, Matt Huenerfauth, Hernisa Kacorri, Tessa Verhoef, Christian Vogler, Meredith Ringel Morris
2019 J jnl
CoRR
Danielle Bragg, Oscar Koller, Mary Bellard, Larwan Berke, Patrick Boudreault, Annelies Braffort, Naomi Caselli, Matt Huenerfauth, Hernisa Kacorri, Tessa Verhoef, Christian Vogler, Meredith Ringel Morris
2018 J jnl
Int. J. Comput. Vis.
Oscar Koller, Sepehr Zargaran, Hermann Ney, Richard Bowden
2018 J jnl
CoRR
Hamid Reza Vaezi Joze, Oscar Koller
2018 A* conf
CVPR
Necati Cihan Camgöz, Simon Hadfield, Oscar Koller, Hermann Ney, Richard Bowden
2017 A* conf
CVPR
Oscar Koller, Sepehr Zargaran, Hermann Ney
2017 A* conf
ICCV
Necati Cihan Camgöz, Simon Hadfield, Oscar Koller, Richard Bowden
2016 A* conf
CVPR
Oscar Koller, Hermann Ney, Richard Bowden
2016 A conf
BMVC
Oscar Koller, Sepehr Zargaran, Hermann Ney, Richard Bowden
2016 B conf
ICPR
Necati Cihan Camgöz, Simon Hadfield, Oscar Koller, Richard Bowden
2015 J jnl
Comput. Vis. Image Underst.
Oscar Koller, Jens Forster, Hermann Ney
2015 conf
ICCV Workshops
Oscar Koller, Hermann Ney, Richard Bowden
2014 B conf
LREC
Jens Forster, Christoph Schmidt, Oscar Koller, Martin Bellgardt, Hermann Ney
2014 conf
ECCV (1)
Oscar Koller, Hermann Ney, Richard Bowden
2013 conf
SLPAT
Jens Forster, Oscar Koller, Christian Oberdörfer, Yannick L. Gweth, Hermann Ney
2013 B conf
FG
Oscar Koller, Hermann Ney, Richard Bowden
2013 Misc conf
IbPRIA
Jens Forster, Christian Oberdörfer, Oscar Koller, Hermann Ney
2013 conf
IWSLT
Christoph Schmidt, Oscar Koller, Hermann Ney, Thomas Hoyoux, Justus H. Piater
2012 B conf
LREC
Jens Forster, Christoph Schmidt, Thomas Hoyoux, Oscar Koller, Uwe Zelle, Justus H. Piater, Hermann Ney
2011 C conf
ASRU
Luis Javier Rodríguez, Mikel Peñagarikano, Amparo Varona, Mireia Díez, Germán Bordel, David Martínez González, Jesús Antonio Villalba López, Antonio Miguel, Alfonso Ortega, Eduardo Lleida, Alberto Abad, Oscar Koller, Isabel Trancoso, Paula Lopez-Otero, Laura Docío Fernández, Carmen García-Mateo, Rahim Saeidi, Mehdi Soufifar, Tomi Kinnunen, Torbjørn Svendsen, Pasi Fränti
2010 conf
Odyssey
Oscar Koller, Alberto Abad, Isabel Trancoso
2010 A conf
INTERSPEECH
Oscar Koller, Alberto Abad, Isabel Trancoso, Céu Viana
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` |