Mae Milano

23 papers A* 4A 2C 1Journal 10Unranked 4
YearRankTypeTitle / Venue / Authors
2025 A* conf
OSDI
Leon Schuermann, Jack Toubes, Tyler Potyondy, Pat Pannuto, Mae Milano, Amit Levy
2025 J jnl
Proc. ACM Program. Lang.
Shadaj Laddad, Alvin Cheung, Joseph M. Hellerstein, Mae Milano
2024 J jnl
CoRR
Shadaj Laddad, Alvin Cheung, Joseph M. Hellerstein, Mae Milano
2024 ed.
FARM
Mae Milano, Stephen Taylor
2024 conf
NFM
Ramana Nagasamudram, Lennart Beringer, Ken Birman, Mae Milano, David A. Naumann
2023 J jnl
Proc. ACM Program. Lang.
William Brandon, Benjamin Driscoll, Frank Dai, Wilson Berkow, Mae Milano
2023 conf
ApPLIED@PODC
Joseph M. Hellerstein, Shadaj Laddad, Mae Milano, Conor Power, Mingwei Samuel
2023 J jnl
CoRR
Joseph M. Hellerstein, Shadaj Laddad, Mae Milano, Conor Power, Mingwei Samuel
2023 C conf
SSS
Ken Birman, Sagar Jha, Mae Milano, Lorenzo Rosa, Weijia Song, Edward Tremel
2023 ed.
FARM
Mae Milano, John Leo
2022 A* conf
PLDI
Mae Milano, Joshua Turcotti, Andrew C. Myers
2022 J jnl
Proc. ACM Program. Lang.
Shadaj Laddad, Conor Power, Mae Milano, Alvin Cheung, Joseph M. Hellerstein
2022 J jnl
CoRR
Shadaj Laddad, Conor Power, Mae Milano, Alvin Cheung, Natacha Crooks, Joseph M. Hellerstein
2022 J jnl
Proc. VLDB Endow.
Shadaj Laddad, Conor Power, Mae Milano, Alvin Cheung, Natacha Crooks, Joseph M. Hellerstein
2022 J jnl
CoRR
Shadaj Laddad, Conor Power, Mae Milano, Alvin Cheung, Joseph M. Hellerstein
2021 A conf
CIDR
Alvin Cheung, Natacha Crooks, Joseph M. Hellerstein, Mae Milano
2021 J jnl
CoRR
Alvin Cheung, Natacha Crooks, Joseph M. Hellerstein, Mae Milano
2019 conf
SNAPL
Mae Milano, Rolph Recto, Tom Magrino, Andrew C. Myers
2018 J jnl
ACM Trans. Comput. Syst.
Sagar Jha, Jonathan Behrens, Theo Gkountouvas, Mae Milano, Weijia Song, Edward Tremel, Robbert van Renesse, Sydney Zink, Kenneth P. Birman
2018 A* conf
PLDI
Mae Milano, Andrew C. Myers
2017 conf
SoCC
Sagar Jha, Jonathan Behrens, Theo Gkountouvas, Mae Milano, Weijia Song, Edward Tremel, Sydney Zink, Ken Birman, Robbert van Renesse
2015 A* conf
POPL
Nate Foster, Dexter Kozen, Mae Milano, Alexandra Silva, Laure Thompson
2013 A conf
OOPSLA
Joe Gibbs Politz, Alejandro Martinez, Mae Milano, Sumner Warren, Daniel Patterson, Junsong Li, Anand Chitipothu, Shriram Krishnamurthi
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` |