Kash Khorasani

35 papers A* 1B 2C 8Misc 1Journal 7Unranked 16
YearRankTypeTitle / Venue / Authors
2014 J jnl
Inf. Sci.
Zakieh Nasim Sadough Vanini, Kash Khorasani, Nader Meskin
2014 conf
ECC
Mohammad Reza Davoodi, Nader Meskin, Kash Khorasani
2013 conf
CICA
Farzad Baghernezhad, Kash Khorasani
2012 conf
CCECE
Iman Saboori, Kash Khorasani
2012 C conf
IECON
Maryam Etezad, Mojtaba Kahrizi, Kash Khorasani
2012 conf
CCECE
Seyed Sina Tayarani-Bathaie, Zakieh Nasim Sadough Vanini, Kash Khorasani
2012 conf
CCECE
Seyed Hossein Nayyeri, Kash Khorasani
2012 J jnl
Autom.
Mani M. Tousi, Kash Khorasani
2011 J jnl
IEEE Trans. Control. Syst. Technol.
Farzaneh Abdollahi, Kash Khorasani
2011 conf
CDC/ECC
Rui Ru Chen, Kash Khorasani
2011 conf
CDC/ECC
Rui Ru Chen, Kash Khorasani
2011 J jnl
Autom.
Rui Ru Chen, Kash Khorasani
2011 J jnl
Int. J. Comput. Sci. Appl.
Nicolae Tudoroiu, Eshan Sobhani-Tehrani, Kash Khorasani, Tiberiu Letia, Roxana-Elena Tudoroiu
2010 conf
CDC
Seyyedmohsen Azizi, Kash Khorasani
2010 conf
IMCSIT
Nicolae Tudoroiu, Eshan Sobhani-Tehrani, Kash Khorasani, Tiberiu Letia, Roxana-Elena Tudoroiu
2009 C conf
ACC
Seyyedmohsen Azizi, Kash Khorasani
2009 C conf
ACC
Farzaneh Abdollahi, Kash Khorasani
2009 conf
CCECE
Seyyedmohsen Azizi, Mani M. Tousi, Kash Khorasani
2009 conf
CCECE
Mani M. Tousi, Seyyedmohsen Azizi, Kash Khorasani
2009 C conf
ACC
Elham Semsar-Kazerooni, Kash Khorasani
2009 C conf
ACC
Elham Semsar-Kazerooni, Kash Khorasani
2009 C conf
ACC
Elham Semsar-Kazerooni, Kash Khorasani
2008 J jnl
IEEE J. Sel. Areas Commun.
Farzaneh Abdollahi, Kash Khorasani
2007 B conf
SMC
Hamed Azarnoush, Kash Khorasani
2007 B conf
SMC
Farzaneh Abdollahi, Kash Khorasani
2007 conf
CDC
Elham Semsar-Kazerooni, Kash Khorasani
2006 C conf
ACC
Elham Semsar, Kash Khorasani
2006 C conf
ACC
Kamal Bouyoucef, Kash Khorasani
2005 conf
CDC/ECC
Fei Gong, Kash Khorasani
2004 conf
RAM
Mehrdad Zadeh, Kash Khorasani
2003 conf
ECC
Nicolae Tudoroiu, Kash Khorasani, Valery D. Yurkevich
1997 J jnl
J. Field Robotics
Mehrdad Moallem, Rajni V. Patel, Kash Khorasani
1997 A* conf
ICRA
Mehrdad Moallem, Rajni V. Patel, Kash Khorasani
1997 conf
ICNN
A. Yazdizadeh, Kash Khorasani
1989 Misc conf
ICASSP
Mahmood R. Azimi-Sadjadi, Kash Khorasani
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` |