Xiaodan Fan

45 papers A* 1B 1Journal 37Unranked 6
YearRankTypeTitle / Venue / Authors
2026 J jnl
CoRR
Dongrong Li, Tianwei Yu, Xiaodan Fan
2026 J jnl
PLoS Comput. Biol.
Kai Liao, Danfeng Du, Jiawei Li, Jian Huang, Xiaodan Fan, Changshui Chen, Shanshan Wu, Bowei Yan, Haibo Li
2026 J jnl
Pattern Recognit.
Mengyu Li, Cheng Meng, Xiaodan Fan
2026 J jnl
Bioinform.
Yitao Xu, Guanyun Wei, Jingying Zhou, Yuanhua Huang, Weichuan Yu, Zhixiang Lin, Ran Liu, Xiaodan Fan
2025 J jnl
Quant. Biol.
Shanjun Mao, Chenyang Zhang, Runjiu Chen, Shan Tang, Xiaodan Fan, Jie Hu
2025 J jnl
J. Comput. Graph. Stat.
Lijun Wang, Hongyu Zhao, Xiaodan Fan
2025 J jnl
Bioinform.
Mengyu Li, Bencong Zhu, Cheng Meng, Xiaodan Fan
2025 J jnl
J. Comput. Graph. Stat.
Lijun Wang, Xiaodan Fan, Huabai Li, Jun S. Liu
2025 A* conf
EMNLP
Jiyue Jiang, Yitao Xu, Zikang Wang, Yihan Ye, Yanruisheng Shao, Yuheng Shan, Jiuming Wang, Xiaodan Fan, Jiao Yuan, Yu Li
2025 J jnl
Comput. Biol. Medicine
Kai Liao, Bowei Yan, Ziyin Ding, Jian Huang, Xiaodan Fan, Shanshan Wu, Changshui Chen, Haibo Li
2024 J jnl
Briefings Bioinform.
Bencong Zhu, Zhen Zhang, Suet Yi Leung, Xiaodan Fan
2023 J jnl
Briefings Bioinform.
Ran Liu, Ye-Fan Hu, Jian-Dong Huang, Xiaodan Fan
2023 J jnl
Briefings Bioinform.
Yetian Fan, April S. Chan, Jun Zhu, Suet Yi Leung, Xiaodan Fan
2023 J jnl
BMC Bioinform.
Jin Du, Chaojie Wang, Lijun Wang, Shanjun Mao, Bencong Zhu, Zheng Li, Xiaodan Fan
2023 J jnl
Commun. Stat. Simul. Comput.
Jie Hu, Zirui Chen, Wenwei Lin, Xiaodan Fan
2023 J jnl
IEEE ACM Trans. Comput. Biol. Bioinform.
Ran Liu, Ye-Fan Hu, Jin Du, Bao-Zhong Zhang, Thomas Yau, Xiaodan Fan, Jian-Dong Huang
2023 J jnl
Stat. Comput.
Maolin Pan, Minggao Gu, Xianyi Wu, Xiaodan Fan
2023 J jnl
IEEE ACM Trans. Comput. Biol. Bioinform.
Hangjin Jiang, Han Li, Wing Hung Wong, Xiaodan Fan
2022 J jnl
Mach. Learn.
Chaojie Wang, Jin Du, Xiaodan Fan
2021 J jnl
Comput. Stat. Data Anal.
Shanjun Mao, Xiaodan Fan, Jie Hu
2021 J jnl
PLoS Comput. Biol.
Zutan Li, Hangjin Jiang, Lingpeng Kong, Yuanyuan Chen, Kun Lang, Xiaodan Fan, Liangyun Zhang, Cong Pian
2020 J jnl
Int. J. Fuzzy Syst.
Xiaodan Fan, Kunting Yu
2020 J jnl
CoRR
Lijun Wang, Yanting Zhu, Jue Shi, Xiaodan Fan
2020 J jnl
Bioinform.
Jie Hu, Huihui Qin, Xiaodan Fan
2020 J jnl
Bioinform.
Cong Pian, Guang-Le Zhang, Fei Li, Xiaodan Fan
2020 J jnl
Bioinform.
Jiali Yang, Kun Lang, Guang-Le Zhang, Xiaodan Fan, Yuanyuan Chen, Cong Pian
2020 J jnl
Complex.
Zhen Zhang, Jin Du, Qingchun Meng, Xiaoxia Rong, Xiaodan Fan
2020 J jnl
Briefings Bioinform.
Cong Pian, Guang-Le Zhang, Libin Gao, Xiaodan Fan, Fei Li
2018 J jnl
IEEE ACM Trans. Comput. Biol. Bioinform.
Xiaodan Fan, Xinglai Ji, Rui Jiang
2017 J jnl
CoRR
Yunchuan Kong, Xiaodan Fan
2017 J jnl
Bioinform.
Qing Zhang, Xiaodan Fan, Yejun Wang, Ming-an Sun, Jianlin Shao, Dianjing Guo
2017 J jnl
Bioinform.
Linghao Shen, Jun Zhu, Shuo-Yen Robert Li, Xiaodan Fan
2016 conf
BIC-TA (2)
Shudong Wang, Shanqiang Zhang, Shanshan Li, Xinzeng Wang, Sicheng He, Yan Zhao, Xiaodan Fan, Fayou Yuan, Xinjie Zhu, Yun Jiang
2016 conf
BIBM
Guozhi Jiang, Claudia H. Tam, Andrea O. Y. Luk, Alice P. S. Kong, Wing-Yee So, Juliana C. N. Chan, Ronald Ching Wan Ma, Xiaodan Fan
2015 J jnl
IEEE ACM Trans. Comput. Biol. Bioinform.
Han Li, Chun Li, Jie Hu, Xiaodan Fan
2013 conf
BIOINFORMATICS
Guozhi Jiang, Eric S. Lau, Ying Wang, Andrea O. Y. Luk, Claudia H. Tam, Janice S. Ho, Vincent K. Lam, Heung M. Lee, Xiaodan Fan, Wing-Yee So, Juliana C. Chan, Ronald C. Ma
2013 J jnl
Int. J. Data Min. Bioinform.
Jin Xu, Qiwei Li, Victor O. K. Li, Shuo-Yen Robert Li, Xiaodan Fan
2012 B conf
IEEE Congress on Evolutionary Computation
Jin Xu, Albert Y. S. Lam, Victor O. K. Li, Qiwei Li, Xiaodan Fan
2011 J jnl
Bioinform.
Qiwei Li, Xiaodan Fan, Tong Liang, Shuo-Yen Robert Li
2010 conf
BIBM
Jin Xu, Qiwei Li, Xiaodan Fan, Victor O. K. Li, Shuo-Yen Robert Li
2010 conf
BIBM
Qiwei Li, Tong Liang, Xiaodan Fan, Chunhui Xu, Weichang Yu, Shuo-Yen Robert Li
2010 J jnl
CoRR
Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Xiaodan Fan, Nelson L. S. Tang, Weichuan Yu
2010 conf
BIOCOMP
Qiwei Li, Tong Liang, Shuo-Yen Robert Li, Xiaodan Fan
2007 J jnl
BMC Bioinform.
Xiaodan Fan, Jun Zhu, Eric E. Schadt, Jun S. Liu
2003 J jnl
Comput. Biol. Medicine
Ying-Fei Sun, Xiaodan Fan, Yan-Da Li
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` |