Xiang Tao

25 papers A* 3B 2Journal 13Unranked 7
YearRankTypeTitle / Venue / Authors
2026 J jnl
Pattern Recognit.
Qiang Liu, Xiang Tao, Liang Wang, Shu Wu, Liang Wang
2025 A* conf
ACM Multimedia
Hang Guo, Qing Zhang, Zixuan Gao, Siyuan Yang, Shulin Peng, Xiang Tao, Ting Yu, Yan Wang, Qingli Li
2025 J jnl
CoRR
Hang Guo, Qing Zhang, Zixuan Gao, Siyuan Yang, Shulin Peng, Xiang Tao, Ting Yu, Yan Wang, Qingli Li
2025 J jnl
Comput. Medical Imaging Graph.
Yiming Liu, Ling Zhang, Mingxue Gu, Yaoxing Xiao, Ting Yu, Xiang Tao, Qing Zhang, Yan Wang, Dinggang Shen, Qingli Li
2025 conf
ACL (1)
Mingqing Zhang, Qiang Liu, Xiang Tao, Shu Wu, Liang Wang
2025 J jnl
CoRR
Mingqing Zhang, Qiang Liu, Xiang Tao, Shu Wu, Liang Wang
2024 J jnl
Biomed. Signal Process. Control.
Yiming Liu, Xiang Tao, Yan Wang, Xia Jiang, Maxim Sergeevich Vonsky, Lubov Borisovna Mitrofanova, Qingli Li
2024 J jnl
CoRR
Liang Wang, Shu Wu, Qiang Liu, Yanqiao Zhu, Xiang Tao, Mengdi Zhang, Liang Wang
2024 J jnl
IEEE Trans. Knowl. Data Eng.
Liang Wang, Shu Wu, Qiang Liu, Yanqiao Zhu, Xiang Tao, Mengdi Zhang, Liang Wang
2024 J jnl
CoRR
Qiang Liu, Xiang Tao, Junfei Wu, Shu Wu, Liang Wang
2024 J jnl
CoRR
Xiang Tao, Mingqing Zhang, Qiang Liu, Shu Wu, Liang Wang
2024 A* conf
AAAI
Liang Wang, Xiang Tao, Qiang Liu, Shu Wu, Liang Wang
2024 J jnl
CoRR
Liang Wang, Xiang Tao, Qiang Liu, Shu Wu, Liang Wang
2024 conf
CISP-BMEI
Mingxue Gu, Yiming Liu, Yan Wang, Xiang Tao, Maxim Sergeevich Vonsky, Lubov B. Mitrofanova, Jiansheng Wang
2024 A* conf
WWW
Xiang Tao, Liang Wang, Qiang Liu, Shu Wu, Liang Wang
2024 J jnl
CoRR
Xiang Tao, Liang Wang, Qiang Liu, Shu Wu, Liang Wang
2021 conf
CISP-BMEI
Xintian Mao, Jiansheng Wang, Xiang Tao, Yan Wang, Qingli Li, Xiufeng Zhou, Yonghe Zhang
2020 J jnl
Sensors
Ting Wu, Hao Jin, Shurong Dong, Weipeng Xuan, Hongsheng Xu, Leihe Lu, Zijing Fang, Shuyi Huang, Xiang Tao, Lin Shi, Shuting Liu, Ji-Kui Luo
2020 J jnl
Nucleic Acids Res.
Chen Wang, Jian Yang, Hong Luo, Kun Wang, Yu Wang, Zhi-Xiong Xiao, Xiang Tao, Hao Jiang, Haoyang Cai
2017 conf
SmartCloud
Kedun Mao, Yongxin Zhu, Zhixiong Chen, Xiang Tao, Qixuan Xue, Han Wu, Yishu Mao, Junjie Hou
2015 conf
CSCloud
Xiang Tao, Yongxin Zhu, Yishu Mao, Han Song, Mengyun Liu, Xinyi Liu, Weiguang Sheng, Weiwei Shi
2015 conf
HPCC/CSS/ICESS
Weiwei Shi, Yongxin Zhu, Jinkui Zhang, Xiang Tao, Gehao Sheng, Yong Lian, Guoxing Wang, Yufeng Chen
2013 B conf
Intelligent Vehicles Symposium
Daobin Wang, Huawei Liang, Tao Mei, Hui Zhu, Jing Fu, Xiang Tao
2013 conf
WISE Workshops
Xiang Tao, Yongli Wang, Gongxuan Zhang
2012 B conf
Intelligent Vehicles Symposium
Tao Mei, Huawei Liang, Bin Kong, Jing Yang, Hui Zhu, Bichun Li, Jiajia Chen, Pan Zhao, Tiejuan Xu, Xiang Tao, Weizhong Zhang, Yan Song, Hu Wei, Jun Wang
Docker-README.md
← Index Docker-README.md markdown
# REDB Docker Setup

This document describes the Docker containerization for the REDB malware analysis framework.

## Overview

REDB has been containerized as a single unified image that supports both feature extraction and decompilation analysis. The container is stateless, processes files from S3 or local mounts, and exports results to ClickHouse database or via API callbacks.

## Architecture

- **Single Unified Container**: One image handles both feature extraction and decompilation
- **Runtime Tool Installation**: Tools (CAPA, DIE, Binary Ninja) installed at runtime from host snapshots
- **Stateless Processing**: No persistent storage required between runs
- **Multiple Invocation Modes**: Supports `--nomad-job`, `--s3`, `--s3-solo`, and `--path` modes
- **External Dependencies**: Connects to external ClickHouse and S3 services

## Files Structure

```
├── Dockerfile                 # Single unified container definition
├── docker-build.sh            # Build script with Docker Desktop bug workaround
├── docker-push.sh             # Push script to registry
├── test-docker.sh             # Container testing script
├── test-nomad.sh              # Nomad job mode testing
├── .dockerignore              # Build context exclusions
└── scripts/
    └── setup-and-run.sh       # Runtime tool setup entrypoint
```

## Tool Installation Strategy

The container uses a **runtime installation** approach:

1. **Base Image**: Contains Python dependencies and REDB code
2. **Runtime Setup**: `scripts/setup-and-run.sh` configures tools at container start
3. **Host Snapshots**: Binary Ninja installed from `/opt/binaryninja` if available
4. **System Tools**: CAPA and DIE expected at `/usr/bin/capa` and `/usr/bin/nfdc`

## Build and Run

### 1. Build Container

```bash
# Build unified image
./docker-build.sh

# Manual build
docker build --platform linux/amd64 -f Dockerfile -t redb:latest .
```

### 2. Run Modes

#### Nomad Job Mode (Primary)
```bash
# Feature extraction
docker run --rm \
  -e JOB_ID="analysis_001" \
  -e S3_KEY="samples/malware.exe" \
  -e S3_BUCKET="malware-bucket" \
  -e WORKER_TYPE="feature_extraction" \
  -e CALLBACK_URL="https://api.example.com/callbacks" \
  -e ANALYSIS_MODULES="BasicPropertiesExtractor,PEFeaturesExtractor" \
  -e CLICKHOUSE_HOST="clickhouse.example.com" \
  -e S3_ENDPOINT="s3.example.com" \
  -e S3_ACCESS_KEY="your-key" \
  -e S3_SECRET_KEY="your-secret" \
  redb:latest python3 start.py --nomad-job

# Decompilation (same container, different flags)
docker run --rm \
  -e JOB_ID="analysis_002" \
  -e S3_KEY="samples/malware.exe" \
  -e S3_BUCKET="malware-bucket" \
  -e WORKER_TYPE="decompilation" \
  -e CALLBACK_URL="https://api.example.com/callbacks" \
  -e ANALYSIS_MODULES="all" \
  -v /opt/binaryninja:/opt/binaryninja:ro \
  redb:latest python3 start.py --nomad-job --decompile
```

#### S3 Solo Mode
```bash
# Process single sample by S3 key (standard sharded path)
docker run --rm \
  -e S3_BUCKET="samples-bucket" \
  -e CLICKHOUSE_HOST="clickhouse.example.com" \
  -e S3_ENDPOINT="s3.example.com" \
  -e INDEX_PREFIX="redb" \
  -e REPO="test-analysis" \
  redb:latest python3 start.py --s3-solo "09/f7/09f7d02a3c2382199458c98a62b045145ee54ab6aba86166aecf3d10c3c1444c.zip"

# Process private sample (with prepath)
docker run --rm \
  -e S3_BUCKET="samples-bucket" \
  -e CLICKHOUSE_HOST="clickhouse.example.com" \
  -e S3_ENDPOINT="s3.example.com" \
  -e INDEX_PREFIX="redb" \
  -e REPO="test-analysis" \
  redb:latest python3 start.py --s3-solo "private/ab/cd/abcd1234567890abcdef1234567890abcdef1234567890abcdef123456.zip"
```

#### Local Files Mode
```bash
# Mount local samples
docker run --rm \
  -v /path/to/samples:/samples:ro \
  -v ./logs:/app/logs \
  -e CLICKHOUSE_HOST="clickhouse.example.com" \
  redb:latest python3 start.py --path /samples --repo local_test --index_prefix redb
```

## Environment Variables

### Required for Nomad Job Mode
- `JOB_ID` - Unique job identifier
- `S3_KEY` - S3 object key for sample
- `S3_BUCKET` - S3 bucket name
- `WORKER_TYPE` - "feature_extraction" or "decompilation"
- `CALLBACK_URL` - API endpoint for results
- `ANALYSIS_MODULES` - Comma-separated extractor list or "all"

### Database Configuration
- `CLICKHOUSE_HOST` - ClickHouse server hostname
- `CLICKHOUSE_PORT` - Port (default: 8123)
- `CLICKHOUSE_USER` - Database user (default: default)
- `CLICKHOUSE_PASSWORD` - Database password
- `CLICKHOUSE_DATABASE` - Database name (default: default)

### S3 Configuration
- `S3_ENDPOINT` - S3 endpoint URL
- `S3_ACCESS_KEY` - S3 access key
- `S3_SECRET_KEY` - S3 secret key
- `S3_SECURE` - "true" or "false" for HTTPS

### Processing Configuration
- `INDEX_PREFIX` - Database table prefix (default: redb)
- `REPO` - Repository identifier for this analysis batch
- `BATCH_SIZE` - Processing batch size (default: 10)
- `REDB_TIMEOUT` - Analysis timeout in seconds (default: 300)

### Tool Timeouts
- `CAPA_TIMEOUT` - CAPA analysis timeout (default: 300)
- `DIE_TIMEOUT` - DIE analysis timeout (default: 180)
- `BINJA_TIMEOUT` - Binary Ninja timeout (default: 1200)
- `DECOMPILE_EXTRACTOR_TIMEOUT` - Decompilation timeout (default: 2580)

## Binary Ninja Setup

For decompilation capabilities, mount Binary Ninja from host:

```bash
# Mount Binary Ninja installation
-v /opt/binaryninja:/opt/binaryninja:ro

# Mount license file
-v /path/to/license.dat:/home/analyzer/.binaryninja/license.dat:ro
```

The container will automatically detect and configure Binary Ninja at runtime.

## Registry Deployment

### Push to Registry
```bash
# Tag and push
./docker-push.sh

# Or manually
docker tag redb:latest your-registry/redb:latest
docker push your-registry/redb:latest
```

### Pull and Run
```bash
docker pull your-registry/redb:latest
docker run your-registry/redb:latest python3 start.py --nomad-job
```

## Testing

### Container Functionality Test
```bash
# Test with S3 key (standard sharded path)
./test-docker.sh "09/f7/09f7d02a3c2382199458c98a62b045145ee54ab6aba86166aecf3d10c3c1444c.zip"

# Test with private sample S3 key
./test-docker.sh "private/ab/cd/abcd1234567890abcdef1234567890abcdef1234567890abcdef123456.zip"
```

### Nomad Job Architecture Test
```bash
# Test Nomad job mode
./test-nomad.sh
```

## Development

### Interactive Container
```bash
# Debug container interactively
docker run -it --entrypoint /bin/bash redb:latest

# Check tool availability
docker run --rm redb:latest which python3
docker run --rm redb:latest ls -la /usr/bin/capa
```

### Build Troubleshooting

The build script includes workarounds for Docker Desktop bugs:

```bash
# If build hangs at "exporting to image", press Ctrl+C
# The image will still be created and tagged automatically
./docker-build.sh
```

### Container Logs
```bash
# View logs from mounted directory
docker run -v ./logs:/app/logs redb:latest python3 start.py --path /samples
tail -f logs/*.txt
```

## Production Notes

### Resource Requirements
- **Memory**: 2-4GB recommended (8GB for decompilation)
- **CPU**: 2+ cores recommended
- **Disk**: Minimal (stateless container)
- **Network**: Access to ClickHouse and S3 services

### Security
- Container runs as non-root user `analyzer` (UID 1000)
- Sample files should be mounted read-only
- No persistent state between container runs
- Isolated processing environment for malware analysis

### Deployment Architecture

This container is designed for:
- **Nomad job dispatch**: Single-use containers processing one sample each
- **Kubernetes jobs**: Batch processing with external orchestration
- **CI/CD pipelines**: Automated analysis in build systems
- **Development**: Local testing and debugging

The unified container approach means the same image handles both feature extraction and decompilation - the difference is only in the command-line flags used when starting the container.