Xiaojun Yan

25 papers Journal 23Unranked 2
YearRankTypeTitle / Venue / Authors
2025 J jnl
IEEE Trans. Ind. Electron.
Shihao Zhou, Zhiwei Liu, Lei Qu, Xiaoyuan Wang, Xupeng Li, Wenhui Zhang, Jiaming Leng, Dawei Huang, Xiaojun Yan
2024 J jnl
IEEE Trans. Aerosp. Electron. Syst.
Jiaming Leng, Zhiwei Liu, Mingjing Qi, Xiaojun Yan
2023 J jnl
IEEE Robotics Autom. Lett.
Ruide Yun, Zhiwei Liu, Jiaming Leng, Jianmei Huang, Yong Cui, Xiaojun Yan, Mingjing Qi
2023 J jnl
IEEE Robotics Autom. Lett.
Ruide Yun, Mingjing Qi, Zhiwei Liu, Jiaming Leng, Xiaojun Yan
2023 J jnl
Remote. Sens.
Rong Tang, Lina Cai, Xiaojun Yan, Xiaomin Ye, Yuzhu Xu, Jie Yin
2023 J jnl
Adv. Intell. Syst.
Ruide Yun, Lingyue Zhang, Yangsheng Zhu, Hengyu Zhang, Wencheng Zhang, Zhiwei Liu, Xiaojun Yan, Mingjing Qi
2022 J jnl
IEEE Trans. Robotics
Yangsheng Zhu, Mingjing Qi, Zhiwei Liu, Jianmei Huang, Dawei Huang, Xiaojun Yan, Liwei Lin
2022 J jnl
IEEE Robotics Autom. Lett.
Hengyu Zhang, Jiaming Leng, Di Liu, Wencheng Zhan, Ruide Yun, Zhiwei Liu, Mingjing Qi, Xiaojun Yan
2022 J jnl
IEEE Trans. Instrum. Meas.
Xiaojun Yan, Jingang Wang, Zeliang Shen, Pengcheng Zhao, Xiang Li, Qian Wang
2022 J jnl
Remote. Sens.
Lina Cai, Menghan Yu, Xiaojun Yan, Yongdong Zhou, Songyu Chen
2022 J jnl
IEEE Trans. Instrum. Meas.
Yiming Zhang, Jingang Wang, Pengcheng Zhao, YangTian Yan, Ruiqiang Zhang, Xiaojun Yan
2022 J jnl
Remote. Sens.
Lina Cai, Songyu Chen, Xiaojun Yan, Yan Bai, Juan Bu
2022 J jnl
IEEE Robotics Autom. Lett.
Wencheng Zhang, Mingjing Qi, Zhiwei Liu, Jiaming Leng, Hengyu Zhang, Ruide Yun, Xiaojun Yan
2021 J jnl
IEEE Trans. Instrum. Meas.
Pengcheng Zhao, Jingang Wang, Zeliang Shen, Xiaojun Yan, Yiming Zhang, YangTian Yan
2021 J jnl
IEEE Trans. Instrum. Meas.
Xiaojun Yan, Jingang Wang, Pengcheng Zhao, Zeliang Shen, Xiang Li, Ruiqiang Zhang
2021 J jnl
Sensors
Jiarui Fan, Cheng Ai, Aofei Guo, Xiaojun Yan, Jingang Wang
2021 J jnl
IEEE Robotics Autom. Lett.
Hengyu Zhang, Jiaming Leng, Di Liu, Zhiwei Liu, Dawei Huang, Mingjing Qi, Xiaojun Yan
2020 J jnl
IEEE Trans. Instrum. Meas.
Diancheng Si, Jingang Wang, Gang Wei, Xiaojun Yan
2020 J jnl
J. Intell. Fuzzy Syst.
Zhijun Zeng, Yong Gao, Liyan Liu, Xiaojun Yan, Guoliang Xu, Hongning Liu, Yanhua Ji
2020 J jnl
Sensors
Jingang Wang, Xiaojun Yan, Lu Zhong, Xiaobao Zhu
2019 J jnl
Sci. Robotics
Yichuan Wu, Justin K. Yim, Jiaming Liang, Zhichun Shao, Mingjing Qi, Junwen Zhong, Zihao Luo, Xiaojun Yan, Min Zhang, Xiaohao Wang, Ronald S. Fearing, Robert J. Full, Liwei Lin
2017 J jnl
IEEE Trans. Commun.
Jing Xu, Xiaojun Yan, Yuanping Zhu, Jiang Wang, Yang Yang, Xiaohu Ge, Guoqiang Mao, Olav Tirkkonen
2016 J jnl
IEEE Trans. Commun.
Xiaojun Yan, Jing Xu, Yuanping Zhu, Jiang Wang, Yang Yang, Cheng-Xiang Wang
2015 conf
ICC
Xiaojun Yan, Jing Xu, Yuanping Zhu, Yang Yang, Guoping Tan
2014 conf
ICUFN
Yu Zeng, Yunqing Chen, Shuntian Feng, Kaiyu Zhou, Xianghui Sun, Tong Mao, Lei Shi, Baohua Lei, Feng Wang, Xiaojun Yan, Quanhui Xu, Bofei Zhang
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.