Chan-Yun Yang

37 papers B 4C 2Journal 19Unranked 12
YearRankTypeTitle / Venue / Authors
2025 J jnl
Expert Syst. Appl.
Chan-Yun Yang, Nilantha Premakumara, Hooman Samani, Chinthaka Premachandra
2024 J jnl
Int. J. Intell. Robotics Appl.
Chan-Yun Yang, Hooman Samani, Zirong Tang, Chunxu Li
2024 conf
ICSSE
Ying Wai Wong, Hao-Ting Cheng, Si-Qian Chen, Chan-Yun Yang
2023 J jnl
Comput. Methods Programs Biomed.
Chan-Yun Yang, Chamani Shiranthika, Chung-Yih Wang, Kuo-Wei Chen, Sagara Sumathipala
2023 conf
ICSSE
Yi-Wen Cheng, Sung-Chih Chen, Cheng-Yi Lin, Bing-Chien Yu, Chan-Yun Yang
2022 J jnl
IEEE J. Biomed. Health Informatics
Chamani Shiranthika, Kuo-Wei Chen, Chung-Yih Wang, Chan-Yun Yang, B. H. Sudantha, Wei-Fu Li
2021 J jnl
J. Comput. Des. Eng.
Hooman Samani, Chan-Yun Yang, Chunxu Li, Chia-Ling Chung, Shaoxiang Li
2021 J jnl
Wirel. Pers. Commun.
Chan-Yun Yang, Muhammad Sharif, Sri Devi Ravana, Anandakumar Haldorai
2021 J jnl
Comput. Informatics
Chan-Yun Yang, Hooman Samani, Nana Ji, Chunxu Li, Ding-Bang Chen, Man Qi
2020 J jnl
Neurocomputing
Can Jiang, Feng Zhang, Jianjun Wang, Chan-Yun Yang, Wendong Wang
2019 J jnl
Sensors
Chan-Yun Yang, Pei-Yu Chen, Te-Jen Wen, Gene Eu Jan
2018 B conf
SMC
Chan-Yun Yang, Chen-Yu Lin, Sainzaya Galsanbadam, Hooman Samani
2017 B conf
SMC
Chan-Yun Yang, Gene Eu Jan, Hooman Samani, LiYu Yu
2017 conf
ICNSC
Jr-Syu Yang, Chan-Yun Yang, Gene Eu Jan, Tung-Lin Hsieh
2016 conf
ICNSC
Yi-Wen Cheng, Te-Jen Wen, Hui-Chuan Cheng, Chan-Yun Yang
2016 B conf
SMC
Yung-Hsiang Chou, Hui-Chuan Cheng, Chih-Hsiu Cheng, Kuo-Ho Su, Chan-Yun Yang
2015 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Jianjun Wang, Jing Zhang, Wendong Wang, Chan-Yun Yang
2015 J jnl
Neurocomputing
Chan-Yun Yang, Jian-Jun Wang, Jui-Jen Chou, Feng-Li Lian
2015 C conf
ICMLA
Bin-Bin Gao, Jian-Jun Wang, Yao Wang, Chan-Yun Yang
2015 J jnl
Int. J. Fuzzy Syst.
Kuo-Ho Su, Syuan-Jie Huang, Chan-Yun Yang
2015 conf
ICSSE
Kuo-Ho Su, Duy-Thanh Pham, Tsing-Tshih Tsung, Chan-Yun Yang
2015 B conf
SMC
Hui-Chuan Cheng, Chan-Yun Yang, Gene Eu Jan, Angela Shin-Yih Chen
2015 conf
ICNSC
Wei-Chih Lin, Chan-Yun Yang, Gene Eu Jan, Jr-Syu Yang
2015 conf
ICSSE
Pei-Yu Chen, Feng-Li Lian, Kuo-Ho Su, Jr-Syu Yang, Chan-Yun Yang
2014 conf
ICCA
Kuo-Ho Su, Tan-Phat Phan, Chan-Yun Yang, Wen-June Wang
2013 J jnl
Neurocomputing
Chan-Yun Yang, Jui-Jen Chou, Feng-Li Lian
2012 J jnl
Neural Comput. Appl.
Jianjun Wang, Baili Chen, Chan-Yun Yang
2012 J jnl
J. Appl. Math.
Jianjun Wang, Chan-Yun Yang, Jia Jing
2009 J jnl
Neurocomputing
Chan-Yun Yang, Jr-Syu Yang, Jian-Jun Wang
2009 J jnl
Neural Process. Lett.
Chan-Yun Yang, Che-Chang Hsu, Jr-Syu Yang
2008 J jnl
Neurocomputing
Chan-Yun Yang
2008 conf
ISNN (1)
Chan-Yun Yang, Jianjun Wang, Jr-Syu Yang, Guo-Ding Yu
2008 conf
ISI Workshops
Chan-Yun Yang, Wei-Wen Tseng, Jr-Syu Yang
2006 C conf
CIS
Chan-Yun Yang, Che-Chang Hsu, Jr-Syu Yang
2005 J jnl
Image Vis. Comput.
Chan-Yun Yang, Jui-Jen Chou
2005 conf
CIS (1)
Che-Chang Hsu, Chan-Yun Yang, Jr-Syu Yang
2004 conf
ISNN (1)
Chan-Yun Yang
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.