Haijing Wang

38 papers A* 1C 2Misc 1Journal 19Unranked 15
YearRankTypeTitle / Venue / Authors
2026 J jnl
IEEE Trans. Ind. Electron.
Penghui Fan, Jinzhu Peng, Shuai Ding, Haijing Wang, Yaoyu Yang, Rickey Dubay
2025 J jnl
IEEE Trans. Cybern.
Haijing Wang, Jinzhu Peng, Yaqiang Liu, Wei He, Yaonan Wang
2025 J jnl
IEEE Syst. J.
Fangfang Zhang, Yongqi Wang, Jianbin Xin, Haijing Wang, Jinzhu Peng, Yaonan Wang
2025 J jnl
Ind. Manag. Data Syst.
Qinqin Wu, Sikandar Ali Qalati, Kayhan Tajeddini, Haijing Wang
2024 J jnl
IEEE Trans. Circuits Syst. II Express Briefs
Jinzhu Peng, Zhiyao Ni, Haijing Wang, Hongshan Yu, Shuai Ding
2024 J jnl
IEEE CAA J. Autom. Sinica
Haijing Wang, Jinzhu Peng, Fangfang Zhang, Yaonan Wang
2024 J jnl
IEEE Trans Autom. Sci. Eng.
Jinzhu Peng, Haijing Wang, Shuai Ding, Jing J. Liang, Yaonan Wang
2024 J jnl
IEEE Trans. Circuits Syst. II Express Briefs
Haijing Wang, Jinzhu Peng, Fangfang Zhang, Yaonan Wang
2023 J jnl
IEEE Trans. Syst. Man Cybern. Syst.
Haijing Wang, Jinzhu Peng, Fangfang Zhang, Yaonan Wang
2023 conf
HP3C
Xiaohan Zhang, Haijing Wang, Ruipeng Tian, Xuyang Cao, Wei Ding
2023 conf
CACRE
Haijing Wang, Jinzhu Peng, Fangfang Zhang, Yaonan Wang
2022 J jnl
IEEE Trans. Ind. Electron.
Jinli Zhu, Chengxiong Mao, Bin Liu, Zhaoyuan Wang, Dan Wang, Haijing Wang
2022 J jnl
Complex.
Haijing Wang, Junguo Shi, Muhammad Imran, Jiaqi Gao, Yitian Zhang, Rongyu Wang
2021 J jnl
IEEE Trans. Ind. Electron.
Jinli Zhu, Chengxiong Mao, Zhaoyuan Wang, Jinfan Chen, Dan Wang, Le Luan, Yajun Qiao, Haijing Wang
2021 conf
ICIRA (1)
Fangfang Zhang, Wenli Zhang, Bo Chen, Haijing Wang, Yanhong Liu
2020 J jnl
J. Sensors
Haijing Wang, Fangfang Zhang, Wenli Zhang
2019 conf
GamiFIN
Mela Kocher, Anna Lisa Martin-Niedecken, Yu Li, Wolfgang Kinzelbach, Haijing Wang, René Bauer, Livio Lunin
2019 J jnl
J. Frankl. Inst.
Youcheng Niu, Haijing Wang, Zheng Wang, Dawen Xia, Huaqing Li
2016 conf
ICCA
Haijing Wang, Lihua Xie, Shuai Liu, Juanjuan Xu
2016 C conf
IGARSS
Chunfeng Ma, Xin Li, Irena Hajnsek, Haijing Wang
2016 A* conf
KDD
Ying Shan, T. Ryan Hoens, Jian Jiao, Haijing Wang, Dong Yu, J. C. Mao
2016 J jnl
IBM J. Res. Dev.
Amith Singhee, Zhiguo Li, Ali Koc, Haijing Wang, James P. Cipriani, Younghun Kim, Ashok Pon Kumar, Lloyd A. Treinish, Richard Mueller, Gerard Labut, Richard A. Foltman, Gary M. Gauthier
2016 conf
CISP-BMEI
Guixiong He, Rui Zhao, Jian Qin, Limin Jiang, Haijing Wang, Yanmei Tang
2015 Misc conf
WSC
Rui Zhang, Tarun Kumar, Haijing Wang
2014 J jnl
Grey Syst. Theory Appl.
Xiaoning Li, Xinbo Liao, Xuerui Tan, Haijing Wang
2012 C conf
IGARSS
Haijing Wang, Irena Hajnsek, Wolfgang Kinzelbach
2010 conf
ECCV Workshops (1)
Haijing Wang, Alexandra Stefan, Sajjad Moradi, Vassilis Athitsos, Carol Neidle, Farhad Kamangar
2010 J jnl
Pers. Ubiquitous Comput.
Vassilis Athitsos, Haijing Wang, Alexandra Stefan
2009 conf
HCI (7)
Haijing Wang, Alexandra Stefan, Vassilis Athitsos
2009 conf
PETRA
Alexandra Stefan, Haijing Wang, Vassilis Athitsos
2008 J jnl
Neural Comput. Appl.
Haijing Wang, Peihua Li, Tianwen Zhang
2007 J jnl
Int. J. Pattern Recognit. Artif. Intell.
Haijing Wang, Peihua Li, Tianwen Zhang
2007 conf
MIRAGE
Peihua Li, Haijing Wang
2006 conf
ACCV (2)
Haijing Wang, Peihua Li, Tianwen Zhang
2005 conf
ICIP (3)
Haijing Wang, Peihua Li, Tianwen Zhang
2005 conf
IbPRIA (1)
Peihua Li, Haijing Wang
2005 conf
ICAPR (2)
Haijing Wang, Peihua Li, Tianwen Zhang
2004 conf
PCM (2)
Peihua Li, Haijing 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.