Xiaojun Zhou

76 papers C 2Misc 1Journal 70Unranked 3
YearRankTypeTitle / Venue / Authors
2026 J jnl
Inf. Sci.
Xiaohe Li, Xiaojun Zhou, Yan Sun, Tingwen Huang, Chunhua Yang
2026 J jnl
Expert Syst. Appl.
Yingchao Dong, Ge Lan, Xiaojun Zhou
2025 J jnl
IEEE J. Biomed. Health Informatics
Yangyi Du, Xiaojun Zhou, Qian Gao, Chunhua Yang, Tingwen Huang
2025 J jnl
IEEE Syst. J.
Yi Li, Guo Chen, Xiaojun Zhou
2025 J jnl
Neurocomputing
Xiaojun Zhou, Zheng Wang, Tingwen Huang
2025 J jnl
Knowl. Based Syst.
Xiaojun Zhou, Runsong Xia, Tingwen Huang
2025 J jnl
Swarm Evol. Comput.
Yangyi Du, Xiaojun Zhou, Chunhua Yang, Weihua Gui
2025 J jnl
Expert Syst. Appl.
Yan Sun, Xiaojun Zhou, Chunhua Yang, Tingwen Huang
2025 J jnl
Neurocomputing
Jiawen Yi, Guo Chen, Xiaojun Zhou
2025 J jnl
IEEE Trans. Ind. Informatics
Xiaojun Zhou, Ming Li, Yangyi Du, Chunhua Yang, Shiping Wen
2024 J jnl
Inf. Sci.
Yingchao Dong, Cong Wang, Hongli Zhang, Xiaojun Zhou
2024 J jnl
Expert Syst. Appl.
Xiaojun Zhou, Zhouhang Tang, Nan Wang, Chunhua Yang, Tingwen Huang
2024 J jnl
IEEE Trans. Parallel Distributed Syst.
Renyou Xie, Chaojie Li, Xiaojun Zhou, Zhaoyang Dong
2024 J jnl
Inf. Sci.
Xiaojun Zhou, Weijun Yuan, Qian Gao, Chunhua Yang
2024 J jnl
IEEE Trans. Ind. Informatics
Renyou Xie, Chaojie Li, Xiaojun Zhou, Hongyang Chen, Zhaoyang Dong
2024 J jnl
IEEE Trans. Intell. Transp. Syst.
Chaojie Li, Borui Zhang, Zeyu Wang, Yin Yang, Xiaojun Zhou, Shirui Pan, Xinghuo Yu
2024 J jnl
Expert Syst. Appl.
Xiaojun Zhou, Wan Tan, Yanan Sun, Tingwen Huang, Chunhua Yang
2024 J jnl
IEEE Trans. Smart Grid
Yingchao Dong, Zhengmao Li, Hongli Zhang, Cong Wang, Xiaojun Zhou
2024 J jnl
Eng. Appl. Artif. Intell.
Yingchao Dong, Hongli Zhang, Cong Wang, Xiaojun Zhou
2023 J jnl
Int. J. Mach. Learn. Cybern.
Yangyi Du, Xiaojun Zhou, Tingwen Huang, Chunhua Yang
2023 J jnl
Appl. Intell.
Boyan Ma, Yangyi Du, Xiaojun Zhou, Chunhua Yang
2023 J jnl
Inf. Fusion
Yanan Sun, Xiaojun Zhou, Chunhua Yang, Tingwen Huang
2023 J jnl
IEEE Trans. Ind. Electron.
Renyou Xie, Chaojie Li, Rui Ma, Liangcai Xu, Xiaojun Zhou
2023 J jnl
Eng. Appl. Artif. Intell.
Zhaoke Huang, Chunhua Yang, Xiaojun Zhou, Weihua Gui
2023 J jnl
Knowl. Based Syst.
Yangyi Du, Xiaojun Zhou, Chunhua Yang, Tingwen Huang
2023 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Shengbo Wang, Shiping Wen, Kaibo Shi, Xiaojun Zhou, Tingwen Huang
2023 Misc conf
ICASSP
Renyou Xie, Chaojie Li, Xiaojun Zhou, Zhaoyang Dong
2023 J jnl
Appl. Intell.
Zhaoke Huang, Chunhua Yang, Xiaojun Zhou, Weihua Gui, Tingwen Huang
2023 J jnl
IEEE Trans. Neural Networks Learn. Syst.
Feifan Lin, Xiaojun Zhou, Chaojie Li, Tingwen Huang, Chunhua Yang
2023 J jnl
IEEE Trans. Cloud Comput.
Jiangxia Zhong, Bin Liu, Xinghuo Yu, Peter Wong, Zeyu Wang, Chongchong Xu, Xiaojun Zhou
2023 J jnl
Expert Syst. Appl.
Yingchao Dong, Hongli Zhang, Cong Wang, Xiaojun Zhou
2022 J jnl
IEEE Trans. Netw. Sci. Eng.
Xiaojun Zhou, Yuan Gao, Chaojie Li, Zhaoke Huang
2022 J jnl
Appl. Soft Comput.
Xiaojun Zhou, Yuan Gao, Shengxiang Yang, Chunhua Yang, Jiajia Zhou
2022 J jnl
Appl. Soft Comput.
Yingchao Dong, Hongli Zhang, Cong Wang, Xiaojun Zhou
2022 J jnl
Knowl. Based Syst.
Xiaojun Zhou, Jingyi He, Chunhua Yang
2022 J jnl
Appl. Soft Comput.
Xiaojun Zhou, Yanan Sun, Zhaoke Huang, Chunhua Yang, Gary G. Yen
2022 J jnl
Knowl. Based Syst.
Xiaojun Zhou, Jituo Tian, Zeyu Wang, Chunhua Yang, Tingwen Huang, Xuesong Xu
2022 J jnl
IEEE Trans. Fuzzy Syst.
Jie Han, Chunhua Yang, Cheng-Chew Lim, Xiaojun Zhou, Peng Shi
2022 J jnl
Comput. Ind. Eng.
Zhaoke Huang, Chunhua Yang, Xiaofang Chen, Xiaojun Zhou, Weihua Gui
2021 J jnl
Neurocomputing
Xiaojun Zhou, Jituo Tian, Jianpeng Long, Yaochu Jin, Guo Yu, Chunhua Yang
2021 J jnl
Appl. Soft Comput.
Zhaoke Huang, Chunhua Yang, Xiaofang Chen, Xiaojun Zhou, Guo Chen, Tingwen Huang, Weihua Gui
2021 J jnl
IEEE Trans. Ind. Informatics
Xiaojun Zhou, Xiangyue Wang, Tingwen Huang, Chunhua Yang
2021 J jnl
IEEE Trans. Fuzzy Syst.
Jie Han, Chunhua Yang, Cheng-Chew Lim, Xiaojun Zhou, Peng Shi
2021 J jnl
Neurocomputing
Yingchao Dong, Hongli Zhang, Cong Wang, Xiaojun Zhou
2020 J jnl
CoRR
Jingyi He, Xiaojun Zhou, Rundong Zhang, Chunhua Yang
2020 J jnl
IEEE Trans. Ind. Informatics
Xiaojun Zhou, Miao Huang, Tingwen Huang, Chunhua Yang, Weihua Gui
2020 J jnl
Cogn. Comput.
Zhaoke Huang, Chunhua Yang, Xiaojun Zhou, Shengxiang Yang
2020 J jnl
Soft Comput.
Xiaojun Zhou, Rundong Zhang, Xiangyue Wang, Tingwen Huang, Chunhua Yang
2020 J jnl
Neurocomputing
Jie Han, Chunhua Yang, Cheng-Chew Lim, Xiaojun Zhou, Peng Shi, Weihua Gui
2020 J jnl
IEEE Geosci. Remote. Sens. Lett.
Tingting Zhao, Jingtian Tang, Shuanggui Hu, GuangYin Lu, Xiaojun Zhou, Yiyuan Zhong
2020 C conf
IECON
Chongchong Xu, Guo Chen, Xiaojun Zhou
2020 J jnl
Neurocomputing
Xiaojun Zhou, Rundong Zhang, Ke Yang, Chunhua Yang, Tingwen Huang
2019 J jnl
IEEE J. Biomed. Health Informatics
Zhaoke Huang, Chunhua Yang, Xiaojun Zhou, Tingwen Huang
2019 J jnl
IEEE Trans. Cybern.
Xiaojun Zhou, Chunhua Yang, Weihua Gui
2019 J jnl
Neurocomputing
Xiaojun Zhou, Ke Yang, Yongfang Xie, Chunhua Yang, Tingwen Huang
2019 conf
SSCI
Yunxiang Zhang, Xiaojun Zhou, Chunhua Yang
2019 J jnl
Neural Comput. Appl.
Miao Huang, Xiaojun Zhou, Tingwen Huang, Chunhua Yang, Weihua Gui
2019 J jnl
IEEE Access
Fengxue Zhang, Chunhua Yang, Xiaojun Zhou, Weihua Gui
2018 J jnl
Cogn. Comput.
Zhaoke Huang, Chunhua Yang, Xiaojun Zhou, Weihua Gui
2018 J jnl
Neurocomputing
Xiaojun Zhou, Peng Shi, Cheng-Chew Lim, Chunhua Yang, Weihua Gui
2018 J jnl
Neural Comput. Appl.
Fengxue Zhang, Chunhua Yang, Xiaojun Zhou, Weihua Gui
2018 J jnl
IEEE Trans. Multi Scale Comput. Syst.
Jiafeng Xie, Pramod Kumar Meher, Xiaojun Zhou, Chiou-Yng Lee
2018 J jnl
IEEE Access
Xiaojun Zhou, Jiajia Zhou, Chunhua Yang, Weihua Gui
2017 C conf
IECON
Chaojie Li, Xinghuo Yu, Xiaojun Zhou, Wei Ren
2016 J jnl
Neurocomputing
Xiaojun Zhou, David Yang Gao, Chunhua Yang, Weihua Gui
2016 J jnl
Optim. Lett.
Xiaojun Zhou, David Yang Gao, Chunhua Yang
2015 J jnl
J. Frankl. Inst.
Xiaojun Zhou, Peng Shi, Cheng-Chew Lim, Chunhua Yang, Weihua Gui
2014 J jnl
Appl. Math. Comput.
Xiaojun Zhou, David Yang Gao, Chunhua Yang
2014 J jnl
Appl. Math. Comput.
Xiaojun Zhou, Chunhua Yang, Weihua Gui
2013 conf
BIC-TA
Xiaojun Zhou, David Yang Gao, Chunhua Yang
2013 J jnl
CoRR
Xiaolin Tang, Chunhua Yang, Xiaojun Zhou, Weihua Gui
2013 J jnl
CoRR
Xiaojun Zhou
2012 J jnl
CoRR
Xiaojun Zhou
2012 J jnl
CoRR
Xiaojun Zhou, Chunhua Yang, Weihua Gui
2012 J jnl
CoRR
Xiaojun Zhou, Chunhua Yang, Weihua Gui
2011 conf
ICDMA
Xiaojun Zhou, Chunhua Yang, Weihua Gui
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.