Xiaofei Zhao

105 papers A* 1A 1B 2C 2Misc 1Journal 82Unranked 16
YearRankTypeTitle / Venue / Authors
2026 A* conf
AAAI
Xiaofei Zhao
2026 J jnl
CoRR
Wei Liu, Tingfeng Wang, Xiaofei Zhao
2026 J jnl
Comput. Phys. Commun.
Yanzhi Zhang, Xiaofei Zhao, Shiping Zhou
2026 J jnl
J. Comput. Phys.
Bin Wang, Lina Wang, Ruijie Yin, Xiaofei Zhao
2026 J jnl
Inf. Sci.
Xiaofei Zhao, Qi Duan, Hongji Yang
2026 J jnl
J. Sci. Comput.
Bing Li, Qianrui Wei, Xiaofei Zhao
2025 J jnl
CoRR
Qing Cheng, Tingfeng Wang, Xiaofei Zhao
2025 J jnl
Clust. Comput.
Xiaofei Zhao, Jingyi Bai, Hongji Yang
2025 J jnl
Briefings Bioinform.
Xiaofei Zhao, Lei Wei, Xuegong Zhang
2025 J jnl
J. Comput. Phys.
Wei Liu, Zhenye Wen, Yongjun Yuan, Xiaofei Zhao
2025 J jnl
J. Comput. Phys.
Weizhu Bao, Zhipeng Chang, Xiaofei Zhao
2025 J jnl
CoRR
Lun Ji, Xiaofei Zhao
2025 J jnl
J. Sci. Comput.
Rui Chen, Tingfeng Wang, Xiaofei Zhao
2025 J jnl
CoRR
Zhangyong Liang, Zhiping Mao, Xiaofei Zhao
2025 J jnl
CoRR
Jing Li, Cui Ning, Xiaofei Zhao
2025 J jnl
CoRR
Zhizhang Wu, Zhiwen Zhang, Xiaofei Zhao
2025 J jnl
Multiscale Model. Simul.
Kai Liu, Bin Wang, Xiaofei Zhao
2025 J jnl
CoRR
Zhipeng Chang, Zhenye Wen, Xiaofei Zhao
2024 J jnl
Clust. Comput.
Xiaofei Zhao, Mengqian Yang, Hongji Yang
2024 conf
WACV (Workshops)
Chong Zeng, Hongji Yang, Zhongxi Lu, Xiaofei Zhao, Zhiying Xiu
2024 J jnl
Comput. Phys. Commun.
Xue Hong, Qianrui Wei, Xiaofei Zhao
2024 J jnl
SIAM/ASA J. Uncertain. Quantification
Zhizhang Wu, Zhiwen Zhang, Xiaofei Zhao
2024 J jnl
IEEE Trans. Geosci. Remote. Sens.
Xin Li, Xiaofei Zhao, Fusheng Wang, Peng Ren
2024 J jnl
CoRR
Rui Chen, Tingfeng Wang, Xiaofei Zhao
2024 J jnl
CoRR
Norbert J. Mauser, Yifei Wu, Xiaofei Zhao
2023 J jnl
Remote. Sens.
Xingchen Qiu, Hailiang Gao, Yixue Wang, Wei Zhang, Xinda Shi, Fengjun Lv, Yanqiu Yu, Zhuoran Luan, Qianqian Wang, Xiaofei Zhao
2023 J jnl
SIAM J. Sci. Comput.
Wei Liu, Yongjun Yuan, Xiaofei Zhao
2023 J jnl
CoRR
Zhizhang Wu, Zhiwen Zhang, Xiaofei Zhao
2023 J jnl
CoRR
Shiping Zhou, Xiaofei Zhao, Yanzhi Zhang
2023 J jnl
SIAM J. Numer. Anal.
Bing Li, Yifei Wu, Xiaofei Zhao
2023 J jnl
SIAM J. Numer. Anal.
Bin Wang, Xiaofei Zhao
2023 J jnl
J. Appl. Math.
Qingxiang Li, Xiaofei Zhao, Yude He, Yin Shaojun
2023 J jnl
MIS Q.
Jun Li, Xianwei Liu, Qiang Ye, Feng Zhao, Xiaofei Zhao
2023 J jnl
J. Sci. Comput.
Ying He, Xiaofei Zhao
2023 J jnl
CoRR
Wei Liu, Chushan Wang, Xiaofei Zhao
2023 J jnl
IEEE Access
Xiaofei Zhao, Fanzhang Li, Hongji Yang
2023 conf
ICCPR
Xin Li, Baile Sun, Jixiu Liao, Xiaofei Zhao
2023 J jnl
Remote. Sens.
Hailiang Gao, Qianqian Wang, Xingfa Gu, Jian Yang, Qiyue Liu, Zui Tao, Xingchen Qiu, Wei Zhang, Xinda Shi, Xiaofei Zhao
2023 J jnl
Manag. Sci.
Joon Woo Bae, Frederico Belo, Jun Li, Xiaoji Lin, Xiaofei Zhao
2022 J jnl
Math. Comput.
Yan Wang, Xiaofei Zhao
2022 J jnl
SIAM J. Numer. Anal.
Cui Ning, Yifei Wu, Xiaofei Zhao
2022 J jnl
Appl. Artif. Intell.
Zhaozhao Zhang, Yue Liu, Ying-qin Zhu, Xiaofei Zhao
2022 J jnl
Briefings Bioinform.
Xiaofei Zhao, Allison C. Hu, Sizhen Wang, Xiaoyue Wang
2022 J jnl
CoRR
Wei Liu, Yongjun Yuan, Xiaofei Zhao
2022 J jnl
J. Comput. Phys.
Xavier Antoine, Xiaofei Zhao
2021 J jnl
J. Comput. Phys.
Chunmei Su, Xiaofei Zhao
2021 J jnl
SIAM J. Numer. Anal.
Bin Wang, Xiaofei Zhao
2021 J jnl
IEEE Access
Xiaofei Zhao, Fanzhang Li, Hongji Yang
2021 J jnl
CoRR
Bin Wang, Xiaofei Zhao
2021 J jnl
Math. Comput.
Katharina Schratz, Yan Wang, Xiaofei Zhao
2021 J jnl
J. Sci. Comput.
Xiaofei Zhao
2021 J jnl
CoRR
Xavier Antoine, Xiaofei Zhao
2021 J jnl
Sensors
Jianyu Yang, Guanchao Li, Xiaofei Zhao, Hualong Xie
2021 J jnl
J. Comput. Appl. Math.
Patrick Krämer, Katharina Schratz, Xiaofei Zhao
2020 J jnl
CoRR
Yifei Wu, Xiaofei Zhao
2020 J jnl
CoRR
Bin Wang, Xiaofei Zhao
2020 J jnl
J. Intell. Fuzzy Syst.
Yuan Luo, Xiaofei Zhao, Yiyu Qiu
2020 J jnl
CoRR
Xiaofei Zhao
2020 J jnl
Multiscale Model. Simul.
Norbert J. Mauser, Yong Zhang, Xiaofei Zhao
2020 J jnl
Sensors
Hualong Xie, Guanchao Li, Xiaofei Zhao, Fei Li
2020 J jnl
SIAM J. Sci. Comput.
Philippe Chartier, Nicolas Crouseilles, Mohammed Lemou, Florian Méhats, Xiaofei Zhao
2019 C conf
IGARSS
Xiaofei Zhao, Hongyi Liu, Jun Zhang, Zebin Wu, Zhihui Wei
2019 J jnl
Bioinform.
Xiaofei Zhao
2019 conf
KSEM (1)
Xiaofei Zhao, Zhiyong Feng
2019 J jnl
Swarm Evol. Comput.
Dongping Tian, Xiaofei Zhao, Zhongzhi Shi
2019 J jnl
J. Comput. Phys.
Weizhu Bao, Xiaofei Zhao
2019 J jnl
IEEE Access
Dongping Tian, Xiaofei Zhao, Zhongzhi Shi
2019 J jnl
CoRR
Katharina Schratz, Yan Wang, Xiaofei Zhao
2019 J jnl
CoRR
Yifei Wu, Xiaofei Zhao
2019 J jnl
Math. Comput.
Philippe Chartier, Nicolas Crouseilles, Mohammed Lemou, Florian Méhats, Xiaofei Zhao
2019 J jnl
CoRR
Philippe Chartier, Nicolas Crouseilles, Mohammed Lemou, Florian Méhats, Xiaofei Zhao
2018 J jnl
Comput. Phys. Commun.
Nicolas Crouseilles, Sever A. Hirstoaga, Xiaofei Zhao
2018 J jnl
J. Comput. Phys.
Philippe Chartier, Nicolas Crouseilles, Xiaofei Zhao
2018 conf
EM-GIS@SIGSPATIAL
Yangyang Meng, Zhongwen Li, Wei Zhou, Zhi-Jie Zhou, Xiaofei Zhao, Maohua Zhong
2018 J jnl
Adv. Comput. Math.
Tingchun Wang, Xiaofei Zhao, Jiaping Jiang
2017 J jnl
J. Comput. Appl. Math.
Xiaofei Zhao
2017 J jnl
Numerische Mathematik
Weizhu Bao, Xiaofei Zhao
2017 J jnl
Manag. Sci.
Xiaofei Zhao
2017 C conf
IDEAL
JinShuai Li, Xiaofei Zhao, Baoshan Sun
2017 J jnl
Internet Res.
Xiaofei Zhao, Shengliang Deng, Yi Zhou
2017 J jnl
Multiscale Model. Simul.
Nicolas Crouseilles, Mohammed Lemou, Florian Méhats, Xiaofei Zhao
2017 J jnl
J. Comput. Phys.
Nicolas Crouseilles, Mohammed Lemou, Florian Méhats, Xiaofei Zhao
2016 J jnl
J. Comput. Phys.
Weizhu Bao, Xiaofei Zhao
2016 J jnl
BMC Medical Informatics Decis. Mak.
Yuan Xu, Ning Li, Ming-Shan Lu, Robert P. Myers, Elijah Dixon, Robin Walker, Libo Sun, Xiaofei Zhao, Hude Quan
2016 conf
ROBIO
Xiaoqiang Xue, Xiaofei Zhao, Jinguo Huang, Xingbang Yang, Guocai Yao, Jianhong Liang, Daibing Zhang
2015 conf
WCSP
Xiaofei Zhao, Xinyu Gu, Yi Gong, Lin Zhang, Wenyu Li
2015 conf
ChinaCom
Yi Gong, Xinyu Gu, Lin Zhang, Wenyu Li, Xiaofei Zhao
2014 J jnl
SIAM J. Numer. Anal.
Weizhu Bao, Yongyong Cai, Xiaofei Zhao
2014 J jnl
Intell. Autom. Soft Comput.
Dongping Tian, Xiaofei Zhao, Zhongzhi Shi
2014 conf
ChinaCom
Xin Lv, Xinyu Gu, Xiaofei Zhao, Lin Zhang, Wenyu Li, Xin Deng
2014 J jnl
J. Sci. Comput.
Xiaofei Zhao, Ziyi Li
2013 J jnl
SIAM J. Sci. Comput.
Weizhu Bao, Xuanchun Dong, Xiaofei Zhao
2013 Misc conf
ICASSP
Wenbo Zhang, Dongping Tian, Hong Hu, Xiaofei Zhao, Zhongzhi Shi
2013 B conf
ICIP
Dongping Tian, Wenbo Zhang, Xiaofei Zhao, Zhongzhi Shi
2013 J jnl
Pattern Recognit. Lett.
Dongwei Ren, Wangmeng Zuo, Xiaofei Zhao, Zhouchen Lin, David Zhang
2013 conf
MMM (1)
Dongping Tian, Xiaofei Zhao, Zhongzhi Shi
2012 conf
CCPR
Dongwei Ren, Wangmeng Zuo, Xiaofei Zhao, Hongzhi Zhang, David Zhang
2012 conf
CCIS
Zhongzhi Shi, Guang Jiang, Bo Zhang, Jinpeng Yue, Xiaofei Zhao
2012 conf
IIP
Xiaofei Zhao, Dongping Tian, Limin Chen, Zhongzhi Shi
2012 conf
IIP
Dongping Tian, Xiaofei Zhao, Zhongzhi Shi
2011 B conf
ICIP
Jianfeng Lu, Wangmeng Zuo, Xiaofei Zhao, David Zhang
2011 conf
CCIS
Xiaofei Zhao, Zhongzhi Shi
2010 conf
DASFAA Workshops
Xiaofei Zhao, Zhiqiu Huang
2006 A conf
ER
Xiaofei Zhao, Zhiqiu Huang
2006 conf
OWLED
Guohua Shen, Zhiqiu Huang, Xiaodong Zhu, Xiaofei Zhao
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.