Hande Yaman

69 papers Journal 69
YearRankTypeTitle / Venue / Authors
2025 J jnl
CoRR
Kobe Grobben, Phablo F. S. Moura, Hande Yaman
2025 J jnl
CoRR
Morteza Davari, Phablo F. S. Moura, Hande Yaman
2025 J jnl
CoRR
Diego Jiménez, Bernardo K. Pagnoncelli, Hande Yaman
2024 J jnl
Eur. J. Oper. Res.
Vedat Bayram, Hande Yaman
2024 J jnl
Transp. Sci.
Nicola Morandi, Roel Leus, Hande Yaman
2024 J jnl
Transp. Sci.
Munise Kübra Sahin, Hande Yaman
2023 J jnl
Eur. J. Oper. Res.
Hatice Çalik, Marc Juwet, Hande Yaman, Greet Vanden Berghe
2023 J jnl
Networks
Mercedes Landete, Juanjo Peiró, Hande Yaman
2023 J jnl
Eur. J. Oper. Res.
Özlem Mahmutogullari, Hande Yaman
2023 J jnl
CoRR
Phablo F. S. Moura, Roel Leus, Hande Yaman
2023 J jnl
Open J. Math. Optim.
Hande Yaman
2023 J jnl
Transp. Sci.
Nicola Morandi, Roel Leus, Jannik Matuschke, Hande Yaman
2022 J jnl
Transp. Sci.
Munise Kübra Sahin, Hande Yaman
2022 J jnl
Comput. Oper. Res.
Nicola Morandi, Roel Leus, Hande Yaman
2021 J jnl
INFORMS J. Comput.
Nihal Berktas, Hande Yaman
2021 J jnl
Comput. Oper. Res.
Gizem Ozbaygin, Esra Koca, Hande Yaman
2021 J jnl
Math. Program.
Laurence A. Wolsey, Hande Yaman
2021 J jnl
Transp. Sci.
Baris Yildiz, Hande Yaman, Oya Ekin Karasan
2021 J jnl
IISE Trans.
Esra Koca, Nilay Noyan, Hande Yaman
2020 J jnl
Comput. Oper. Res.
Munise Kübra Sahin, Özlem Çavus, Hande Yaman
2019 J jnl
Transp. Sci.
Okan Arslan, Oya Ekin Karasan, Ali Ridha Mahjoub, Hande Yaman
2019 J jnl
Eur. J. Oper. Res.
Inmaculada Rodríguez Martín, Juan José Salazar González, Hande Yaman
2018 J jnl
RAIRO Oper. Res.
Vedat Bayram, Hande Yaman
2018 J jnl
Comput. Oper. Res.
Baris Yildiz, Oya Ekin Karasan, Hande Yaman
2018 J jnl
Discret. Optim.
Laurence A. Wolsey, Hande Yaman
2018 J jnl
EURO J. Comput. Optim.
Gizem Ozbaygin, Oya Ekin Karasan, Hande Yaman
2018 J jnl
Transp. Sci.
Vedat Bayram, Hande Yaman
2018 J jnl
Comput. Oper. Res.
Esra Koca, Hande Yaman, M. Selim Akturk
2018 J jnl
Ann. des Télécommunications
Ibrahima Diarrassouba, Meriem Mahjoub, Ali Ridha Mahjoub, Hande Yaman
2017 J jnl
Comput. Oper. Res.
Merve Merakli, Hande Yaman
2016 J jnl
Comput. Oper. Res.
Inmaculada Rodríguez Martín, Juan José Salazar González, Hande Yaman
2016 J jnl
Math. Program.
Laurence A. Wolsey, Hande Yaman
2016 J jnl
Electron. Notes Discret. Math.
Inmaculada Rodríguez Martín, Juan José Salazar González, Hande Yaman
2016 J jnl
Networks
Inmaculada Rodríguez Martín, Juan José Salazar González, Hande Yaman
2016 J jnl
Comput. Oper. Res.
Gizem Ozbaygin, Hande Yaman, Oya Ekin Karasan
2014 J jnl
Comput. Oper. Res.
Inmaculada Rodríguez Martín, Juan José Salazar González, Hande Yaman
2014 J jnl
INFORMS J. Comput.
Esra Koca, Hande Yaman, M. Selim Aktürk
2014 J jnl
J. Oper. Res. Soc.
Evrim D. Günes, Hande Yaman, Bora Çekyay, Vedat Verter
2014 J jnl
J. Oper. Res. Soc.
Alper Sen, Hande Yaman, Kemal Güler, Evren Körpeoglu
2014 J jnl
Math. Program.
Mathieu Van Vyve, Laurence A. Wolsey, Hande Yaman
2014 J jnl
INFORMS J. Comput.
Oya Ekin Karasan, Ali Ridha Mahjoub, Onur Özkök, Hande Yaman
2013 J jnl
Oper. Res. Lett.
Hande Yaman
2012 J jnl
Oper. Res.
Minjiao Zhang, Simge Küçükyavuz, Hande Yaman
2012 J jnl
Comput. Optim. Appl.
Burak Ayar, Hande Yaman
2012 J jnl
Oper. Res.
Hande Yaman, Oya Ekin Karasan, Bahar Yetis Kara
2012 J jnl
Comput. Oper. Res.
Hande Yaman, Sourour Elloumi
2012 J jnl
Networks
Pierre Fouilhoux, Oya Ekin Karasan, Ali Ridha Mahjoub, Onur Özkök, Hande Yaman
2011 J jnl
Eur. J. Oper. Res.
Ersin Körpeoglu, Hande Yaman, M. Selim Aktürk
2011 J jnl
Eur. J. Oper. Res.
Hande Yaman
2011 J jnl
Discret. Appl. Math.
Hande Yaman
2011 J jnl
INFORMS J. Comput.
Aysegül Altin, Hande Yaman, Mustafa Ç. Pinar
2011 J jnl
Comput. Oper. Res.
Yüce Çinar, Hande Yaman
2010 J jnl
J. Oper. Res. Soc.
Evrim D. Günes, Hande Yaman
2009 J jnl
SIAM J. Discret. Math.
Pierre Fouilhoux, Martine Labbé, Ali Ridha Mahjoub, Hande Yaman
2009 J jnl
Discret. Appl. Math.
Hande Yaman
2008 J jnl
Networks
Julie Christophe, Sophie Dewez, Jean-Paul Doignon, Gilles Fasbender, Philippe Grégoire, David Huygens, Martine Labbé, Sourour Elloumi, Hadrien Mélot, Hande Yaman
2008 J jnl
Eur. J. Oper. Res.
Hande Yaman, Alper Sen
2008 J jnl
Networks
Martine Labbé, Hande Yaman
2008 J jnl
Comput. Oper. Res.
Hande Yaman
2007 J jnl
Math. Program.
Hande Yaman, Oya Ekin Karasan, Mustafa Ç. Pinar
2007 J jnl
SIAM J. Discret. Math.
Hande Yaman
2006 J jnl
Math. Program.
Hande Yaman
2006 J jnl
Comput. Optim. Appl.
Martine Labbé, Hande Yaman
2005 J jnl
Math. Program.
Martine Labbé, Hande Yaman, Éric Gourdin
2005 J jnl
SIAM J. Discret. Math.
Hande Yaman
2005 J jnl
Comput. Oper. Res.
Hande Yaman, Giuliana Carello
2004 J jnl
4OR
Hande Yaman
2004 J jnl
Networks
Martine Labbé, Hande Yaman
2001 J jnl
Oper. Res. Lett.
Hande Yaman, Oya Ekin Karasan, Mustafa Ç. Pinar
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.