Ralf Korn

45 papers A 1Journal 37Unranked 7
YearRankTypeTitle / Venue / Authors
2025 J jnl
Algorithms
Ralf Korn, Laurena Ramadani
2024 J jnl
Expert Syst. Appl.
Bilgi Yilmaz, Ralf Korn
2024 J jnl
Math. Comput. Simul.
Bükre Yildirim Külekci, Ralf Korn, A. Sevtap Selcuk-Kestel
2023 J jnl
CoRR
Magnus Wiese, Phillip Murray, Ralf Korn
2021 J jnl
J. Mach. Learn. Res.
Robert Sicks, Ralf Korn, Stefanie Schwaar
2021 J jnl
CoRR
Robert Sicks, Stefanie Grimm, Ralf Korn, Ivo Richert
2021 J jnl
CoRR
Magnus Wiese, Ben Wood, Alexandre Pachoud, Ralf Korn, Hans Buehler, Phillip Murray, Lianjun Bai
2020 J jnl
CoRR
Robert Sicks, Ralf Korn, Stefanie Schwaar
2020 J jnl
CoRR
Robert Sicks, Ralf Korn, Stefanie Schwaar
2019 J jnl
CoRR
Magnus Wiese, Robert Knobloch, Ralf Korn
2019 J jnl
Math. Methods Oper. Res.
Simone Göttlich, Ralf Korn, Kerstin Lux
2019 J jnl
CoRR
Magnus Wiese, Robert Knobloch, Ralf Korn, Peter Kretschmer
2019 J jnl
Math. Methods Oper. Res.
Lihua Chen, Ralf Korn
2018 J jnl
Ann. Oper. Res.
Büsra Zeynep Temoçin, Ralf Korn, A. Sevtap Selcuk-Kestel
2018 J jnl
Ann. Oper. Res.
Büsra Zeynep Temoçin, Ralf Korn, A. Sevtap Selcuk-Kestel
2018 J jnl
Oper. Res. Lett.
Sascha Desmettre, Sarah Grün, Ralf Korn
2018 J jnl
Monte Carlo Methods Appl.
Sema Coskun, Ralf Korn
2017 conf
PyHPC@SC
Javier Alejandro Varela, Norbert Wehn, Sascha Desmettre, Ralf Korn
2017 J jnl
Eur. J. Oper. Res.
Ralf Korn, Yaroslav Melnyk, Frank Thomas Seifried
2015 conf
WHPCF@SC
Javier Alejandro Varela, Claus Kestel, Christian de Schryver, Norbert Wehn, Sascha Desmettre, Ralf Korn
2015 A conf
DATE
Christian Brugger, Javier Alejandro Varela, Norbert Wehn, Songyin Tang, Ralf Korn
2015 J jnl
OR Spectr.
Sascha Desmettre, Ralf Korn, Peter Ruckdeschel, Frank Thomas Seifried
2014 conf
CIFEr
Christian Brugger, Christian de Schryver, Norbert Wehn, Steffen Omland, Mario Hefter, Klaus Ritter, Anton Kostiuk, Ralf Korn
2013 J jnl
J. Comput. Appl. Math.
Ralf Korn, Serkan Zeytun
2013 J jnl
Finance Stochastics
Ralf Korn, Stefanie Müller
2012 J jnl
Int. J. Reconfigurable Comput.
Christian de Schryver, Daniel Schmidt, Norbert Wehn, Elke Korn, Henning Marxen, Anton Kostiuk, Ralf Korn
2011 conf
WHPCF@SC
Henning Marxen, Anton Kostiuk, Ralf Korn, Christian de Schryver, Stephan Wurm, Ivan Shcherbakov, Norbert Wehn
2011 conf
ReConFig
Christian de Schryver, Ivan Shcherbakov, Frank Kienle, Norbert Wehn, Henning Marxen, Anton Kostiuk, Ralf Korn
2011 conf
KES (4)
Christian de Schryver, Matthias Jung, Norbert Wehn, Henning Marxen, Anton Kostiuk, Ralf Korn
2010 conf
ReConFig
Christian de Schryver, Daniel Schmidt, Norbert Wehn, Elke Korn, Henning Marxen, Ralf Korn
2009 J jnl
Finance Stochastics
Ralf Korn, Martin Schweizer
2009 J jnl
ERCIM News
Ralf Korn
2008 J jnl
Comput. Manag. Sci.
Ralf Korn
2007 J jnl
SIAM J. Control. Optim.
Ralf Korn, Mogens Steffensen
2005 J jnl
Math. Methods Oper. Res.
Ralf Korn, Olaf Menkens
2004 J jnl
Math. Methods Oper. Res.
Ralf Korn
2002 J jnl
SIAM J. Control. Optim.
Ralf Korn, Holger Kraft
1999 J jnl
Math. Methods Oper. Res.
Ralf Korn, Manfred Schäl
1999 J jnl
Math. Methods Oper. Res.
Ralf Korn
1998 J jnl
Finance Stochastics
Ralf Korn
1998 J jnl
Math. Methods Oper. Res.
Ralf Korn
1997 J jnl
Math. Oper. Res.
Ralf Korn
1997 J jnl
Math. Methods Oper. Res.
Ralf Korn
1995 J jnl
Math. Methods Oper. Res.
Ralf Korn
1995 J jnl
Math. Methods Oper. Res.
Ralf Korn, Siegfried Trautmann
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.