Ralf Moos

43 papers Journal 43
YearRankTypeTitle / Venue / Authors
2024 J jnl
Sensors
Stefanie Walter, Johanna Baumgärtner, Gunter Hagen, Daniela Schönauer-Kamin, Jaroslaw Kita, Ralf Moos
2024 J jnl
Sensors
Carsten Steiner, Vladimir Malashchuk, David J. Kubinski, Gunter Hagen, Ralf Moos
2024 J jnl
Sensors
Nils Donker, Daniela Schönauer-Kamin, Ralf Moos
2023 J jnl
Sensors
Gunter Hagen, Julia Herrmann, Xin Zhang, Heinz Kohler, Ingo Hartmann, Ralf Moos
2023 J jnl
Sensors
Carsten Steiner, Thomas Wöhrl, Monika Steiner, Jaroslaw Kita, Andreas Müller, Hessam Eisazadeh, Ralf Moos, Gunter Hagen
2023 J jnl
Sensors
Carsten Steiner, Simon Püls, Murat Bektas, Andreas Müller, Gunter Hagen, Ralf Moos
2023 J jnl
Sensors
Stefanie Walter, Peter Schwanzer, Gunter Hagen, Hans-Peter Rabl, Markus Dietrich, Ralf Moos
2022 J jnl
Sensors
Stefanie Walter, Peter Schwanzer, Carsten Steiner, Gunter Hagen, Hans-Peter Rabl, Markus Dietrich, Ralf Moos
2020 J jnl
Sensors
Carsten Steiner, Stefanie Walter, Vladimir Malashchuk, Gunter Hagen, Iurii Kogut, Holger Fritze, Ralf Moos
2020 J jnl
Sensors
Stefanie Walter, Peter Schwanzer, Gunter Hagen, Gerhard Haft, Hans-Peter Rabl, Markus Dietrich, Ralf Moos
2019 J jnl
Sensors
Carsten Steiner, Vladimir Malashchuk, David J. Kubinski, Gunter Hagen, Ralf Moos
2019 J jnl
Sensors
Michaela Schubert, Christian Münch, Sophie Schuurman, Véronique Poulain, Jaroslaw Kita, Ralf Moos
2019 J jnl
Sensors
Ricarda Wagner, Daniela Schönauer-Kamin, Ralf Moos
2018 J jnl
Sensors
Gunter Hagen, Christoph Spannbauer, Markus Feulner, Jaroslaw Kita, Andreas Müller, Ralf Moos
2018 J jnl
Sensors
Michaela Schubert, Christian Münch, Sophie Schuurman, Véronique Poulain, Jaroslaw Kita, Ralf Moos
2018 J jnl
Sensors
Julia Metzner, Katrin Luckert, Karin Lemuth, Martin Hämmerle, Ralf Moos
2017 J jnl
Sensors
Markus Feulner, Gunter Hagen, Kathrin Hottner, Sabrina Redel, Andreas Müller, Ralf Moos
2017 J jnl
Sensors
Andreas Bogner, Carsten Steiner, Stefanie Walter, Jaroslaw Kita, Gunter Hagen, Ralf Moos
2017 J jnl
Sensors
Jörg Exner, Gaby Albrecht, Daniela Schönauer-Kamin, Jaroslaw Kita, Ralf Moos
2017 J jnl
Sensors
Markus Dietrich, Gunter Hagen, Willibald Reitmeier, Katharina Burger, Markus Hien, Philippe Grass, David J. Kubinski, Jacobus H. Visser, Ralf Moos
2017 J jnl
Sensors
Markus Dietrich, Gunter Hagen, Willibald Reitmeier, Katharina Burger, Markus Hien, Philippe Grass, David J. Kubinski, Jacobus H. Visser, Ralf Moos
2016 J jnl
Microelectron. Reliab.
Dominique Ortolino, Jaroslaw Kita, Karin Beart, Roland Wurm, S. Kleinewig, A. Pletsch, Ralf Moos
2015 J jnl
Sensors
Markus Feulner, Gunter Hagen, Andreas Müller, Andreas Schott, Christian Zöllner, Dieter Brüggemann, Ralf Moos
2015 J jnl
Sensors
Peirong Chen, Simon Schönebaum, Thomas Simons, Dieter Rauch, Markus Dietrich, Ralf Moos, Ulrich Simon
2015 J jnl
Sensors
Markus Dietrich, Christoph Jahn, Peter Lanzerath, Ralf Moos
2015 J jnl
Sensors
Peter Fremerey, Andreas Jess, Ralf Moos
2014 J jnl
Sensors
Markus Dietrich, Dieter Rauch, Adrian Porch, Ralf Moos
2014 J jnl
IEEE Trans. Control. Syst. Technol.
Sebastian Schodel, Ralf Moos, Martin Votsmeier, Gerhard Fischerauer
2013 J jnl
Sensors
Andrea Groß, Michael Kremling, Isabella Marr, David J. Kubinski, Jacobus H. Visser, Harry L. Tuller, Ralf Moos
2013 J jnl
Sensors
Daniela Schönauer-Kamin, Maximilian Fleischer, Ralf Moos
2013 J jnl
Sensors
Sabine Fischer, Daniela Schönauer-Kamin, Roland Pohle, Maximilian Fleischer, Ralf Moos
2012 J jnl
Sensors
Andrea Groß, Gregor Beulertz, Isabella Marr, David J. Kubinski, Jaco H. Visser, Ralf Moos
2012 J jnl
Sensors
Andrea Groß, Miriam Richter, David J. Kubinski, Jacobus H. Visser, Ralf Moos
2011 J jnl
Sensors
Noriya Izu, Gunter Hagen, Daniela Schönauer, Ulla Röder-Roith, Ralf Moos
2011 J jnl
Sensors
Peter Fremerey, Sebastian Reiß, Andrea Geupel, Gerhard Fischerauer, Ralf Moos
2011 J jnl
Microelectron. Reliab.
Dominique Ortolino, Jaroslaw Kita, Roland Wurm, Emmanuel Blum, Karin Beart, Ralf Moos
2011 J jnl
Sensors
Isabella Marr, Sebastian Reiß, Gunter Hagen, Ralf Moos
2011 J jnl
Sensors
Ralf Moos, Noriya Izu, Frank Rettig, Sebastian Reiß, Woosuck Shin, Ichiro Matsubara
2010 J jnl
Sensors
Ralf Moos
2010 J jnl
Sensors
Gunter Hagen, Constanze Feistkorn, Sven Wiegärtner, Andreas Heinrich, Dieter Brüggemann, Ralf Moos
2009 J jnl
Sensors
Sabine Achmann, Gunter Hagen, Jaroslaw Kita, Itamar M. Malkowsky, Christoph Kiener, Ralf Moos
2009 J jnl
Sensors
Diana Biskupski, Andrea Geupel, Kerstin Wiesner, Maximilian Fleischer, Ralf Moos
2009 J jnl
Sensors
Ralf Moos, Kathy Sahner, Maximilian Fleischer, Ulrich Guth, Nicolae Barsan, Udo Weimar
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.