James M. Hogan

47 papers B 4C 5Misc 9Journal 16Unranked 10
YearRankTypeTitle / Venue / Authors
2023 J jnl
IEEE ACM Trans. Comput. Biol. Bioinform.
Dhananjay Kimothi, Pravesh Biyani, James M. Hogan, Melissa J. Davis
2022 J jnl
IEEE Trans. Vis. Comput. Graph.
Markus Rittenbruch, Kellie Vella, Margot Brereton, James M. Hogan, Daniel Johnson, Julian Heinrich, Seán I. O'Donoghue
2020 J jnl
Algorithms
Sarah Pilz, Florian Porrmann, Martin Kaiser, Jens Hagemeyer, James M. Hogan, Ulrich Rückert
2019 J jnl
Briefings Bioinform.
Guillaume Bernard, Cheong Xin Chan, Yao-ban Chan, Xin-Yi Chua, Yingnan Cong, James M. Hogan, Stefan R. Maetschke, Mark A. Ragan
2018 Misc conf
ADCS
Lawrence Buckingham, Shlomo Geva, James M. Hogan
2018 J jnl
BMC Bioinform.
Timothy Chappell, Shlomo Geva, James M. Hogan, Flavia Huygens, Irani U. Rathnayake, Stephen Rudd, Wayne Kelly, Dimitri Perrin
2018 Misc conf
ADCS
Timothy Chappell, Shlomo Geva, James M. Hogan, Dimitri Perrin
2017 Misc conf
ADCS
Timothy Chappell, Shlomo Geva, James M. Hogan
2017 conf
SPAWC
Dhananjay Kimothi, Ankita Shukla, Pravesh Biyani, Saket Anand, James M. Hogan
2017 conf
BIBM
Timothy Chappell, Shlomo Geva, James M. Hogan, Flavia Huygens, Wayne Kelly, Dimitri Perrin
2017 conf
eScience
Lawrence Buckingham, Timothy Chappell, James M. Hogan, Shlomo Geva
2016 conf
CHI PLAY (Companion)
Holly Hutson, Daniel M. Johnson, Xin-Yi Chua, James M. Hogan, Margot Brereton, Markus Rittenbruch, Benedetta Frida Baldi, Seán I. O'Donoghue
2016 J jnl
CoRR
Dhananjay Kimothi, Akshay Soni, Pravesh Biyani, James M. Hogan
2016 J jnl
Comput. Graph. Forum
Julian Heinrich, Jenny Vuong, Christopher J. Hammang, A. Wu, Markus Rittenbruch, James M. Hogan, Margot Brereton, Seán I. O'Donoghue
2016 Misc conf
OZCHI
Zachary Fitz-Walter, Seán I. O'Donoghue, Benedetta Frida Baldi, James M. Hogan, Daniel M. Johnson, Margot Brereton
2015 Misc conf
ICCS
Xin-Yi Chua, Lawrence Buckingham, James M. Hogan, Pavel S. Novichkov
2015 B conf
ITiCSE
Samuel A. Brian, Richard N. Thomas, James M. Hogan, Colin J. Fidge
2015 Misc conf
OZCHI
Samuel Thomas Smith, James M. Hogan, Markus Rittenbruch, Daniel M. Johnson, Margot Brereton
2014 Misc conf
ICCS
Lawrence Buckingham, James M. Hogan
2014 Misc conf
ICCS
James M. Hogan, Timothy Peut
2014 Misc conf
AusDM
Lawrence Buckingham, James M. Hogan, Shlomo Geva, Wayne Kelly
2013 B conf
e-Science
James M. Hogan, Wayne Kelly, Felicity Newell
2013 conf
Australian Software Engineering Conference
Jaspar Cahill, James M. Hogan, Richard N. Thomas
2011 J jnl
Concurr. Comput. Pract. Exp.
James M. Hogan, Jiro Sumitomo, Paul Roe, Felicity Newell
2011 book
Joachim Diederich, Cengiz Günay, James M. Hogan
2008 conf
eScience
Jiro Sumitomo, James M. Hogan, Felicity Newell, Paul Roe
2008 B conf
CCGRID
Lawrence Buckingham, James M. Hogan, Paul Roe, Jiro Sumitomo, Michael W. Towsey
2008 conf
ICCS (3)
Lawrence Buckingham, James M. Hogan, Paul Roe, Jiro Sumitomo, Michael W. Towsey
2008 J jnl
Comput. Biol. Chem.
Michael W. Towsey, Peter Timms, James M. Hogan, Sarah A. Mathews
2006 J jnl
Bioinform.
James J. Gordon, Michael W. Towsey, James M. Hogan, Sarah A. Mathews, Peter Timms
2006 J jnl
Int. J. Neural Syst.
Michael W. Towsey, James J. Gordon, James M. Hogan
2005 C conf
ACE
James M. Hogan, Richard N. Thomas
2005 C ed.
IDEAL
Marcus Gallagher, James M. Hogan, Frédéric Maire
2005 conf
Australian Software Engineering Conference
Andrew Marrington, James M. Hogan, Richard N. Thomas
2005 C conf
ACE
James M. Hogan, Glenn Smith, Richard N. Thomas
2004 ed.
ACSW
James M. Hogan, Paul Montague, Martin K. Purvis, Chris Steketee
2004 conf
ACSW
Tony R. Sahama, Chris Ho-Stuart, James M. Hogan
2004 conf
ACSW
James M. Hogan, Chris Ho-Stuart, Bihn Pham
2002 J jnl
Neurocomputing
James M. Hogan
2002
James M. Hogan
2002 C conf
APSEC
Jaspar Cahill, James M. Hogan, Richard N. Thomas
2002 C conf
APSEC
James M. Hogan, Glenn Smith, Richard N. Thomas
2001 J jnl
Int. J. Neural Syst.
James M. Hogan, Joachim Diederich
1998 B conf
CoNLL
James M. Hogan, Joachim Diederich, Gerard D. Finn
1997 J jnl
Aust. Comput. J.
James M. Hogan, Peter C. J. James
1993 J jnl
Comput. Secur.
Helen A. Bergen, James M. Hogan
1992 J jnl
Comput. Secur.
Helen A. Bergen, James M. Hogan
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.