N. Sundararajan

45 papers B 2Journal 35Unranked 7
YearRankTypeTitle / Venue / Authors
2021 J jnl
IFAC J. Syst. Control.
Cheryl Sze Yin Wong, Suresh Sundaram, N. Sundararajan
2020 J jnl
IFAC J. Syst. Control.
Cheryl Sze Yin Wong, Suresh Sundaram, N. Sundararajan
2020 J jnl
CoRR
Nishant Mohanty, Mohitvishnu S. Gadde, Suresh Sundaram, N. Sundararajan, P. B. Sujit
2019 J jnl
Appl. Soft Comput.
Subhrajit Samanta, Suresh Sundaram, J. Senthilnath, N. Sundararajan
2019 J jnl
Appl. Soft Comput.
S. V. Aruna Kumar, B. S. Harish, B. S. Mahanand, N. Sundararajan
2018 J jnl
Inf. Sci.
Cheryl Sze Yin Wong, Abdullah Al-Dujaili, Suresh Sundaram, N. Sundararajan
2016 J jnl
Expert Syst. Appl.
A. K. Das, Suresh Sundaram, N. Sundararajan
2016 J jnl
Neurocomputing
Shirin Dora, K. Subramanian, Suresh Sundaram, N. Sundararajan
2016 J jnl
Swarm Evol. Comput.
Muhammad Rizwan Tanweer, R. Auditya, Suresh Sundaram, N. Sundararajan, N. Srikanth
2016 J jnl
Inf. Sci.
Muhammad Rizwan Tanweer, Suresh Sundaram, N. Sundararajan
2016 J jnl
J. Glob. Optim.
Abdullah Al-Dujaili, Suresh Sundaram, N. Sundararajan
2015 J jnl
Appl. Soft Comput.
Shirin Dora, Suresh Sundaram, N. Sundararajan
2015 B conf
IJCNN
Vigneshwaran Senthilvel, Suresh Sundaram, B. S. Mahanand, N. Sundararajan
2014 B conf
IJCNN
Shirin Dora, Suresh Sundaram, N. Sundararajan
2013 conf
CIMI
Saras Saraswathi, B. S. Mahanand, Andrzej Kloczkowski, Suresh Sundaram, N. Sundararajan
2013 conf
CISDA
Shaik Ismail, Abhay A. Pashilkar, Ramakalyan Ayyagari, N. Sundararajan
2012 J jnl
Neural Networks
Ramaswamy Savitha, Suresh Sundaram, N. Sundararajan
2012 J jnl
Appl. Soft Comput.
Suresh Sundaram, N. Sundararajan
2012 J jnl
Inf. Sci.
Ramaswamy Savitha, Suresh Sundaram, N. Sundararajan
2012 J jnl
Neural Networks
B. S. Mahanand, Suresh Sundaram, N. Sundararajan, M. Aswatha Kumar
2012 J jnl
Neural Comput.
Ramaswamy Savitha, Suresh Sundaram, N. Sundararajan
2011 J jnl
Comput. Geosci.
N. Sundararajan, Ali Al-Lazki
2011 J jnl
Evol. Syst.
Hai-Jun Rong, N. Sundararajan, Guang-Bin Huang, Guang-She Zhao
2011 conf
ISNN (1)
Ramaswamy Savitha, Suresh Sundaram, N. Sundararajan, Hyoung Joong Kim
2010 J jnl
Eng. Appl. Artif. Intell.
Suresh Sundaram, Saras Saraswathi, N. Sundararajan
2009 J jnl
Int. J. Neural Syst.
Ramaswamy Savitha, Suresh Sundaram, N. Sundararajan
2009 J jnl
Neurocomputing
Ramaswamy Savitha, Suresh Sundaram, N. Sundararajan, P. Saratchandran
2008 J jnl
Comput. Geosci.
V. Chakravarthi, N. Sundararajan
2007 conf
ISIC
Sriram Narasimha, Suresh Sundaram, N. Sundararajan
2007 J jnl
Comput. Geosci.
V. Chakravarthi, N. Sundararajan
2007 J jnl
Neurocomputing
Runxuan Zhang, Guang-Bin Huang, N. Sundararajan, P. Saratchandran
2007 J jnl
IEEE ACM Trans. Comput. Biol. Bioinform.
Runxuan Zhang, Guang-Bin Huang, N. Sundararajan, P. Saratchandran
2006 J jnl
Fuzzy Sets Syst.
Hai-Jun Rong, N. Sundararajan, Guang-Bin Huang, P. Saratchandran
2005 J jnl
Comput. Geosci.
V. Chakravarthi, N. Sundararajan
2005 J jnl
Comput. Geosci.
V. Chakravarthi, N. Sundararajan
2004 J jnl
Comput. Geosci.
V. Chakravarthi, N. Sundararajan
2001 J jnl
IEEE Trans. Neural Networks
Deng Jianping, N. Sundararajan, P. Saratchandran
2001 conf
CDC
Jiong-Sang Yee, Jian Liang Wang, N. Sundararajan, Guang-Hong Yang
2000 conf
ICON
Mohit Aiyar, Shefali Nagpal, N. Sundararajan, P. Saratchandran
1998 book
Parallel architectures for artificial neural networks - paradigms and implementations.
N. Sundararajan, P. Saratchandran
1998 J jnl
IEEE Trans. Neural Networks
Yingwei Lu, N. Sundararajan, P. Saratchandran
1997 conf
ICNN
Shou King Foo, P. Saratchandran, N. Sundararajan
1996 J jnl
Autom.
Jianliang Wang, N. Sundararajan
1996 J jnl
J. Intell. Fuzzy Syst.
Shou King Foo, P. Saratchandran, N. Sundararajan
1987 J jnl
Autom.
N. Sundararajan
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.