A Neuromorphic Cognitive Defense System Against Social Engineering and Psychological Manipulation
⚠️ EARLY DEVELOPMENT STAGE
This project is in early conceptual and development phase. Most features described below represent our research vision and planned implementation, not current functionality.
Current Phase: 🚧 Initial Architecture & Research
| Component | Status | Description |
|---|---|---|
| 🧠 Core Architecture | 🔄 Planning | Designing neuromorphic cognitive framework |
| 🎯 Psychological Models | 📖 Research | Studying cognitive vulnerability frameworks |
| 🔬 Initial Prototypes | 🛠️ Development | Building basic NLP processing pipeline |
| 📊 Dataset Collection | 📝 Design | Creating annotation framework |
| 🧪 Validation Framework | 📋 Planning | Designing evaluation methodology |
NeuroSentinel aims to revolutionize cybersecurity defense by creating the first cognitive-aware security system that understands human psychology and predicts manipulation attempts before they succeed.
- Cognitive Modeling: Simulate individual psychological vulnerabilities in real-time
- Neuromorphic Computing: Implement brain-inspired processing for human-like decision making
- Predictive Defense: Anticipate social engineering attacks before they occur
- Explainable Security: Provide neurologically-grounded explanations for security decisions
- Adaptive Learning: Continuously improve through behavioral pattern recognition
- How can we model individual cognitive vulnerabilities computationally?
- What neuromorphic architectures best simulate human decision-making under manipulation?
- How can we predict susceptibility to social engineering attacks?
- What makes security explanations cognitively compelling to humans?
- How do we balance security effectiveness with user privacy?
The following represents our planned system architecture, currently under development:
┌─────────────────────────────────────────────────────────────────────────────┐
│ 🧠 NEUROSENTINEL (PLANNED ARCHITECTURE) │
├─────────────────────────────────────────────────────────────────────────────┤
│ 🎯 Cognitive Executive Layer (Future Development) │
│ ├── Neuromorphic Decision Engine │
│ ├── Adaptive Defense Orchestrator │
│ └── Explainability Generation Module │
├─────────────────────────────────────────────────────────────────────────────┤
│ 🧮 Cognitive Processing Layer (In Research) │
│ ├── Temporal Intention Decoder │
│ ├── Cognitive Vulnerability Simulator │
│ └── Behavioral Pattern Recognition │
├─────────────────────────────────────────────────────────────────────────────┤
│ 🔍 Perceptual Processing Layer (Early Development) │
│ ├── NLP Pipeline (In Progress) │
│ ├── Psychological Trigger Extractor (Planned) │
│ └── Context Enricher (Planned) │
├─────────────────────────────────────────────────────────────────────────────┤
│ 📥 Input Layer (Basic Implementation) │
│ ├── Text Processing (Working) │
│ ├── Data Preprocessing (Working) │
│ └── Quality Assessment (Planned) │
└─────────────────────────────────────────────────────────────────────────────┘
Phase 1: Foundation (Current - 3 months)
- Literature review on cognitive security
- Basic NLP pipeline implementation
- Initial psychological trigger database
- Simple rule-based detection prototype
Phase 2: Cognitive Modeling (3-6 months)
- Implement basic vulnerability assessment
- Develop psychological profile framework
- Create synthetic dataset
- Build initial machine learning models
Phase 3: Neuromorphic Implementation (6-12 months)
- Research spiking neural network architectures
- Implement brain-inspired processing
- Develop temporal decision making
- Create explainability framework
Phase 4: Validation & Optimization (12-18 months)
- Conduct controlled user studies
- Validate with real-world scenarios
- Optimize for performance and accuracy
- Prepare for academic publication
- Detection Accuracy: >90% (Goal)
- False Positive Rate: <5% (Goal)
- Explanation Quality: Human evaluation >80% (Goal)
- Response Time: <100ms (Goal)
neurosentinel/
├── 📁 core/ # Core cognitive architecture (Planned)
│ ├── cognitive_brain.py # Main cognitive processing (Not implemented)
│ ├── vulnerability_sim.py # Vulnerability modeling (Not implemented)
│ └── decision_engine.py # Decision making logic (Not implemented)
├── 📁 nlp/ # NLP processing pipeline (In Progress)
│ ├── preprocessor.py # Text preprocessing (Basic implementation)
│ ├── trigger_extractor.py # Psychological triggers (Planned)
│ └── intent_classifier.py # Intent classification (Planned)
├── 📁 data/ # Data management (In Progress)
│ ├── loaders.py # Data loading utilities (Basic)
│ └── synthetic_gen.py # Synthetic data generation (Planned)
├── 📁 research/ # Research materials (In Progress)
│ ├── notebooks/ # Jupyter notebooks for experiments
│ └── literature/ # Academic papers and references
├── 📁 tests/ # Testing framework (Basic)
├── requirements.txt # Dependencies (Basic)
└── README.md # This file
- Python 3.11+: Primary development language
- PyTorch: Deep learning framework (planned)
- spaCy: NLP processing (in progress)
- Jupyter: Research and experimentation
- pytest: Testing framework
# Clone the repository
git clone https://github.com/IamXeoth/neurosentinel.git
cd neurosentinel
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install basic dependencies
pip install -r requirements.txt
# Run basic tests
python -m pytest tests/ -v- Dual-Process Theory: Understanding automatic vs. controlled cognitive processes
- Cognitive Load Theory: Modeling information processing limitations
- Social Psychology: Principles of influence, persuasion, and compliance
- Behavioral Economics: Cognitive biases and decision-making heuristics
- Neuromorphic Computing: Brain-inspired processing architectures
- Explainable AI: Interpretable machine learning for security decisions
- Behavioral Analytics: Pattern recognition in human behavior
- Adversarial Learning: Modeling attack and defense strategies
We welcome contributions from:
- Researchers in cognitive science, psychology, and cybersecurity
- ML Engineers interested in neuromorphic computing
- Security Professionals with social engineering expertise
- Data Scientists experienced in behavioral analytics
- Fork the repository
- Create a feature branch
- Make your changes
- Test thoroughly
- Submit a pull request
- Literature review and research
- Algorithm implementation
- Dataset creation and annotation
- Testing and validation
- Documentation and examples
- Social Engineering detection methodologies
- Cognitive vulnerability assessment frameworks
- Neuromorphic computing applications
- Explainable AI in security contexts
- Human-computer interaction in cybersecurity
- Novel cognitive security framework
- Neuromorphic architecture for cybersecurity
- Validation methodology for cognitive defenses
- Open-source research platform
- Benchmark datasets and evaluation metrics
- Data Minimization: Collect only necessary information
- Anonymization: Protect individual identities
- Consent: Transparent data usage agreements
- Security: Protect sensitive psychological data
- Beneficence: Ensure research benefits society
- Non-maleficence: Avoid harm to participants
- Justice: Fair distribution of benefits and risks
- Respect: Honor individual autonomy and dignity
- Complete literature review
- Implement basic NLP pipeline
- Create initial psychological trigger database
- Develop simple proof-of-concept
- Establish research methodology
- Implement cognitive vulnerability modeling
- Develop neuromorphic processing components
- Create comprehensive evaluation framework
- Build initial datasets
- Conduct preliminary validation studies
- Deploy fully functional cognitive defense system
- Publish research findings in top-tier conferences
- Establish industry partnerships
- Create educational resources and certification
- Influence cybersecurity standards and practices
Vinícius Araújo Lisboa
Professor | Machine Learning Engineer | Cybersecurity Researcher
Professional Background:
- Academic Position: Professor at Paraíba State Government Education System
- Research Focus: Artificial Intelligence, Machine Learning, Cybersecurity
- Technical Interests: Neuromorphic Computing, Cognitive Security, Explainable AI
Contact Information:
- Email: vinicius.araujo@professor.pb.gov.br
- Website: https://www.viniciuslisboa.com.br/
- LinkedIn: https://www.linkedin.com/in/lisboa-vinicius/
- GitHub: https://github.com/IamXeoth
This project is licensed under the MIT License - see the LICENSE file for details.
- Repository: https://github.com/IamXeoth/neurosentinel
- Documentation: [Coming Soon]
- Research Papers: [Coming Soon]
- Datasets: [Coming Soon]
- Community: [Coming Soon]
Special thanks to:
- The cognitive science and cybersecurity research communities
- Open-source contributors and maintainers
- Academic institutions supporting interdisciplinary research
- Early collaborators and advisors
Last Updated: July 2025 | Version: 0.1.0-dev | Status: Early Development
"Understanding the human mind is the key to protecting it. NeuroSentinel represents our commitment to building security systems that truly understand and protect human cognition."