koushikcs09/cognitive-cyber-defense

Cognitive Cyber Defense (CCD) Framework

Overview

The Cognitive Cyber Defense (CCD) Framework is a next-generation cybersecurity paradigm that transcends traditional reactive defense by integrating Generative AI, Theory of Mind (ToM), Deep Learning, and LLM Reasoning into autonomous threat modeling systems.

Key Innovation

Unlike conventional approaches that rely primarily on historical attack patterns, CCD:

• Models attacker cognition: Beliefs, goals, capabilities, and intentions
• Simulates adversarial behavior: Generates plausible attack scenarios
• Anticipates zero-day threats: Predicts attacks before they manifest
• Deploys anticipatory defenses: Proactively reduces risk through intent-aware reasoning
• Applies human-like reasoning: Contextual understanding, deep correlation, intent mapping

Architecture

Layer 5: Defense Orchestration (Predictive, Adaptive, Autonomous)
    |
Layer 4: Neural Enhancement (Transformers, GNNs, GANs, DRL)
    |
Layer 3: Cognitive Modeling (Theory of Mind Attacker Profiles)
    |
Layer 2: AuraXP LLM Reasoning (Contextual, Correlation, Intent)
    |
Layer 1: Data Ingestion (Endpoints, Networks, Cloud, Identities)

Core Components

1. Theory of Mind Attacker Model (src/attacker_model.py)

Inspired by AutoToM (Zhang et al., 2025) and MetaMind (Zhang et al., 2025):

• Attacker Profiles: Cognitive models with beliefs, intents, capabilities
• Bayesian Belief Updating: Updates attacker models based on observed actions
• Intent Inference: Deduces attacker objectives from behavior patterns
• Action Prediction: Predicts next techniques based on cognitive state

Attacker Types: Script Kiddie, Hacktivist, Cybercriminal, APT, Insider, Nation-State

2. Threat Simulation Engine (src/threat_simulator.py)

Inspired by SPADE (Ahmed et al., 2025) and GOAT (Pavlova et al., 2024):

• Attack Chain Generation: Creates multi-phase attack scenarios
• MITRE ATT&CK Integration: Maps to real-world techniques
• Zero-Day Simulation: Models novel vulnerability exploitation
• Impact Assessment: Estimates severity and timeline

3. Defense Orchestrator (src/defense_orchestrator.py)

• Anticipatory Defense: Deploys countermeasures before attacks
• Risk-Based Prioritization: Allocates resources by threat severity
• Adaptive Response: Adjusts strategies based on effectiveness feedback
• Countermeasure Library: Maps techniques to defensive actions

Neural Network Integrations (src/nn_integrations/)

4. Transformer Theory of Mind (transformer_tom.py)

• Architecture: Encoder-Decoder Transformer with multi-head attention
• Purpose: Infer attacker cognition from behavioral sequences
• Outputs: Attacker type, intents, belief embeddings, next action confidence

5. GNN Attack Graph (gnn_attack_graph.py)

• Architecture: GAT (Graph Attention) + GCN hybrid
• Purpose: Analyze infrastructure topology and predict attack paths
• Outputs: Critical nodes, path scores, technique predictions, criticality scores

6. GAN Threat Generator (gan_threat_generator.py)

• Architecture: LSTM Generator + CNN-LSTM Discriminator
• Purpose: Generate synthetic attack sequences for data augmentation
• Outputs: Realistic multi-step attack chains conditioned on attacker type

7. DRL Defense Agent (drl_defense_agent.py)

• Architecture: DQN + Actor-Critic with experience replay
• Purpose: Optimize defense strategies through reinforcement learning
• Outputs: Action probabilities, Q-values, recommended defenses

8. VAE Anomaly Detection (gan_threat_generator.py - VAELatentSpace)

• Architecture: Variational Autoencoder with reparameterization
• Purpose: Detect anomalous attack patterns
• Outputs: Anomaly score, reconstruction error, KL divergence

AuraXP LLM Reasoning Layer (src/auraxp_reasoning/)

9. Contextual Threat Reasoning (contextual_threat_reasoning.py)

Capabilities:

• Weak Signal Detection: Identifies subtle indicators that traditional systems miss
• Behavioral Contextualization: Understands events within user behavior context
• Environmental Awareness: Factors in system state, time, location
• Narrative Generation: Creates human-readable threat stories
• Actionable Intelligence: Produces concrete recommendations, not just alerts

10. Deep Correlation Engine (deep_correlation_engine.py)

Capabilities:

• Cross-System Correlation: Links events across endpoints, networks, cloud, identities
• Temporal Correlation: Finds time-based patterns and sequences
• Threat Intel Integration: Enriches with external threat intelligence
• Behavioral Baseline Deviation: Detects deviations from normal behavior
• Hidden Pattern Discovery: Uncovers non-obvious attack patterns

11. Intent and Impact Mapping (intent_impact_mapper.py)

Capabilities:

• Origin Tracing: Determines attack origin from event patterns
• Intent Classification: Classifies attacker intent (espionage, destruction, etc.)
• Impact Estimation: Quantifies potential business impact
• Timeline Reconstruction: Orders events into coherent attack timeline
• Asset Mapping: Identifies affected assets and their criticality

12. AuraXP Orchestrator (auraxp_orchestrator.py)

Serves as the intelligent "cyber brain" that:

• Ingests security events from all infrastructure
• Applies contextual threat reasoning to understand weak signals
• Performs deep correlation across systems and threat intel
• Maps intent and impact for autonomous investigation
• Generates actionable decisions with human-readable narratives

Integrated Framework (src/integrated_framework.py)

The IntegratedCyberDefenseFramework combines all layers:

from src.integrated_framework import IntegratedCyberDefenseFramework

framework = IntegratedCyberDefenseFramework()

# Process security events through complete pipeline
report = framework.process_security_events(events)

# Report includes:
# - AuraXP contextual analysis and intent mapping
# - CCD attacker profiling and threat simulation
# - Neural-enhanced predictions
# - Defense strategy with prioritized actions

Installation

git clone https://huggingface.co/koushikcs09/cognitive-cyber-defense
cd cognitive-cyber-defense
pip install -r requirements.txt

Quick Start

Basic CCD Pipeline

from src.cognitive_framework import CognitiveCyberDefense

ccd = CognitiveCyberDefense()
ccd.register_attacker("apt_001", "apt", skill_level=0.9, resources=0.8)
report = ccd.run_full_pipeline("apt_001", "corporate_network")
print(ccd.export_report(report, format="markdown"))

Neural-Enhanced Framework

from src.nn_integrations.enhanced_framework import EnhancedCognitiveCyberDefense

 eccd = EnhancedCognitiveCyberDefense()
cognition = eccd.infer_attacker_cognition(behavior_history)
graph_analysis = eccd.analyze_attack_graph(node_features, adj_matrix)
synthetic_threats = eccd.generate_synthetic_threats(attacker_type=3)

AuraXP Reasoning Layer

from src.auraxp_reasoning.auraxp_orchestrator import AuraXPOrchestrator

auraxp = AuraXPOrchestrator()
decision = auraxp.investigate_incident(events, attacker_profile)
print(decision.narrative)

Fully Integrated Framework

from src.integrated_framework import IntegratedCyberDefenseFramework

framework = IntegratedCyberDefenseFramework()
report = framework.process_security_events(events)

Demos

# Basic CCD demo
python src/demo.py

# Neural network components demo
python src/nn_integrations/demo_nn.py

# AuraXP reasoning layer demo
python src/auraxp_reasoning/demo_auraxp.py

# Fully integrated demo
python src/demo_integrated.py

Research Foundation

Paper	Contribution
AutoToM (Zhang et al., 2025)	Automated Bayesian Inverse Planning for ToM
MetaMind (Zhang et al., 2025)	Multi-agent metacognitive social reasoning
EnigmaToM (Xu et al., 2025)	Neural Knowledge Base for ToM reasoning
SPADE (Ahmed et al., 2025)	Generative AI for adaptive cyber deception
GOAT (Pavlova et al., 2024)	Automated agentic red teaming
Intent-FT (Yeo et al., 2025)	Intent-aware LLM defense
PromptSleuth (Wang et al., 2025)	Semantic intent invariance detection
AttackSeqBench (Yong et al., 2025)	Sequential attack pattern understanding
CyberPal 2.0 (Levi et al., 2025)	Cybersecurity-expert small language models

Applications

1. Predictive Threat Intelligence: Anticipate attacker moves before execution
2. Adaptive Defense Planning: Dynamically adjust security posture
3. Zero-Day Preparedness: Simulate unknown vulnerability exploitation
4. Red Team Automation: Generate realistic attack scenarios
5. Security Training: Create cognitively realistic simulations
6. Autonomous Investigation: Human-like reasoning at machine speed
7. Cross-System Correlation: Connect weak signals across infrastructure
8. Intent-Aware Response: Defend based on attacker objectives

Citation

@article{cognitivecyberdefense2025,
  title={Cognitive Cyber Defense: Integrating Generative AI, Theory of Mind, and LLM Reasoning for Predictive Threat Modeling},
  author={Cognitive Cyber Defense Research},
  year={2025}
}

License

MIT License - See LICENSE file for details.