# 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: ```python 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 ```bash git clone https://huggingface.co/koushikcs09/cognitive-cyber-defense cd cognitive-cyber-defense pip install -r requirements.txt ``` ## Quick Start ### Basic CCD Pipeline ```python 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 ```python 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 ```python from src.auraxp_reasoning.auraxp_orchestrator import AuraXPOrchestrator auraxp = AuraXPOrchestrator() decision = auraxp.investigate_incident(events, attacker_profile) print(decision.narrative) ``` ### Fully Integrated Framework ```python from src.integrated_framework import IntegratedCyberDefenseFramework framework = IntegratedCyberDefenseFramework() report = framework.process_security_events(events) ``` ## Demos ```bash # 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 ```bibtex @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.
