diff --git a/docs/RESEARCH_FRAMEWORK.md b/docs/RESEARCH_FRAMEWORK.md new file mode 100644 index 0000000..d248879 --- /dev/null +++ b/docs/RESEARCH_FRAMEWORK.md @@ -0,0 +1,458 @@ +# Security Research Framework + +## Academic Collaboration & Research + +Ghost serves as a research platform for studying process injection techniques and developing novel detection methodologies. This framework supports security researchers, academics, and threat hunters. + +## Research Capabilities + +### 1. Technique Analysis Database + +```rust +use ghost_core::research::{TechniqueDatabase, MitreAttack}; + +// Map detections to MITRE ATT&CK framework +let technique_db = TechniqueDatabase::new(); +technique_db.add_mapping("T1055", "Process Injection"); +technique_db.add_mapping("T1055.001", "Dynamic-link Library Injection"); +technique_db.add_mapping("T1055.002", "Portable Executable Injection"); +``` + +### 2. Behavioral Pattern Mining + +```rust +// Extract behavioral patterns from detection data +pub struct PatternMiner { + sequence_analyzer: SequenceAnalyzer, + anomaly_clusterer: AnomalyClusterer, + temporal_correlator: TemporalCorrelator, +} + +impl PatternMiner { + pub fn mine_injection_patterns(&self, events: &[DetectionEvent]) -> Vec { + let sequences = self.sequence_analyzer.extract_sequences(events); + let clusters = self.anomaly_clusterer.cluster_anomalies(&sequences); + self.temporal_correlator.correlate_patterns(clusters) + } +} +``` + +### 3. Adversarial Testing Framework + +```rust +// Test detection capabilities against known techniques +pub struct AdversarialTester { + technique_library: TechniqueLibrary, + evasion_simulator: EvasionSimulator, + effectiveness_measurer: EffectivenessMeasurer, +} + +impl AdversarialTester { + pub fn test_detection_coverage(&self) -> CoverageReport { + let techniques = self.technique_library.get_all_techniques(); + let mut results = Vec::new(); + + for technique in techniques { + let evasions = self.evasion_simulator.generate_variants(&technique); + let detection_rate = self.effectiveness_measurer.measure(&evasions); + results.push(TestResult { technique, detection_rate }); + } + + CoverageReport::new(results) + } +} +``` + +## Data Collection & Analysis + +### Event Correlation Engine + +```rust +pub struct CorrelationEngine { + event_buffer: RingBuffer, + correlation_rules: Vec, + timeline_analyzer: TimelineAnalyzer, +} + +#[derive(Debug, Clone)] +pub struct DetectionEvent { + pub timestamp: SystemTime, + pub process_id: u32, + pub technique: String, + pub indicators: Vec, + pub context: EventContext, +} + +impl CorrelationEngine { + pub fn correlate_events(&mut self, new_event: DetectionEvent) -> Vec { + self.event_buffer.push(new_event.clone()); + + let mut incidents = Vec::new(); + for rule in &self.correlation_rules { + if let Some(incident) = rule.evaluate(&self.event_buffer, &new_event) { + incidents.push(incident); + } + } + + incidents + } +} +``` + +### Statistical Analysis Module + +```rust +use ndarray::{Array1, Array2}; +use linfa::prelude::*; + +pub struct StatisticalAnalyzer { + feature_extractor: FeatureExtractor, + dimensionality_reducer: PCA, + classifier: LogisticRegression, +} + +impl StatisticalAnalyzer { + pub fn analyze_detection_patterns(&self, data: &[ProcessData]) -> AnalysisReport { + // Extract features for statistical analysis + let features = self.feature_extractor.extract_batch(data); + + // Apply dimensionality reduction + let reduced_features = self.dimensionality_reducer.transform(&features); + + // Classify injection likelihood + let predictions = self.classifier.predict(&reduced_features); + + AnalysisReport { + feature_importance: self.calculate_feature_importance(&features), + classification_accuracy: self.calculate_accuracy(&predictions), + statistical_significance: self.calculate_significance(&features), + recommendations: self.generate_recommendations(&predictions), + } + } +} +``` + +## Research Datasets + +### Synthetic Data Generation + +```rust +pub struct SyntheticDataGenerator { + process_simulator: ProcessSimulator, + injection_simulator: InjectionSimulator, + noise_generator: NoiseGenerator, +} + +impl SyntheticDataGenerator { + pub fn generate_training_dataset(&self, config: DatasetConfig) -> Dataset { + let mut samples = Vec::new(); + + // Generate clean processes + for _ in 0..config.clean_samples { + let process = self.process_simulator.generate_clean_process(); + samples.push(LabeledSample { + data: process, + label: Label::Clean, + }); + } + + // Generate injected processes + for technique in &config.injection_techniques { + for _ in 0..config.samples_per_technique { + let process = self.injection_simulator.simulate_injection(technique); + samples.push(LabeledSample { + data: process, + label: Label::Injected(technique.clone()), + }); + } + } + + // Add realistic noise + for sample in &mut samples { + self.noise_generator.add_noise(&mut sample.data); + } + + Dataset::new(samples) + } +} +``` + +### Benchmark Datasets + +```rust +// Standard benchmark datasets for research comparison +pub struct BenchmarkDatasets; + +impl BenchmarkDatasets { + pub fn load_mitre_dataset() -> Result { + // Load MITRE ATT&CK evaluation dataset + unimplemented!("Load standardized MITRE evaluation data") + } + + pub fn load_academic_dataset(name: &str) -> Result { + // Load academic research datasets + match name { + "injection_patterns_2024" => self.load_injection_patterns(), + "evasion_techniques_2024" => self.load_evasion_techniques(), + "behavioral_anomalies_2024" => self.load_behavioral_anomalies(), + _ => Err(Error::DatasetNotFound), + } + } +} +``` + +## Publication & Collaboration + +### Research Metrics + +```rust +pub struct ResearchMetrics { + detection_rates: HashMap, + false_positive_rates: HashMap, + performance_metrics: PerformanceMetrics, + coverage_analysis: CoverageAnalysis, +} + +impl ResearchMetrics { + pub fn generate_publication_data(&self) -> PublicationData { + PublicationData { + methodology: self.describe_methodology(), + experimental_setup: self.describe_setup(), + results: self.compile_results(), + statistical_analysis: self.perform_statistical_tests(), + reproducibility_package: self.create_reproducibility_package(), + } + } + + pub fn export_for_publication(&self, format: PublicationFormat) -> String { + match format { + PublicationFormat::LaTeX => self.generate_latex_tables(), + PublicationFormat::CSV => self.export_csv_data(), + PublicationFormat::JSON => self.export_json_data(), + } + } +} +``` + +### Collaborative Research Platform + +```rust +pub struct CollaborationPlatform { + experiment_registry: ExperimentRegistry, + data_sharing_service: DataSharingService, + result_verification: ResultVerification, +} + +impl CollaborationPlatform { + pub fn register_experiment(&mut self, experiment: Experiment) -> ExperimentId { + let id = self.experiment_registry.register(experiment); + self.data_sharing_service.create_workspace(id); + id + } + + pub fn share_results(&self, experiment_id: ExperimentId, results: Results) -> Result<(), Error> { + // Verify result integrity + self.result_verification.verify(&results)?; + + // Share with registered collaborators + self.data_sharing_service.publish_results(experiment_id, results)?; + + Ok(()) + } +} +``` + +## Educational Components + +### Interactive Learning Modules + +```rust +pub struct EducationalFramework { + technique_simulator: TechniqueSimulator, + guided_analysis: GuidedAnalysis, + assessment_engine: AssessmentEngine, +} + +impl EducationalFramework { + pub fn create_learning_scenario(&self, technique: &str) -> LearningScenario { + LearningScenario { + description: self.get_technique_description(technique), + simulated_environment: self.technique_simulator.create_scenario(technique), + analysis_steps: self.guided_analysis.generate_steps(technique), + assessment_criteria: self.assessment_engine.get_criteria(technique), + } + } + + pub fn evaluate_student_analysis(&self, scenario_id: u64, analysis: StudentAnalysis) -> Assessment { + let criteria = self.assessment_engine.get_criteria_for_scenario(scenario_id); + self.assessment_engine.evaluate(analysis, criteria) + } +} +``` + +### Technique Documentation Generator + +```rust +pub struct TechniqueDocGenerator { + technique_analyzer: TechniqueAnalyzer, + documentation_builder: DocumentationBuilder, + example_generator: ExampleGenerator, +} + +impl TechniqueDocGenerator { + pub fn generate_technique_guide(&self, technique: &InjectionTechnique) -> TechniqueGuide { + TechniqueGuide { + overview: self.technique_analyzer.analyze_technique(technique), + detection_methods: self.technique_analyzer.get_detection_methods(technique), + code_examples: self.example_generator.generate_examples(technique), + countermeasures: self.technique_analyzer.get_countermeasures(technique), + references: self.technique_analyzer.get_academic_references(technique), + } + } +} +``` + +## Research APIs + +### Data Export API + +```rust +use serde::{Serialize, Deserialize}; + +#[derive(Serialize, Deserialize)] +pub struct ResearchDataExport { + pub metadata: ExperimentMetadata, + pub detection_events: Vec, + pub performance_metrics: PerformanceMetrics, + pub statistical_summary: StatisticalSummary, +} + +impl ResearchDataExport { + pub fn export_to_format(&self, format: ExportFormat) -> Result { + match format { + ExportFormat::JSON => serde_json::to_string_pretty(self).map_err(Error::from), + ExportFormat::CSV => self.to_csv(), + ExportFormat::Parquet => self.to_parquet(), + ExportFormat::HDF5 => self.to_hdf5(), + } + } +} +``` + +### Analysis Pipeline API + +```rust +pub trait AnalysisPipeline { + fn process_detection_data(&self, data: &[DetectionEvent]) -> AnalysisResult; + fn extract_features(&self, events: &[DetectionEvent]) -> FeatureMatrix; + fn apply_ml_models(&self, features: &FeatureMatrix) -> PredictionResults; + fn generate_insights(&self, predictions: &PredictionResults) -> Vec; +} + +pub struct StandardAnalysisPipeline { + feature_extractor: FeatureExtractor, + ml_models: Vec>, + insight_generator: InsightGenerator, +} + +impl AnalysisPipeline for StandardAnalysisPipeline { + fn process_detection_data(&self, data: &[DetectionEvent]) -> AnalysisResult { + let features = self.extract_features(data); + let predictions = self.apply_ml_models(&features); + let insights = self.generate_insights(&predictions); + + AnalysisResult { + features, + predictions, + insights, + metadata: self.generate_metadata(), + } + } +} +``` + +## Citation & Attribution + +When using Ghost for research, please cite: + +```bibtex +@software{ghost_detection_framework, + title={Ghost: Cross-Platform Process Injection Detection Framework}, + author={Security Research Team}, + year={2024}, + url={https://github.com/pandaadir05/ghost}, + version={1.0.0} +} +``` + +## Research Ethics & Responsible Disclosure + +### Ethical Guidelines + +1. **Responsible Research**: Use Ghost only for legitimate security research +2. **Data Privacy**: Anonymize sensitive data in publications +3. **Disclosure Policy**: Report vulnerabilities through proper channels +4. **Academic Integrity**: Properly attribute prior work and collaborations + +### Data Handling + +```rust +pub struct EthicalDataHandler { + anonymizer: DataAnonymizer, + consent_manager: ConsentManager, + audit_logger: AuditLogger, +} + +impl EthicalDataHandler { + pub fn handle_research_data(&self, data: RawData) -> Result { + // Verify consent for data usage + self.consent_manager.verify_consent(&data)?; + + // Anonymize sensitive information + let anonymized = self.anonymizer.anonymize(data)?; + + // Log handling for audit trail + self.audit_logger.log_data_handling(&anonymized); + + Ok(anonymized) + } +} +``` + +## Community Contributions + +### Research Collaboration + +- **Open Source Contributions**: Submit improvements via GitHub +- **Academic Partnerships**: Collaborate on research projects +- **Industry Collaboration**: Partner with security vendors +- **Conference Presentations**: Present findings at security conferences + +### Contribution Guidelines + +1. **Code Quality**: Follow Rust best practices +2. **Documentation**: Comprehensive documentation required +3. **Testing**: Include unit and integration tests +4. **Benchmarking**: Provide performance benchmarks +5. **Research Value**: Demonstrate novel research contributions + +## Future Research Directions + +### Emerging Techniques + +- **AI-Powered Evasion**: Detection of AI-generated injection techniques +- **Hardware-Assisted Injection**: Analysis of hardware-based attacks +- **Cloud-Native Threats**: Container and serverless injection techniques +- **IoT Security**: Process injection in embedded systems + +### Advanced Analytics + +- **Graph Neural Networks**: Process relationship modeling +- **Temporal Analysis**: Time-series detection modeling +- **Federated Learning**: Distributed detection model training +- **Adversarial ML**: Robust ML models against evasion + +For additional research resources, see: +- [Academic Papers](docs/research/PAPERS.md) +- [Dataset Specifications](docs/research/DATASETS.md) +- [Collaboration Guidelines](docs/research/COLLABORATION.md) \ No newline at end of file