# 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)