ghost/docs/RESEARCH_FRAMEWORK.md

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

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

// 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<Pattern> {
        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

// 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

pub struct CorrelationEngine {
    event_buffer: RingBuffer<DetectionEvent>,
    correlation_rules: Vec<CorrelationRule>,
    timeline_analyzer: TimelineAnalyzer,
}

#[derive(Debug, Clone)]
pub struct DetectionEvent {
    pub timestamp: SystemTime,
    pub process_id: u32,
    pub technique: String,
    pub indicators: Vec<String>,
    pub context: EventContext,
}

impl CorrelationEngine {
    pub fn correlate_events(&mut self, new_event: DetectionEvent) -> Vec<CorrelatedIncident> {
        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

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

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

// Standard benchmark datasets for research comparison
pub struct BenchmarkDatasets;

impl BenchmarkDatasets {
    pub fn load_mitre_dataset() -> Result<Dataset, Error> {
        // Load MITRE ATT&CK evaluation dataset
        unimplemented!("Load standardized MITRE evaluation data")
    }
    
    pub fn load_academic_dataset(name: &str) -> Result<Dataset, Error> {
        // 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

pub struct ResearchMetrics {
    detection_rates: HashMap<String, f64>,
    false_positive_rates: HashMap<String, f64>,
    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

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

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

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

use serde::{Serialize, Deserialize};

#[derive(Serialize, Deserialize)]
pub struct ResearchDataExport {
    pub metadata: ExperimentMetadata,
    pub detection_events: Vec<DetectionEvent>,
    pub performance_metrics: PerformanceMetrics,
    pub statistical_summary: StatisticalSummary,
}

impl ResearchDataExport {
    pub fn export_to_format(&self, format: ExportFormat) -> Result<String, Error> {
        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

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<Insight>;
}

pub struct StandardAnalysisPipeline {
    feature_extractor: FeatureExtractor,
    ml_models: Vec<Box<dyn MLModel>>,
    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:

@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

pub struct EthicalDataHandler {
    anonymizer: DataAnonymizer,
    consent_manager: ConsentManager,
    audit_logger: AuditLogger,
}

impl EthicalDataHandler {
    pub fn handle_research_data(&self, data: RawData) -> Result<AnonymizedData, Error> {
        // 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
• Dataset Specifications
• Collaboration Guidelines