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document research framework and analysis methods

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Adir Shitrit
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# 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<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
```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<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
```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<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
```rust
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
```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<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
```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<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:
```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<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](docs/research/PAPERS.md)
- [Dataset Specifications](docs/research/DATASETS.md)
- [Collaboration Guidelines](docs/research/COLLABORATION.md)