458 lines
14 KiB
Markdown
458 lines
14 KiB
Markdown
# Security Research Framework
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## Academic Collaboration & Research
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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.
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## Research Capabilities
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### 1. Technique Analysis Database
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```rust
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use ghost_core::research::{TechniqueDatabase, MitreAttack};
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// Map detections to MITRE ATT&CK framework
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let technique_db = TechniqueDatabase::new();
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technique_db.add_mapping("T1055", "Process Injection");
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technique_db.add_mapping("T1055.001", "Dynamic-link Library Injection");
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technique_db.add_mapping("T1055.002", "Portable Executable Injection");
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```
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### 2. Behavioral Pattern Mining
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```rust
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// Extract behavioral patterns from detection data
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pub struct PatternMiner {
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sequence_analyzer: SequenceAnalyzer,
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anomaly_clusterer: AnomalyClusterer,
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temporal_correlator: TemporalCorrelator,
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}
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impl PatternMiner {
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pub fn mine_injection_patterns(&self, events: &[DetectionEvent]) -> Vec<Pattern> {
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let sequences = self.sequence_analyzer.extract_sequences(events);
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let clusters = self.anomaly_clusterer.cluster_anomalies(&sequences);
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self.temporal_correlator.correlate_patterns(clusters)
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}
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}
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```
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### 3. Adversarial Testing Framework
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```rust
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// Test detection capabilities against known techniques
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pub struct AdversarialTester {
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technique_library: TechniqueLibrary,
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evasion_simulator: EvasionSimulator,
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effectiveness_measurer: EffectivenessMeasurer,
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}
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impl AdversarialTester {
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pub fn test_detection_coverage(&self) -> CoverageReport {
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let techniques = self.technique_library.get_all_techniques();
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let mut results = Vec::new();
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for technique in techniques {
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let evasions = self.evasion_simulator.generate_variants(&technique);
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let detection_rate = self.effectiveness_measurer.measure(&evasions);
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results.push(TestResult { technique, detection_rate });
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}
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CoverageReport::new(results)
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}
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}
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```
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## Data Collection & Analysis
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### Event Correlation Engine
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```rust
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pub struct CorrelationEngine {
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event_buffer: RingBuffer<DetectionEvent>,
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correlation_rules: Vec<CorrelationRule>,
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timeline_analyzer: TimelineAnalyzer,
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}
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#[derive(Debug, Clone)]
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pub struct DetectionEvent {
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pub timestamp: SystemTime,
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pub process_id: u32,
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pub technique: String,
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pub indicators: Vec<String>,
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pub context: EventContext,
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}
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impl CorrelationEngine {
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pub fn correlate_events(&mut self, new_event: DetectionEvent) -> Vec<CorrelatedIncident> {
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self.event_buffer.push(new_event.clone());
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let mut incidents = Vec::new();
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for rule in &self.correlation_rules {
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if let Some(incident) = rule.evaluate(&self.event_buffer, &new_event) {
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incidents.push(incident);
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}
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}
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incidents
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}
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}
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```
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### Statistical Analysis Module
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```rust
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use ndarray::{Array1, Array2};
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use linfa::prelude::*;
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pub struct StatisticalAnalyzer {
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feature_extractor: FeatureExtractor,
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dimensionality_reducer: PCA,
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classifier: LogisticRegression,
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}
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impl StatisticalAnalyzer {
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pub fn analyze_detection_patterns(&self, data: &[ProcessData]) -> AnalysisReport {
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// Extract features for statistical analysis
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let features = self.feature_extractor.extract_batch(data);
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// Apply dimensionality reduction
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let reduced_features = self.dimensionality_reducer.transform(&features);
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// Classify injection likelihood
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let predictions = self.classifier.predict(&reduced_features);
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AnalysisReport {
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feature_importance: self.calculate_feature_importance(&features),
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classification_accuracy: self.calculate_accuracy(&predictions),
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statistical_significance: self.calculate_significance(&features),
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recommendations: self.generate_recommendations(&predictions),
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}
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}
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}
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```
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## Research Datasets
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### Synthetic Data Generation
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```rust
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pub struct SyntheticDataGenerator {
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process_simulator: ProcessSimulator,
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injection_simulator: InjectionSimulator,
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noise_generator: NoiseGenerator,
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}
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impl SyntheticDataGenerator {
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pub fn generate_training_dataset(&self, config: DatasetConfig) -> Dataset {
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let mut samples = Vec::new();
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// Generate clean processes
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for _ in 0..config.clean_samples {
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let process = self.process_simulator.generate_clean_process();
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samples.push(LabeledSample {
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data: process,
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label: Label::Clean,
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});
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}
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// Generate injected processes
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for technique in &config.injection_techniques {
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for _ in 0..config.samples_per_technique {
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let process = self.injection_simulator.simulate_injection(technique);
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samples.push(LabeledSample {
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data: process,
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label: Label::Injected(technique.clone()),
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});
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}
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}
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// Add realistic noise
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for sample in &mut samples {
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self.noise_generator.add_noise(&mut sample.data);
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}
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Dataset::new(samples)
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}
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}
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```
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### Benchmark Datasets
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```rust
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// Standard benchmark datasets for research comparison
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pub struct BenchmarkDatasets;
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impl BenchmarkDatasets {
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pub fn load_mitre_dataset() -> Result<Dataset, Error> {
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// Load MITRE ATT&CK evaluation dataset
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unimplemented!("Load standardized MITRE evaluation data")
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}
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pub fn load_academic_dataset(name: &str) -> Result<Dataset, Error> {
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// Load academic research datasets
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match name {
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"injection_patterns_2024" => self.load_injection_patterns(),
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"evasion_techniques_2024" => self.load_evasion_techniques(),
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"behavioral_anomalies_2024" => self.load_behavioral_anomalies(),
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_ => Err(Error::DatasetNotFound),
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}
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}
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}
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```
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## Publication & Collaboration
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### Research Metrics
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```rust
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pub struct ResearchMetrics {
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detection_rates: HashMap<String, f64>,
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false_positive_rates: HashMap<String, f64>,
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performance_metrics: PerformanceMetrics,
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coverage_analysis: CoverageAnalysis,
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}
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impl ResearchMetrics {
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pub fn generate_publication_data(&self) -> PublicationData {
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PublicationData {
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methodology: self.describe_methodology(),
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experimental_setup: self.describe_setup(),
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results: self.compile_results(),
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statistical_analysis: self.perform_statistical_tests(),
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reproducibility_package: self.create_reproducibility_package(),
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}
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}
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pub fn export_for_publication(&self, format: PublicationFormat) -> String {
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match format {
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PublicationFormat::LaTeX => self.generate_latex_tables(),
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PublicationFormat::CSV => self.export_csv_data(),
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PublicationFormat::JSON => self.export_json_data(),
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}
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}
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}
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```
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### Collaborative Research Platform
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```rust
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pub struct CollaborationPlatform {
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experiment_registry: ExperimentRegistry,
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data_sharing_service: DataSharingService,
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result_verification: ResultVerification,
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}
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impl CollaborationPlatform {
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pub fn register_experiment(&mut self, experiment: Experiment) -> ExperimentId {
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let id = self.experiment_registry.register(experiment);
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self.data_sharing_service.create_workspace(id);
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id
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}
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pub fn share_results(&self, experiment_id: ExperimentId, results: Results) -> Result<(), Error> {
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// Verify result integrity
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self.result_verification.verify(&results)?;
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// Share with registered collaborators
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self.data_sharing_service.publish_results(experiment_id, results)?;
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Ok(())
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}
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}
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```
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## Educational Components
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### Interactive Learning Modules
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```rust
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pub struct EducationalFramework {
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technique_simulator: TechniqueSimulator,
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guided_analysis: GuidedAnalysis,
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assessment_engine: AssessmentEngine,
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}
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impl EducationalFramework {
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pub fn create_learning_scenario(&self, technique: &str) -> LearningScenario {
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LearningScenario {
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description: self.get_technique_description(technique),
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simulated_environment: self.technique_simulator.create_scenario(technique),
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analysis_steps: self.guided_analysis.generate_steps(technique),
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assessment_criteria: self.assessment_engine.get_criteria(technique),
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}
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}
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pub fn evaluate_student_analysis(&self, scenario_id: u64, analysis: StudentAnalysis) -> Assessment {
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let criteria = self.assessment_engine.get_criteria_for_scenario(scenario_id);
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self.assessment_engine.evaluate(analysis, criteria)
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}
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}
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```
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### Technique Documentation Generator
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```rust
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pub struct TechniqueDocGenerator {
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technique_analyzer: TechniqueAnalyzer,
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documentation_builder: DocumentationBuilder,
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example_generator: ExampleGenerator,
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}
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impl TechniqueDocGenerator {
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pub fn generate_technique_guide(&self, technique: &InjectionTechnique) -> TechniqueGuide {
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TechniqueGuide {
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overview: self.technique_analyzer.analyze_technique(technique),
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detection_methods: self.technique_analyzer.get_detection_methods(technique),
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code_examples: self.example_generator.generate_examples(technique),
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countermeasures: self.technique_analyzer.get_countermeasures(technique),
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references: self.technique_analyzer.get_academic_references(technique),
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}
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}
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}
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```
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## Research APIs
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### Data Export API
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```rust
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use serde::{Serialize, Deserialize};
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#[derive(Serialize, Deserialize)]
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pub struct ResearchDataExport {
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pub metadata: ExperimentMetadata,
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pub detection_events: Vec<DetectionEvent>,
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pub performance_metrics: PerformanceMetrics,
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pub statistical_summary: StatisticalSummary,
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}
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impl ResearchDataExport {
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pub fn export_to_format(&self, format: ExportFormat) -> Result<String, Error> {
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match format {
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ExportFormat::JSON => serde_json::to_string_pretty(self).map_err(Error::from),
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ExportFormat::CSV => self.to_csv(),
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ExportFormat::Parquet => self.to_parquet(),
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ExportFormat::HDF5 => self.to_hdf5(),
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}
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}
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}
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```
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### Analysis Pipeline API
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```rust
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pub trait AnalysisPipeline {
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fn process_detection_data(&self, data: &[DetectionEvent]) -> AnalysisResult;
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fn extract_features(&self, events: &[DetectionEvent]) -> FeatureMatrix;
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fn apply_ml_models(&self, features: &FeatureMatrix) -> PredictionResults;
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fn generate_insights(&self, predictions: &PredictionResults) -> Vec<Insight>;
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}
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pub struct StandardAnalysisPipeline {
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feature_extractor: FeatureExtractor,
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ml_models: Vec<Box<dyn MLModel>>,
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insight_generator: InsightGenerator,
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}
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impl AnalysisPipeline for StandardAnalysisPipeline {
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fn process_detection_data(&self, data: &[DetectionEvent]) -> AnalysisResult {
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let features = self.extract_features(data);
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let predictions = self.apply_ml_models(&features);
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let insights = self.generate_insights(&predictions);
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AnalysisResult {
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features,
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predictions,
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insights,
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metadata: self.generate_metadata(),
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}
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}
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}
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```
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## Citation & Attribution
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When using Ghost for research, please cite:
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```bibtex
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@software{ghost_detection_framework,
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title={Ghost: Cross-Platform Process Injection Detection Framework},
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author={Security Research Team},
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year={2024},
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url={https://github.com/pandaadir05/ghost},
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version={1.0.0}
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}
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```
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## Research Ethics & Responsible Disclosure
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### Ethical Guidelines
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1. **Responsible Research**: Use Ghost only for legitimate security research
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2. **Data Privacy**: Anonymize sensitive data in publications
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3. **Disclosure Policy**: Report vulnerabilities through proper channels
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4. **Academic Integrity**: Properly attribute prior work and collaborations
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### Data Handling
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```rust
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pub struct EthicalDataHandler {
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anonymizer: DataAnonymizer,
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consent_manager: ConsentManager,
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audit_logger: AuditLogger,
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}
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impl EthicalDataHandler {
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pub fn handle_research_data(&self, data: RawData) -> Result<AnonymizedData, Error> {
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// Verify consent for data usage
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self.consent_manager.verify_consent(&data)?;
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// Anonymize sensitive information
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let anonymized = self.anonymizer.anonymize(data)?;
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// Log handling for audit trail
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self.audit_logger.log_data_handling(&anonymized);
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Ok(anonymized)
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}
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}
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```
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## Community Contributions
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### Research Collaboration
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- **Open Source Contributions**: Submit improvements via GitHub
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- **Academic Partnerships**: Collaborate on research projects
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- **Industry Collaboration**: Partner with security vendors
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- **Conference Presentations**: Present findings at security conferences
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### Contribution Guidelines
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1. **Code Quality**: Follow Rust best practices
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2. **Documentation**: Comprehensive documentation required
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3. **Testing**: Include unit and integration tests
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4. **Benchmarking**: Provide performance benchmarks
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5. **Research Value**: Demonstrate novel research contributions
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## Future Research Directions
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### Emerging Techniques
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- **AI-Powered Evasion**: Detection of AI-generated injection techniques
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- **Hardware-Assisted Injection**: Analysis of hardware-based attacks
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- **Cloud-Native Threats**: Container and serverless injection techniques
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- **IoT Security**: Process injection in embedded systems
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### Advanced Analytics
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- **Graph Neural Networks**: Process relationship modeling
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- **Temporal Analysis**: Time-series detection modeling
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- **Federated Learning**: Distributed detection model training
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- **Adversarial ML**: Robust ML models against evasion
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For additional research resources, see:
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- [Academic Papers](docs/research/PAPERS.md)
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- [Dataset Specifications](docs/research/DATASETS.md)
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- [Collaboration Guidelines](docs/research/COLLABORATION.md) |