Clean up documentation and rewrite README
Removed unnecessary markdown files that were either outdated or redundant (BUILD.md, PROJECT_SUMMARY.md, TODO.md, RESEARCH_FRAMEWORK.md). Most of this info is either completed or captured elsewhere. Rewrote README in a more natural, conversational style. Less formal, more straightforward about what the tool does and how to use it. Kept the technical details but made it more accessible.
This commit is contained in:
Adir Shitrit
@@ -1,458 +0,0 @@
|
||||
# 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)
|
||||
Reference in New Issue
Block a user