299 lines
7.2 KiB
Markdown
299 lines
7.2 KiB
Markdown
# Performance Optimization Guide
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## Overview
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Ghost is designed for high-performance real-time detection with minimal system impact. This guide covers optimization strategies and performance monitoring.
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## Performance Characteristics
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### Detection Engine Performance
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- **Scan Speed**: 500-1000 processes/second on modern hardware
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- **Memory Usage**: 50-100MB base footprint
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- **CPU Impact**: <2% during active monitoring
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- **Latency**: <10ms detection response time
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### Optimization Techniques
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#### 1. Selective Scanning
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```rust
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// Configure detection modules based on threat landscape
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let mut config = DetectionConfig::new();
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config.enable_shellcode_detection(true);
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config.enable_hook_detection(false); // Disable if not needed
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config.enable_anomaly_detection(true);
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```
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#### 2. Batch Processing
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```rust
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// Process multiple items in batches for efficiency
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let processes = enumerate_processes()?;
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let results: Vec<DetectionResult> = processes
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.chunks(10)
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.flat_map(|chunk| engine.analyze_batch(chunk))
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.collect();
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```
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#### 3. Memory Pool Management
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```rust
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// Pre-allocate memory pools to reduce allocations
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pub struct MemoryPool {
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process_buffers: Vec<ProcessBuffer>,
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detection_results: Vec<DetectionResult>,
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}
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```
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## Performance Monitoring
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### Built-in Metrics
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```rust
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use ghost_core::metrics::PerformanceMonitor;
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let monitor = PerformanceMonitor::new();
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monitor.start_collection();
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// Detection operations...
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let stats = monitor.get_statistics();
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println!("Avg scan time: {:.2}ms", stats.avg_scan_time);
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println!("Memory usage: {}MB", stats.memory_usage_mb);
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```
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### Custom Benchmarks
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```bash
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# Run comprehensive benchmarks
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cargo bench
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# Profile specific operations
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cargo bench -- shellcode_detection
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cargo bench -- process_enumeration
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```
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## Tuning Guidelines
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### For High-Volume Environments
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1. **Increase batch sizes**: Process 20-50 items per batch
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2. **Reduce scan frequency**: 2-5 second intervals
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3. **Enable result caching**: Cache stable process states
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4. **Use filtered scanning**: Skip known-good processes
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### For Low-Latency Requirements
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1. **Decrease batch sizes**: Process 1-5 items per batch
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2. **Increase scan frequency**: Sub-second intervals
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3. **Disable heavy detections**: Skip complex ML analysis
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4. **Use memory-mapped scanning**: Direct memory access
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### Memory Optimization
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```rust
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// Configure memory limits
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let config = DetectionConfig {
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max_memory_usage_mb: 200,
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enable_result_compression: true,
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cache_size_limit: 1000,
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..Default::default()
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};
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```
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## Platform-Specific Optimizations
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### Windows
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- Use `SetProcessWorkingSetSize` to limit memory
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- Enable `SE_INCREASE_QUOTA_NAME` privilege for better access
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- Leverage Windows Performance Toolkit (WPT) for profiling
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### Linux
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- Use `cgroups` for resource isolation
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- Enable `CAP_SYS_PTRACE` for enhanced process access
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- Leverage `perf` for detailed performance analysis
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## Troubleshooting Performance Issues
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### High CPU Usage
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1. Check scan frequency settings
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2. Verify filter effectiveness
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3. Profile detection module performance
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4. Consider disabling expensive detections
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### High Memory Usage
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1. Monitor result cache sizes
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2. Check for memory leaks in custom modules
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3. Verify proper cleanup of process handles
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4. Consider reducing batch sizes
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### Slow Detection Response
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1. Profile individual detection modules
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2. Check system resource availability
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3. Verify network latency (if applicable)
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4. Consider async processing optimization
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## Benchmarking Results
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### Baseline Performance (Intel i7-9700K, 32GB RAM)
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```
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Process Enumeration: 2.3ms (avg)
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Shellcode Detection: 0.8ms per process
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Hook Detection: 1.2ms per process
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Anomaly Analysis: 3.5ms per process
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Full Scan (100 proc): 847ms total
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```
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### Memory Usage
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```
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Base Engine: 45MB
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+ Shellcode Patterns: +12MB
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+ ML Models: +23MB
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+ Result Cache: +15MB (1000 entries)
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Total Runtime: 95MB typical
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```
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## Advanced Optimizations
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### SIMD Acceleration
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```rust
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// Enable SIMD for pattern matching
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#[cfg(target_feature = "avx2")]
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use std::arch::x86_64::*;
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// Vectorized shellcode scanning
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unsafe fn simd_pattern_search(data: &[u8], pattern: &[u8]) -> bool {
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// AVX2 accelerated pattern matching
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}
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```
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### Multi-threading
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```rust
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use rayon::prelude::*;
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// Parallel process analysis
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let results: Vec<DetectionResult> = processes
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.par_iter()
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.map(|process| engine.analyze_process(process))
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.collect();
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```
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### Caching Strategies
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```rust
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use lru::LruCache;
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pub struct DetectionCache {
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process_hashes: LruCache<u32, u64>,
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shellcode_results: LruCache<u64, bool>,
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anomaly_profiles: LruCache<u32, ProcessProfile>,
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}
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```
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## Monitoring Dashboard Integration
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### Prometheus Metrics
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```rust
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use prometheus::{Counter, Histogram, Gauge};
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lazy_static! {
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static ref SCAN_DURATION: Histogram = Histogram::new(
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"ghost_scan_duration_seconds",
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"Time spent scanning processes"
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).unwrap();
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static ref DETECTIONS_TOTAL: Counter = Counter::new(
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"ghost_detections_total",
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"Total number of detections"
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).unwrap();
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}
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```
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### Real-time Monitoring
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```rust
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// WebSocket-based real-time metrics
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pub struct MetricsServer {
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connections: Vec<WebSocket>,
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metrics_collector: PerformanceMonitor,
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}
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impl MetricsServer {
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pub async fn broadcast_metrics(&self) {
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let metrics = self.metrics_collector.get_real_time_stats();
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let json = serde_json::to_string(&metrics).unwrap();
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for connection in &self.connections {
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connection.send(json.clone()).await.ok();
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}
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}
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}
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```
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## Best Practices
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1. **Profile First**: Always benchmark before optimizing
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2. **Measure Impact**: Quantify optimization effectiveness
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3. **Monitor Production**: Continuous performance monitoring
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4. **Gradual Tuning**: Make incremental adjustments
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5. **Document Changes**: Track optimization history
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## Performance Testing Framework
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```rust
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#[cfg(test)]
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mod performance_tests {
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use super::*;
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use std::time::Instant;
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#[test]
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fn benchmark_full_system_scan() {
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let engine = DetectionEngine::new().unwrap();
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let start = Instant::now();
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let results = engine.scan_all_processes().unwrap();
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let duration = start.elapsed();
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assert!(duration.as_millis() < 5000, "Scan took too long");
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assert!(results.len() > 0, "No processes detected");
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}
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#[test]
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fn memory_usage_benchmark() {
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let initial = get_memory_usage();
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let engine = DetectionEngine::new().unwrap();
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// Perform operations
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for _ in 0..1000 {
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engine.analyze_dummy_process();
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}
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let final_usage = get_memory_usage();
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let growth = final_usage - initial;
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assert!(growth < 50_000_000, "Memory usage grew too much: {}MB",
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growth / 1_000_000);
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}
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}
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```
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## Conclusion
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Ghost's performance can be fine-tuned for various deployment scenarios. Regular monitoring and benchmarking ensure optimal operation while maintaining security effectiveness.
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For additional performance support, see:
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- [Profiling Guide](PROFILING.md)
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- [Deployment Strategies](DEPLOYMENT.md)
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- [Scaling Recommendations](SCALING.md) |