ghost/docs/PERFORMANCE_GUIDE.md

Performance Optimization Guide

Overview

Ghost is designed for high-performance real-time detection with minimal system impact. This guide covers optimization strategies and performance monitoring.

Performance Characteristics

Detection Engine Performance

• Scan Speed: 500-1000 processes/second on modern hardware
• Memory Usage: 50-100MB base footprint
• CPU Impact: <2% during active monitoring
• Latency: <10ms detection response time

Optimization Techniques

1. Selective Scanning

// Configure detection modules based on threat landscape
let mut config = DetectionConfig::new();
config.enable_shellcode_detection(true);
config.enable_hook_detection(false); // Disable if not needed
config.enable_anomaly_detection(true);

2. Batch Processing

// Process multiple items in batches for efficiency
let processes = enumerate_processes()?;
let results: Vec<DetectionResult> = processes
    .chunks(10)
    .flat_map(|chunk| engine.analyze_batch(chunk))
    .collect();

3. Memory Pool Management

// Pre-allocate memory pools to reduce allocations
pub struct MemoryPool {
    process_buffers: Vec<ProcessBuffer>,
    detection_results: Vec<DetectionResult>,
}

Performance Monitoring

Built-in Metrics

use ghost_core::metrics::PerformanceMonitor;

let monitor = PerformanceMonitor::new();
monitor.start_collection();

// Detection operations...

let stats = monitor.get_statistics();
println!("Avg scan time: {:.2}ms", stats.avg_scan_time);
println!("Memory usage: {}MB", stats.memory_usage_mb);

Custom Benchmarks

# Run comprehensive benchmarks
cargo bench

# Profile specific operations
cargo bench -- shellcode_detection
cargo bench -- process_enumeration

Tuning Guidelines

For High-Volume Environments

1. Increase batch sizes: Process 20-50 items per batch
2. Reduce scan frequency: 2-5 second intervals
3. Enable result caching: Cache stable process states
4. Use filtered scanning: Skip known-good processes

For Low-Latency Requirements

1. Decrease batch sizes: Process 1-5 items per batch
2. Increase scan frequency: Sub-second intervals
3. Disable heavy detections: Skip complex ML analysis
4. Use memory-mapped scanning: Direct memory access

Memory Optimization

// Configure memory limits
let config = DetectionConfig {
    max_memory_usage_mb: 200,
    enable_result_compression: true,
    cache_size_limit: 1000,
    ..Default::default()
};

Platform-Specific Optimizations

Windows

• Use SetProcessWorkingSetSize to limit memory
• Enable SE_INCREASE_QUOTA_NAME privilege for better access
• Leverage Windows Performance Toolkit (WPT) for profiling

Linux

• Use cgroups for resource isolation
• Enable CAP_SYS_PTRACE for enhanced process access
• Leverage perf for detailed performance analysis

Troubleshooting Performance Issues

High CPU Usage

1. Check scan frequency settings
2. Verify filter effectiveness
3. Profile detection module performance
4. Consider disabling expensive detections

High Memory Usage

1. Monitor result cache sizes
2. Check for memory leaks in custom modules
3. Verify proper cleanup of process handles
4. Consider reducing batch sizes

Slow Detection Response

1. Profile individual detection modules
2. Check system resource availability
3. Verify network latency (if applicable)
4. Consider async processing optimization

Benchmarking Results

Baseline Performance (Intel i7-9700K, 32GB RAM)

Process Enumeration:     2.3ms (avg)
Shellcode Detection:     0.8ms per process
Hook Detection:          1.2ms per process
Anomaly Analysis:        3.5ms per process
Full Scan (100 proc):    847ms total

Memory Usage

Base Engine:            45MB
+ Shellcode Patterns:   +12MB
+ ML Models:           +23MB
+ Result Cache:        +15MB (1000 entries)
Total Runtime:         95MB typical

Advanced Optimizations

SIMD Acceleration

// Enable SIMD for pattern matching
#[cfg(target_feature = "avx2")]
use std::arch::x86_64::*;

// Vectorized shellcode scanning
unsafe fn simd_pattern_search(data: &[u8], pattern: &[u8]) -> bool {
    // AVX2 accelerated pattern matching
}

Multi-threading

use rayon::prelude::*;

// Parallel process analysis
let results: Vec<DetectionResult> = processes
    .par_iter()
    .map(|process| engine.analyze_process(process))
    .collect();

Caching Strategies

use lru::LruCache;

pub struct DetectionCache {
    process_hashes: LruCache<u32, u64>,
    shellcode_results: LruCache<u64, bool>,
    anomaly_profiles: LruCache<u32, ProcessProfile>,
}

Monitoring Dashboard Integration

Prometheus Metrics

use prometheus::{Counter, Histogram, Gauge};

lazy_static! {
    static ref SCAN_DURATION: Histogram = Histogram::new(
        "ghost_scan_duration_seconds",
        "Time spent scanning processes"
    ).unwrap();
    
    static ref DETECTIONS_TOTAL: Counter = Counter::new(
        "ghost_detections_total",
        "Total number of detections"
    ).unwrap();
}

Real-time Monitoring

// WebSocket-based real-time metrics
pub struct MetricsServer {
    connections: Vec<WebSocket>,
    metrics_collector: PerformanceMonitor,
}

impl MetricsServer {
    pub async fn broadcast_metrics(&self) {
        let metrics = self.metrics_collector.get_real_time_stats();
        let json = serde_json::to_string(&metrics).unwrap();
        
        for connection in &self.connections {
            connection.send(json.clone()).await.ok();
        }
    }
}

Best Practices

1. Profile First: Always benchmark before optimizing
2. Measure Impact: Quantify optimization effectiveness
3. Monitor Production: Continuous performance monitoring
4. Gradual Tuning: Make incremental adjustments
5. Document Changes: Track optimization history

Performance Testing Framework

#[cfg(test)]
mod performance_tests {
    use super::*;
    use std::time::Instant;
    
    #[test]
    fn benchmark_full_system_scan() {
        let engine = DetectionEngine::new().unwrap();
        let start = Instant::now();
        
        let results = engine.scan_all_processes().unwrap();
        let duration = start.elapsed();
        
        assert!(duration.as_millis() < 5000, "Scan took too long");
        assert!(results.len() > 0, "No processes detected");
    }
    
    #[test]
    fn memory_usage_benchmark() {
        let initial = get_memory_usage();
        let engine = DetectionEngine::new().unwrap();
        
        // Perform operations
        for _ in 0..1000 {
            engine.analyze_dummy_process();
        }
        
        let final_usage = get_memory_usage();
        let growth = final_usage - initial;
        
        assert!(growth < 50_000_000, "Memory usage grew too much: {}MB", 
                growth / 1_000_000);
    }
}

Conclusion

Ghost's performance can be fine-tuned for various deployment scenarios. Regular monitoring and benchmarking ensure optimal operation while maintaining security effectiveness.

For additional performance support, see:

• Profiling Guide
• Deployment Strategies
• Scaling Recommendations