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Enterprise Tier Features

This document describes the Enterprise tier features of DaemonEye, including kernel monitoring, network event monitoring, and federated security center architecture.

Overview

The Enterprise tier extends DaemonEye with advanced monitoring capabilities and enterprise-grade features:

  • Kernel Monitoring Layer: eBPF, ETW, and EndpointSecurity integration
  • Network Event Monitor: Real-time network traffic analysis
  • Federated Security Center: Multi-site security center architecture
  • STIX/TAXII Integration: Threat intelligence sharing
  • Advanced Analytics: Machine learning and behavioral analysis

Kernel Monitoring Layer

Linux eBPF Integration

DaemonEye uses eBPF (Extended Berkeley Packet Filter) for low-level system monitoring:

use aya::{
    programs::{Xdp, XdpFlags},
    Bpf,
};

pub struct EBPFMonitor {
    bpf: Bpf,
    program: Xdp,
}

impl EBPFMonitor {
    pub async fn new() -> Result<Self, MonitorError> {
        let bpf = Bpf::load_file("monitor.o")?;
        let program: &mut Xdp = bpf.program_mut("monitor").unwrap().try_into()?;
        program.load()?;
        program.attach("eth0", XdpFlags::default())?;

        Ok(Self { bpf, program })
    }
}

Windows ETW Integration

Windows Event Tracing for Windows (ETW) provides comprehensive system monitoring:

use windows::{core::PCWSTR, Win32::System::Diagnostics::Etw::*};

pub struct ETWMonitor {
    session_handle: TRACEHANDLE,
    trace_properties: EVENT_TRACE_PROPERTIES,
}

impl ETWMonitor {
    pub fn new() -> Result<Self, MonitorError> {
        let mut trace_properties = EVENT_TRACE_PROPERTIES::default();
        trace_properties.Wnode.BufferSize = std::mem::size_of::<EVENT_TRACE_PROPERTIES>() as u32;
        trace_properties.Wnode.Guid = GUID::from("12345678-1234-1234-1234-123456789012");
        trace_properties.Wnode.ClientContext = 1;
        trace_properties.Wnode.Flags = WNODE_FLAG_TRACED_GUID;
        trace_properties.LogFileMode = EVENT_TRACE_REAL_TIME_MODE;
        trace_properties.LoggerNameOffset = std::mem::size_of::<EVENT_TRACE_PROPERTIES>() as u32;
        trace_properties.LogFileNameOffset = 0;
        trace_properties.BufferSize = 64;
        trace_properties.MinimumBuffers = 2;
        trace_properties.MaximumBuffers = 2;
        trace_properties.FlushTimer = 1;
        trace_properties.EnableFlags = EVENT_TRACE_FLAG_PROCESS;

        let session_handle = StartTraceW(
            &mut 0,
            PCWSTR::from_raw("DaemonEye\0".as_ptr() as *const u16),
            &mut trace_properties,
        )?;

        Ok(Self {
            session_handle,
            trace_properties,
        })
    }
}

macOS EndpointSecurity Integration

macOS EndpointSecurity framework provides real-time security event monitoring:

use endpoint_sec::{
    Client, ClientBuilder, Event, EventType, Process,
};

pub struct EndpointSecurityMonitor {
    client: Client,
}

impl EndpointSecurityMonitor {
    pub async fn new() -> Result<Self, MonitorError> {
        let client = ClientBuilder::new()
            .name("com.daemoneye.monitor")
            .build()
            .await?;

        Ok(Self { client })
    }

    pub async fn start_monitoring(&self) -> Result<(), MonitorError> {
        let mut stream = self.client.subscribe(&[
            EventType::NotifyExec,
            EventType::NotifyFork,
            EventType::NotifyExit,
            EventType::NotifySignal,
        ]).await?;

        while let Some(event) = stream.next().await {
            self.handle_event(event).await?;
        }

        Ok(())
    }
}

Network Event Monitor

The Network Event Monitor provides real-time network traffic analysis:

use pcap::{Capture, Device};

pub struct NetworkMonitor {
    capture: Capture<Device>,
}

impl NetworkMonitor {
    pub fn new(interface: &str) -> Result<Self, MonitorError> {
        let device = Device::lookup()?
            .find(|d| d.name == interface)
            .ok_or(MonitorError::DeviceNotFound)?;

        let capture = Capture::from_device(device)?
            .promisc(true)
            .buffer_size(65536)
            .open()?;

        Ok(Self { capture })
    }

    pub async fn start_capture(&mut self) -> Result<(), MonitorError> {
        while let Ok(packet) = self.capture.next() {
            self.process_packet(packet).await?;
        }
        Ok(())
    }
}

Federated Security Center Architecture

The Federated Security Center enables multi-site security center deployment:

pub struct FederatedSecurityCenter {
    primary_center: SecurityCenter,
    regional_centers: Vec<RegionalSecurityCenter>,
    federation_config: FederationConfig,
}

pub struct FederationConfig {
    pub primary_endpoint: String,
    pub regional_endpoints: Vec<String>,
    pub sync_interval: Duration,
    pub conflict_resolution: ConflictResolution,
}

pub enum ConflictResolution {
    PrimaryWins,
    TimestampWins,
    ManualReview,
}

STIX/TAXII Integration

DaemonEye integrates with STIX/TAXII for threat intelligence sharing:

use stix::{
    objects::{Indicator, Malware, ThreatActor},
    taxii::client::TaxiiClient,
};

pub struct STIXTAXIIIntegration {
    client: TaxiiClient,
    collection_id: String,
}

impl STIXTAXIIIntegration {
    pub async fn new(endpoint: &str, collection_id: &str) -> Result<Self, IntegrationError> {
        let client = TaxiiClient::new(endpoint)?;
        Ok(Self {
            client,
            collection_id: collection_id.to_string(),
        })
    }

    pub async fn fetch_indicators(&self) -> Result<Vec<Indicator>, IntegrationError> {
        let objects = self.client
            .get_objects(&self.collection_id, "indicator")
            .await?;

        let indicators: Vec<Indicator> = objects
            .into_iter()
            .filter_map(|obj| obj.try_into().ok())
            .collect();

        Ok(indicators)
    }
}

Advanced Analytics

Enterprise tier includes machine learning and behavioral analysis:

pub struct BehavioralAnalyzer {
    models: Vec<BehavioralModel>,
    anomaly_threshold: f64,
}

pub struct BehavioralModel {
    name: String,
    features: Vec<String>,
    model: Box<dyn Model>,
}

impl BehavioralAnalyzer {
    pub fn analyze_process(&self, process: &ProcessInfo) -> Result<AnomalyScore, AnalysisError> {
        let features = self.extract_features(process);
        let mut scores = Vec::new();

        for model in &self.models {
            let score = model.model.predict(&features)?;
            scores.push(score);
        }

        let anomaly_score = self.aggregate_scores(scores);
        Ok(anomaly_score)
    }
}

Deployment Considerations

Resource Requirements

Enterprise tier features require additional resources:

  • CPU: 2+ cores for kernel monitoring
  • Memory: 4+ GB for network monitoring and analytics
  • Storage: 100+ GB for event storage and analytics data
  • Network: High-bandwidth for network monitoring

Security Considerations

  • Kernel monitoring requires elevated privileges
  • Network monitoring may capture sensitive data
  • Federated architecture requires secure communication
  • STIX/TAXII integration requires secure authentication

Performance Impact

  • Kernel monitoring: 2-5% CPU overhead
  • Network monitoring: 5-10% CPU overhead
  • Analytics processing: 10-20% CPU overhead
  • Storage requirements: 10x increase for event data

Configuration

Enterprise tier configuration extends the base configuration:

enterprise:
  kernel_monitoring:
    enable_ebpf: true
    ebpf_program_path: /etc/daemoneye/ebpf/monitor.o
    enable_etw: true
    etw_session_name: DaemonEye
    enable_endpoint_security: true
    es_client_name: com.daemoneye.monitor

  network_monitoring:
    enable_packet_capture: true
    capture_interface: eth0
    capture_filter: tcp port 80 or tcp port 443
    max_packet_size: 1500
    buffer_size_mb: 100

  federation:
    enable_federation: true
    primary_endpoint: https://primary.daemoneye.com
    regional_endpoints:
      - https://region1.daemoneye.com
      - https://region2.daemoneye.com
    sync_interval: 300
    conflict_resolution: primary_wins

  stix_taxii:
    enable_integration: true
    taxii_endpoint: https://taxii.example.com
    collection_id: daemoneye-indicators
    sync_interval: 3600

  analytics:
    enable_behavioral_analysis: true
    anomaly_threshold: 0.8
    model_update_interval: 86400
    enable_machine_learning: true

Troubleshooting

Common Issues

Kernel Monitoring Failures:

  • Check kernel version compatibility
  • Verify eBPF/ETW/EndpointSecurity support
  • Check privilege requirements
  • Review kernel logs for errors

Network Monitoring Issues:

  • Verify network interface permissions
  • Check packet capture filters
  • Monitor buffer usage
  • Review network performance impact

Federation Sync Issues:

  • Check network connectivity
  • Verify authentication credentials
  • Review sync logs
  • Check conflict resolution settings

Analytics Performance:

  • Monitor CPU and memory usage
  • Check model update frequency
  • Review feature extraction performance
  • Optimize anomaly detection thresholds

This document provides comprehensive information about Enterprise tier features. For additional help, consult the troubleshooting section or contact support.