Security Monitoring

Comprehensive security monitoring and threat detection for enterprise environments.

Overview

The security monitoring system provides real-time threat detection, incident response, and compliance monitoring capabilities for Anya Enterprise deployments.

Features

Real-time Threat Detection

  • Intrusion Detection: Network and host-based intrusion detection
  • Anomaly Detection: ML-powered behavioral analysis
  • Threat Intelligence: Integration with threat intelligence feeds
  • Automated Response: Configurable automated incident response

Security Analytics

Log Analysis

use serde::{Deserialize, Serialize};

#[derive(Debug, Serialize, Deserialize)]
pub struct SecurityEvent {
    pub timestamp: DateTime<Utc>,
    pub event_type: EventType,
    pub severity: Severity,
    pub source_ip: IpAddr,
    pub target: String,
    pub details: serde_json::Value,
}

pub struct SecurityAnalyzer {
    rules: Vec<DetectionRule>,
    ml_models: Vec<AnomalyModel>,
}

impl SecurityAnalyzer {
    pub fn analyze_event(&self, event: &SecurityEvent) -> Option<Alert> {
        // Rule-based detection
        for rule in &self.rules {
            if rule.matches(event) {
                return Some(Alert::from_rule(rule, event));
            }
        }

        // ML-based anomaly detection
        for model in &self.ml_models {
            if let Some(anomaly) = model.detect_anomaly(event) {
                return Some(Alert::from_anomaly(anomaly, event));
            }
        }

        None
    }
}

Behavioral Analysis

  • User Behavior Analytics: Detect unusual user activities
  • Entity Behavior Analytics: Monitor system and service behavior
  • Network Traffic Analysis: Deep packet inspection and flow analysis
  • File Integrity Monitoring: Detect unauthorized file changes

Compliance Monitoring

Regulatory Compliance

  • SOC 2: Security monitoring for SOC 2 compliance
  • ISO 27001: Information security management monitoring
  • PCI DSS: Payment card industry compliance monitoring
  • GDPR: Data protection regulation compliance

Audit Trail

interface AuditEvent {
  id: string;
  timestamp: Date;
  user_id: string;
  action: string;
  resource: string;
  outcome: 'success' | 'failure';
  details: Record<string, any>;
  source_ip: string;
  user_agent: string;
}

class AuditLogger {
  async logEvent(event: AuditEvent): Promise<void> {
    // Store in secure audit database
    await this.auditDb.insert(event);

    // Real-time compliance checking
    await this.complianceChecker.validate(event);

    // Alert on suspicious activities
    if (this.isSuspicious(event)) {
      await this.alertManager.send(event);
    }
  }
}

Security Dashboard

Monitoring Interface

import React from 'react';
import { SecurityMetrics, ThreatMap, AlertsPanel } from './components';

export const SecurityDashboard: React.FC = () => {
  return (
    <div className="security-dashboard">
      <div className="metrics-row">
        <SecurityMetrics />
        <ThreatMap />
      </div>
      <div className="alerts-row">
        <AlertsPanel />
      </div>
    </div>
  );
};

Key Metrics

  • Security Score: Overall security posture rating
  • Active Threats: Current threat count and severity
  • Incident Response Time: Average time to respond to incidents
  • Compliance Status: Real-time compliance dashboard

Incident Response

Automated Response

incident_response:
  rules:
    - name: "Brute Force Detection"
      trigger: "failed_login_attempts > 10"
      actions:
        - block_ip
        - notify_admin
        - create_incident

    - name: "Malware Detection"
      trigger: "malware_signature_match"
      actions:
        - quarantine_file
        - isolate_system
        - emergency_alert

Response Workflows

  1. Detection: Automated threat detection
  2. Analysis: Security analyst review
  3. Containment: Isolate affected systems
  4. Eradication: Remove threats
  5. Recovery: Restore normal operations
  6. Lessons Learned: Post-incident analysis

Integration

SIEM Integration

from anya_security import SIEMConnector

class EnterpriseMonitoring:
    def __init__(self):
        self.siem = SIEMConnector()
        self.ml_detector = AnomalyDetector()

    async def process_logs(self, logs):
        for log in logs:
            # Normalize log format
            normalized = self.normalize_log(log)

            # Send to SIEM
            await self.siem.send_event(normalized)

            # ML analysis
            if anomaly := self.ml_detector.detect(normalized):
                await self.handle_anomaly(anomaly)

API Endpoints

# Get security metrics
GET /api/v1/security/metrics

# Get active threats
GET /api/v1/security/threats

# Get incident reports
GET /api/v1/security/incidents

# Create security alert
POST /api/v1/security/alerts

Configuration

Monitoring Configuration

[security_monitoring]
enabled = true
log_level = "info"
retention_days = 365

[threat_detection]
enabled = true
ml_models = ["behavioral", "network", "file"]
sensitivity = "medium"

[compliance]
frameworks = ["soc2", "iso27001", "pci_dss"]
automated_reporting = true
report_schedule = "daily"

[incident_response]
auto_response = true
escalation_timeout = 300  # seconds
notification_channels = ["email", "slack", "pagerduty"]

Machine Learning Models

Anomaly Detection

import torch
import torch.nn as nn

class SecurityAnomalyDetector(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers):
        super().__init__()
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.classifier = nn.Linear(hidden_size, 1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        lstm_out, _ = self.lstm(x)
        output = self.classifier(lstm_out[:, -1, :])
        return self.sigmoid(output)

Threat Intelligence

  • IOC Matching: Indicators of Compromise detection
  • Reputation Scoring: IP and domain reputation analysis
  • Attack Pattern Recognition: Known attack pattern detection
  • Zero-day Detection: Unknown threat identification

Performance and Scaling

High-Performance Processing

use tokio::sync::mpsc;
use futures::stream::StreamExt;

pub async fn process_security_events(
    mut event_stream: impl Stream<Item = SecurityEvent> + Unpin
) {
    let (tx, mut rx) = mpsc::channel(1000);

    // Parallel event processing
    tokio::spawn(async move {
        while let Some(event) = event_stream.next().await {
            if let Err(_) = tx.send(event).await {
                break;
            }
        }
    });

    // Process events in parallel
    while let Some(event) = rx.recv().await {
        tokio::spawn(analyze_security_event(event));
    }
}

Monitoring Metrics

  • Events per Second: Throughput monitoring
  • Processing Latency: Event processing time
  • Memory Usage: Resource utilization
  • Storage Growth: Log storage requirements

See Also

This documentation is part of the Anya Enterprise Security suite.