> ## Documentation Index > Fetch the complete documentation index at: https://mintlify.com/iLotuus/Enterprise-SOC-Architecture/llms.txt > Use this file to discover all available pages before exploring further. # Threat Detection > Comprehensive threat detection across all layers of the SOC architecture ## Overview The Enterprise SOC Architecture implements a multi-layered threat detection strategy that provides comprehensive visibility across network, endpoint, and infrastructure layers. This approach ensures that threats are identified quickly and accurately, enabling rapid response to security incidents. ## Detection Layers IDS/IPS systems (Snort & Suricata) monitor all network traffic from endpoints, detecting suspicious patterns and known attack signatures in real-time. Wazuh agents provide EDR capabilities, monitoring file integrity, system calls, registry changes, and process execution on endpoints. Zabbix and Prometheus track system health, performance anomalies, and unusual resource consumption patterns that may indicate compromise. Correlation engines in Wazuh analyze patterns across multiple data sources to identify advanced threats and lateral movement. ## Threat Categories ### Network-Based Threats * Port scanning and reconnaissance * Brute force authentication attacks * Exploitation attempts against known vulnerabilities * Command and control (C2) communications * Malware download attempts * Data exfiltration patterns * DDoS attack traffic * Protocol abuse and tunneling ### Endpoint-Based Threats * Known malware signatures * Suspicious file modifications * Encryption behaviors * Unauthorized process execution * Privilege escalation attempts * Unauthorized access to sensitive data * Suspicious user behavior patterns * Policy violations ### Infrastructure Threats * Unusual resource consumption * Unexpected service failures * Configuration changes * Unauthorized software installation * Compromised dependencies * Unauthorized system updates * Third-party access violations * Shadow IT detection ## Detection Rule Development Detection rules should be continuously refined based on threat intelligence, incident analysis, and environmental changes. ### Rule Creation Process 1. **Threat Research**: Analyze current threat landscape and emerging attack techniques 2. **Signature Development**: Create detection signatures for Snort/Suricata 3. **Behavioral Rules**: Develop correlation rules in Wazuh for complex attack patterns 4. **Testing**: Validate rules in a test environment to ensure effectiveness 5. **Deployment**: Roll out rules to production with appropriate tuning 6. **Monitoring**: Track rule performance and detection accuracy ### Rule Types | Rule Type | Platform | Use Case | Example | | ----------------- | -------------- | --------------------- | ----------------------------------------- | | Signature-based | Snort/Suricata | Known attack patterns | CVE exploits, malware signatures | | Anomaly-based | Wazuh | Behavioral deviations | Unusual login times, abnormal data access | | Correlation-based | Wazuh | Multi-stage attacks | APT detection, lateral movement | | Threshold-based | Prometheus | Resource abuse | Failed login attempts, traffic spikes | Overly broad detection rules can generate excessive false positives. Always balance detection sensitivity with operational efficiency. ## False Positive Management Effective false positive management is critical to maintaining analyst efficiency and preventing alert fatigue. ### Identification Strategies Establish baselines for normal network traffic, user behavior, and system operations. Deviations from baseline that are legitimate should be documented and tuned. * Monitor for recurring false positives * Analyze legitimate business processes that trigger alerts * Document approved exceptions Continuously refine detection rules based on false positive analysis: * Add whitelists for known-good traffic * Adjust detection thresholds * Implement time-based exceptions * Use context-aware rules Implement a feedback mechanism where analysts can mark false positives: * Track false positive rates per rule * Regular review sessions with security team * Automated tuning recommendations * Documentation of tuning decisions Create suppression rules for known false positive scenarios: * Maintenance windows * Approved security tools * Testing environments * Known benign applications ### Best Practices Maintain a false positive rate below 10% to ensure analyst effectiveness. Higher rates lead to alert fatigue and missed threats. * **Regular Review**: Schedule weekly reviews of high-volume alerts * **Prioritization**: Focus tuning efforts on high-frequency, low-severity alerts * **Documentation**: Maintain a knowledge base of tuning decisions * **Metrics**: Track false positive rates as a key performance indicator ## Threat Intelligence Integration Threat intelligence enhances detection capabilities by providing context about emerging threats, attack techniques, and known malicious indicators. ### Intelligence Sources Paid threat intelligence services providing high-quality, curated indicators Community-driven feeds like MISP, AlienVault OTX, and abuse.ch Indicators derived from past incidents and internal research ### Integration Points 1. **IDS/IPS Rule Updates**: Automatically update Snort/Suricata rules with new signatures 2. **IP Reputation**: Feed malicious IP addresses to firewall and detection systems 3. **Domain Blacklists**: Block known malicious domains at DNS and proxy level 4. **File Hashes**: Match known malware hashes in endpoint detection 5. **TTPs**: Update behavioral detection rules based on adversary tactics Threat intelligence should be consumed from multiple sources and correlated to improve accuracy and reduce false positives from low-quality feeds. ### Implementation Workflow ``` 1. Ingest → Threat intelligence feeds collected from multiple sources 2. Normalize → Convert to standardized format (STIX/TAXII) 3. Validate → Score and verify intelligence quality 4. Enrich → Add context and relevance scoring 5. Distribute → Push to detection systems (Snort, Suricata, Wazuh) 6. Monitor → Track detection efficacy and update feeds ``` ### Intelligence-Driven Detection Use threat intelligence to prioritize detection and response efforts based on current threat actor activity and campaigns. * **Campaign Tracking**: Monitor for indicators related to active threat campaigns * **Actor Attribution**: Identify threat actors based on TTPs and infrastructure * **Vulnerability Prioritization**: Focus on vulnerabilities actively exploited in the wild * **Proactive Hunting**: Use intelligence to guide threat hunting activities ## Detection Performance Metrics Track these key metrics to measure detection effectiveness: | Metric | Target | Description | | -------------------------- | ---------------- | ----------------------------------------------- | | Mean Time to Detect (MTTD) | \< 1 hour | Average time from compromise to detection | | False Positive Rate | \< 10% | Percentage of alerts that are false positives | | Coverage | > 90% | Percentage of MITRE ATT\&CK techniques covered | | Detection Accuracy | > 95% | Percentage of true threats correctly identified | | Alert Volume | Baseline +/- 20% | Daily alert volume trends | Regularly test detection capabilities using attack simulation tools and red team exercises to validate coverage and identify gaps. ## Continuous Improvement Threat detection is an iterative process that requires continuous refinement: 1. **Incident Review**: Analyze detection performance after each incident 2. **Gap Analysis**: Identify attacks that were not detected 3. **Rule Enhancement**: Develop new rules to address detection gaps 4. **Testing**: Validate improvements in test environment 5. **Deployment**: Implement enhanced detection capabilities 6. **Monitoring**: Track effectiveness of new detections Never deploy untested detection rules directly to production. Always validate in a test environment first.