Continuous Activities in Detection Engineering

Detection engineering (DE) is a critical component of cybersecurity operations, dedicated to designing, implementing, and maintaining systems that detect malicious activities within an organization's environment. This blog will delve into the continuous activities involved in a DE program, including monitoring, maintenance, metrics, and comprehensive validation. Each of these activities is essential for ensuring that detection systems remain effective and relevant in the ever-evolving cybersecurity landscape.

Monitoring

Monitoring is the ongoing process of assessing detections that have been implemented in production environments. Despite extensive testing during the DE lifecycle, real-world deployments often yield unexpected results. Monitoring captures these results, providing insights that guide the refinement of detection rules.

For example, consider a detection rule designed to identify the use of the netcat (nc) command, which is often associated with malicious activities. During testing, you verify that the rule triggers appropriately when nc is executed and does not produce false positives with a sample dataset. However, once deployed, you observe false positives triggered by legitimate commands containing the string "sync." This information indicates a need to refine the detection rule to reduce false positives while maintaining its effectiveness.

Another example involves monitoring file access patterns. Suppose a detection rule is in place to identify unauthorized access to sensitive files. Monitoring this rule might reveal false positives triggered by legitimate automated backup processes. In this case, you would refine the rule to distinguish between malicious access and legitimate automated activities.

Maintenance

The cybersecurity landscape is dynamic, with threat actors constantly evolving their tactics, techniques, and procedures (TTPs). Maintenance involves updating and refining detection rules to keep pace with these changes. Some detections may remain relevant over time, while others require periodic updates.

For instance, a detection rule might be designed to identify a specific malware campaign that delivers payloads via ZIP archives. If threat actors shift to using TAR archives, the original detection rule becomes obsolete. Maintenance requires updating the rule to include TAR archives, ensuring continued effectiveness against the evolving threat.

Similarly, consider a detection rule focused on identifying phishing emails by scanning for specific keywords and attachments. If attackers begin using more sophisticated techniques, such as embedding malicious links within PDFs, the rule must be updated to detect these new tactics.

Metrics

Metrics provide a statistical analysis of the performance of detection rules, offering insights into their effectiveness and areas for improvement. Various types of metrics can be utilized to enhance the detection program.

1. Trigger Frequency: Metrics indicating how often a detection rule is triggered help identify rules that may be causing excessive false positives. For example, a rule that triggers significantly more often than others might indicate a high rate of false positives, necessitating further refinement.
2. Incident Outcomes: Tracking the outcomes of incidents triggered by detection rules provides valuable insights. For example, if a high percentage of alerts generated by a specific rule are determined to be false positives upon manual review, the rule needs adjustment.
3. Performance Metrics: Ensuring that detection rules are efficient and do not negatively impact production systems is crucial. Performance metrics assess the resource consumption of detection rules, helping to identify and optimize resource-intensive detections.

For example, a detection rule that scans all incoming network traffic for specific patterns may consume significant resources. Performance metrics can help identify such issues, leading to the optimization of the rule to minimize its impact on system performance.

Comprehensive Validation

Validation involves using simulated attacks or other techniques to test whether detection systems accurately identify malicious activities. Validation can be conducted during the testing phase of the detection lifecycle and as an ongoing activity to perform a broader gap analysis.

For instance, validation might involve simulating attacks based on the MITRE ATT&CK framework to assess detection coverage. If the validation reveals that the detection system fails to identify certain techniques, such as T1053 (Scheduled Task/Job), this gap becomes a requirement for developing new detections.

Another example of validation is conducting red team exercises, where a team of ethical hackers simulates real-world attacks on the organization. The results of these exercises provide valuable feedback on the effectiveness of existing detection rules and highlight areas for improvement.

Enhancing Detection Engineering Practices

Beyond the core activities of monitoring, maintenance, metrics, and validation, several additional practices can enhance a DE program:

1. Threat Intelligence Integration: Incorporating threat intelligence feeds into the detection program ensures that rules are updated with the latest information on emerging threats. This proactive approach helps maintain the relevance and effectiveness of detection systems.
2. Automation: Automating routine tasks, such as log analysis and rule updates, improves efficiency and allows DE teams to focus on more complex tasks. Automation also reduces the risk of human error in the detection process.
3. Collaboration with Incident Response Teams: Close collaboration between DE and incident response teams ensures that detections are aligned with real-world threats and incident response capabilities. This collaboration enhances the overall effectiveness of the security program.
4. Continuous Training and Skill Development: Providing ongoing training and skill development opportunities for DE team members ensures they stay up-to-date with the latest tools, techniques, and best practices in detection engineering.

Conclusion

Detection engineering is a dynamic and essential component of modern cybersecurity operations. By focusing on continuous activities such as monitoring, maintenance, metrics, and comprehensive validation, organizations can ensure their detection systems remain effective and adaptive to evolving threats. Incorporating additional practices like threat intelligence integration, automation, collaboration, and continuous training further strengthens the DE program, enhancing its ability to protect against sophisticated cyber threats.

Detection engineering is not a one-time effort but an ongoing process that requires constant vigilance and adaptation. By embracing these practices and continuously refining detection systems, organizations can stay ahead of threat actors and maintain robust security postures.