National Cyber Warfare Foundation (NCWF)

Detection engineering is a field of cybersecurity focused on designing, implementing, and maintaining detection methods to identify potential security threats within an organization’s environment. It goes beyond simply setting up alerts and involves a strategic approach to understanding threat behaviors, identifying IOCs (indicators of compromise), and developing detection logic that accurately identifies malicious activity without […]

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By developing precise detection logic and continuously refining detection mechanisms, detection engineers help reduce the TTD (time to detect) and TTR (time to respond) to incidents. This proactive approach ensures that security teams can quickly act on alerts and prevent potential breaches.

Key Concepts in Detection Engineering

Detection Logic: This refers to the specific rules, queries, and analytics developed to identify malicious activities based on observed behaviors and IOCs. Effective detection logic is tailored to an organization’s environment and considers various data sources like network traffic, endpoint logs, and other services.

False Positives and False Negatives: A false positive occurs when a benign action is incorrectly flagged as malicious, while a false negative is when a malicious action goes undetected. Detection engineering aims to minimize these errors to ensure accurate and actionable alerts.

Behavioral Analysis: Unlike static detection methods that rely solely on IOCs, behavioral analysis involves monitoring for abnormal patterns of behavior that may indicate a threat, such as unusual login times or data exfiltration attempts. This approach helps in detecting novel and evolving threats but requires a predefined baseline of “normal” behavior.

Threat Hunting: Threat hunting is a proactive approach to detecting threats by searching through networks and systems to identify suspicious activity. Detection engineers may assist with creating hunting hypotheses and work with threat hunting teams to validate detection logic to develop improved analytics, uncovering hidden threats that automated systems might miss.

Tools of the Trade

Detection engineers use a variety of tools and standards to find and respond to threats. Certain tools are optimized for different functions, including file analysis, network analysis, and log analysis.

File-Based: These tools are designed to identify malware or suspicious activity based on file patterns, signatures, or behaviors.
1. YARA is a tool used by detection engineers to identify and classify malware based on patterns in files or processes. YARA rules consist of strings and conditions that define what constitutes a match, making them a powerful resource for identifying threats.

Network-Based: These tools monitor network traffic to detect suspicious activities or intrusion attempts.
1. Suricata: An open source network intrusion detection and prevention system (IDS/IPS) that analyzes network traffic in real-time. It uses signature-based rules to identify threats, such as detecting attacks based on known network behavior or patterns. It can also perform network session decoding and analysis, acting as a sort of network flight recorder, generating audit records for all activity on the network.
2. Snort: Snort is another popular open source IDS/IPS tool that uses rules to inspect network traffic and identify threats based on known attack signatures.
3. Zeek: An open source network analytic and audit engine with the capability to identify, decode and generate audit records for numerous relevant network protocols. Zeek provides a complete domain specific language for network protocol analytics, and also supplies a number of frameworks for the development of plugins as well as interfaces for applying threat intelligence to the task of network analysis.

Log-Based: These tools analyze system or application logs to detect unusual patterns that may indicate security incidents and are often used in concert.
1. Sigma: An open standard for writing detection rules that can be applied to log data. It provides a generic rule format, which can then be translated to different SIEMs.
2. Wazuh: An open source security monitoring platform that integrates log analysis, intrusion detection, file integrity monitoring and vulnerability management. It can monitor logs from various sources and create alerts based on preconfigured or custom rules.

Common Problems

Scale and Resources

While detection engineers might like to secure organizations against all types of threats, engineers are constrained by both resources and manpower. No organization can protect against every threat, and so they must tailor detection engineering efforts to their specific environment. To do this, organizations need to refine their approach to focus on the attack vectors that they are most vulnerable to.

Context is the key word here—an organization’s industries, products, IT systems, and even geopolitical risks need to be considered. Cyber threat intelligence, a closely related field, can help define these contexts. One common approach is to focus on an organization’s industry vertical. For example, a marketing company based in the United States will face different threats than an energy company in Asia.

Reducing False Positives

An inherent difficulty faced by detection engineers is in defining detection methods that catch threats without overwhelming analysts with false positives. Below is a helpful chart that illustrates this concept:

The potential error rate can be reduced through careful development of detection rules and rigorous testing of these rules in complex environments. Rules should be vetted against known threats as well as edge cases and benign examples.

For example, a detection rule that looks for malicious macros in a Microsoft Word document should detect malicious macros consistently, while not generating false positives on normal documents.

Naming Conventions

Once a threat is identified and detection rules are defined and tested, they are then integrated into the tools that SOC teams use to monitor networks and are often shared with other security teams. Naming and describing these rules accurately can be challenging—especially when the indicators they detect are complex or obscure. Standardization is important here, and organizations can identify standards that help with organization and labelling to make management of large detection libraries easier.

Concluding Thoughts

Detection engineering is a cornerstone of modern cybersecurity, providing the framework for identifying and responding to threats effectively. By giving SOC teams the information they need to detect and respond to emerging threats, detection engineering will remain an essential discipline for maintaining robust security defenses.

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