Think back to being in high school and wanting to leave the room during class. Your teacher would give you a hall pass to show anyone monitoring the halls that you had permission to walk around. Your behavior, walking around during the class period, was suspect unless you followed the rule, getting a hall pass.
For security teams, rule-based intrusion detections are the hall monitors that look for behaviors that indicate a problem. Rule-based intrusion detection systems (IDS) use specific rules to identify harmful activities and behaviors, enabling security teams to detect and respond to threats. While they offer various benefits, they also create challenges as security teams need to maintain the rules in an ever-changing threat landscape.
Adding context to rule-based intrusion detections enables security teams to create high-fidelity alerts, reducing false positives and alert fatigue.
What is a rule-based intrusion detection system?
A rule-based intrusion detection system (IDS) identifies malicious activities or unauthorized access by comparing real network traffic against a predefined rule or signature. The rules identify patterns or behaviors associated with security threats, typically based on known attack vectors or published vulnerabilities.
The IDS triggers an alert or takes action when an activity matches rule, enabling security teams to automatically block specific traffic or receive an alert about a potential security incident. Some key features of rule-based IDS include:
- Predefined rules: comparing network traffic to known threat patterns
- Real-time detection: triggering alerts when current network traffic matches the rules
- Response actions: blocking traffic to prevent continued activity or logging the event for later forensic investigation
- Rule management: applying active rules while storing inactive ones
What is the difference between rule-based, signature-based, and anomaly-based IDS?
Three basic types of IDS exist:
- Rule-based: predefined rules to look for policy violations using patterns learned from training data which is valuable for rapidly detecting known threats.
- Signature-based: predefined patterns that match known threats which is valuable for identifying document threats but less effective against new, unknown attacks because it relies on its database.
- Anomaly-based: established baseline for normal network activity that flags significant deviations as suspicious activity or new attacks which may help identify zero-day attacks but can lead to false positives.
What are the benefits of rule-based detections?
Rule-based intrusion detection provides a structured approach to spotting and managing malicious activities, enabling security teams to improve network security. Some of the primary benefits that these rule-based detections provide include:
- Flexibility: giving security teams the ability to enable or disable rules to respond to varying security needs
- Precision: using “if, then” statements to analyze data and identifying potential threats, increasing detection accuracy
- Error management: disabling or ignoring problematic rules when verifying them to reduce errors also reduces false positives and false negatives
- Adaptability: customizing rule sets and regularly updating them for faster responses to newly identified threats and zero days
What are the challenges of using rule-based detections?
Rule-based detections work well for known threats, but they often create challenges when security teams only rely on them to detect incidents. Some primary challenges include:
- Limited capabilities: failure to identify novel or sophisticated attacks, like zero days, that are outside the existing rule set
- Overload of events: high volumes of network traffic triggering excessive event notification that overwhelm security teams and add to alert fatigue
- Errors in rules: duplicated rules or outdated rules leaving threats undetected, increasing data breach risks
- Reliance on rule quality: detection rates and effectiveness dependent on security team’s ability to write clear rules
- Continued maintenance: regular updates and refinements to adapt to new threats and prevent false positive, false negative, and false alarm risks
How does adding context improve rule-based detections?
Adding context to rule-based detections by enriching data can improve accuracy, enabling security teams to more effectively and efficiently protect systems. Context-aware detections enable security teams to improve detections by providing insight into entity behavior and potential impact of the behaviors.
With rule-based detections that use enriched data, security teams can:
- Understand contextual risk in real-time
- Reduce time spent engaging in alert triage
- Filter out low-risk threats and alerts, like testing activity in an environment without sensitive data
For example, adding context to detections may include information like:
- Log message severity
- Event priority based on event definitions or anomaly type
- Assets priority, like business critical applications and databases
- Asset vulnerabilities that impact its risk level
What are the best practices for building high-fidelity rule-based detections?
Building high-fidelity rule-based detections requires careful planning and execution to effectively identify malicious activities. By implementing the following best practices, security teams can improve detection rules in ways that reduce false positives and false negatives.
Centralize and Normalize All Security Data
Capturing the log and event data related to your environment is the basic building block of all detections. To optimize this security data, you need to aggregate it in a central location and normalize the data. The data normalization process converts and standardizes the log formats so that you can compare activities across different technologies.
Identify Use Cases
Identifying use cases helps you determine the types of detections that matter most to your organization’s security. Your use cases define the specific scenarios and threats that you want detections to identify based on your network topology and the typical activities occurring within your environment. For example, some use cases for detections might include:
- Credential stuffing attacks
- Cross-site scripting attacks
- Distributed Denial of Service (DDoS) attack
Build Detections
Building detections involves crafting precise rules that identify network anomalies and potential threats. For example, Sigma rules are a collection of “search scripts” that allows you to identify specific threats by matching log events with potential suspicious activity. Your detections should consider the different logsource components necessary to ensure comprehensive coverage.
Correlate Detections
Correlating detections improves their accuracy and reliability by linking together multiple events. For example, Sigma Correlation rules allow you to build on the basic Sigma rule and define relationships between events. By doing this, you can identify complex threats that might not be detected when looking at isolated events. Effective correlation ensures that suspicious patterns are flagged across different network segments, users, or applications for faster incident detection and investigation.
Add Context to Alerts
Adding context to alerts allows you to better understand the threat and potential impact better. For example, mapping Sigma rules to the MITRE ATT&CK Framework enables you to create tactical alerts for known threats related to your IT environment and engage in proactive threat hunting by leveraging threat intelligence.
Incorporate Risk Scoring
Risk scoring allows you to prioritize the detections so that you can respond to the most critical threats first. Assigning risk scores to the different assets and events enhances decision-making so that you can mitigate a security incident’s impact faster. Some things to consider when creating risk scores include:
- Asset risk, like criticality and known vulnerabilities
- Event risk, like message severity, event priority, and asset priority
Graylog Security: High-fidelity alerts that improve threat detection and incident response
Using Graylog Security, you can rapidly mature your threat detection and incident response capabilities. Graylog Security’s Illuminate bundles include rulesets with content that includes Sigma detections, enabling you to uplevel your monitoring by incorporating threat hunting capabilities and correlations to ATT&CK TTPs.
By leveraging our cloud-native capabilities and out-of-the-box content, you gain immediate value from your logs. Our anomaly detection ML improves over time without manual tuning, adapting rapidly to new data sets, organizational priorities, and custom use cases so that you can automate key user and entity access monitoring.
With our intuitive user interface, you can rapidly investigate alerts. Our lightning-fast search capabilities enable you to search terabytes of data in milliseconds, reducing dwell times and shrinking investigations by hours, days, and weeks.
To learn how Graylog Security can help you implement robust threat detection and response, contact us today.
