GuardDuty Alerts: Differentiating Between True Positives and False Positives

Distinguishing between true and false positives is crucial for maintaining robust defenses while minimizing unnecessary disruptions. A true positive occurs when a security alert accurately identifies a legitimate threat, prompting appropriate action to mitigate the risk. Conversely, a false positive happens when an alert mistakenly signals a threat that does not exist, potentially leading to wasted resources and real threats slipping through the cracks.

Understanding and refining this differentiation process is essential for enhancing the efficiency and effectiveness of cybersecurity measures, ensuring that real threats are addressed promptly while reducing the noise of incorrect alerts.

This blog will guide you through differentiating these alerts in Amazon GuardDuty, a threat detection service designed to protect AWS accounts and workloads to help security teams focus on genuine threats.

Understanding GuardDuty Alerts

Amazon GuardDuty continuously monitors for malicious activity and unauthorized behavior by analyzing events from AWS CloudTrail, VPC Flow Logs, and DNS logs. Amazon Web Services (AWS) maintains and continuously improves its detection algorithms. The primary detection categories include:

• Reconnaissance: This category includes activity suggesting reconnaissance by a malicious actor. This might include, for instance, unusual API activity, intra-VPC port scanning, unusual patterns of failed login requests, or unblocked port probing from a known bad IP.
• Instance Compromise: This involves activity indicating an instance compromise, such as cryptocurrency mining, malware using domain generation algorithms (DGA), or outbound denial of service activity. It could also include an unusually high volume of network traffic, unusual network protocols, outbound instance communication with a known malicious IP, temporary Amazon EC2 credentials used by an external IP address, and data exfiltration using DNS.
• Account Compromise: Common patterns that could be a sign account compromise is happening include API calls from atypical geolocation or anonymizing proxy, attempts to disable Amazon CloudTrail logging, abnormal instance or infrastructure launches, API calls from known malicious IP addresses, and others.
• Bucket Compromise: This category includes activity signaling a bucket compromise, such as suspicious data access patterns indicating credential misuse, unusual S3 API activity from a remote host, and more.

False Vs. True Positives

A true positive alert happens when GuardDuty correctly identifies a genuine threat. This could indicate unauthorized access, data exfiltration, or malicious activity that needs instant action to limit potential fallout.

A false positive alert, on the other hand, occurs when GuardDuty flags an activity as suspicious even though it is benign. These alerts can clutter the monitoring system, leading to wasted resources and potential alert fatigue among security teams.

When GuardDuty detects a potential threat, it generates an alert that is categorized based on severity. GuardDuty categorizes findings into three severity levels: low, medium, and high. While high-severity alerts often indicate critical threats and should be prioritized, low—and medium-severity alerts require further analysis.

• High-Severity Alerts typically pinpoint actions like unauthorized access attempts, data breaches, or malware activity. They are likely to be true positives and should be addressed immediately.
• Low and Medium-Severity Alerts often indicate less critical issues or harmless activities. Security teams need to analyze these alerts in conjunction with other contextual information to determine whether they are valid.

Separating the Wheat From the Chaff

Understanding the distinction between true and false positives is essential for enhancing the accuracy and efficiency of your cybersecurity measures. Several tools help with this:

Contextual Analysis

One of the most effective ways to differentiate between true and false positives is through contextual analysis. By examining the context in which an alert is generated, security teams can determine the likelihood of it being a true positive.

Analyzing user behavior patterns, for instance, can provide insights into whether an alert is likely to be a true positive. For example, if an alert is generated for an IP address that has never been used before, it might warrant further investigation. In addition, reviewing historical data and past alerts can help identify patterns that distinguish true positives from false positives. If similar alerts have previously been marked as false positives, the current alert might also be benign.

Integrating Threat Intelligence

Integrating threat intelligence feeds with GuardDuty also enhances the accuracy of threat detection. Threat intelligence provides up-to-date information about known malicious IP addresses, domains, and other indicators of compromise (IOCs).

When it comes to known IOCs, an alert involving an IP address or domain known to be associated with malicious activity is more likely to be a true positive. Similarly, using reputation scores from threat intelligence feeds can help prioritize alerts. IP addresses with a high reputation for malicious activity should be investigated promptly.

Large Language Models

Large Language Models (LLMs) can dramatically enhance the investigation of alerts by automating the analysis of security logs and identifying patterns indicative of potential threats. By leveraging natural language processing capabilities, LLMs can parse and contextualize alerts, cross-reference them with known threat intelligence, and provide actionable insights.

This allows security teams to quickly understand the nature of the alert, prioritize responses, and mitigate risks efficiently, ultimately improving the overall security posture of the AWS environment.

Human Expertise and Collaboration

Despite advances in automation and machine learning, human expertise remains invaluable in differentiating between true and false positives. Security analysts should collaborate and share insights to improve threat detection accuracy.

Having multiple analysts review alerts can reduce the likelihood of overlooking true positives. Also, developing and following incident response playbooks can standardize handling alerts, ensuring that true positives are addressed effectively.

Best Practices for Managing GuardDuty Alerts

To optimize the management of GuardDuty alerts and minimize the impact of false positives, consider the following best practices:

• Regularly Update Detection Rules: Keep GuardDuty detection rules up-to-date to reflect the latest threat intelligence and evolving threat landscape.
• Implement Multi-Factor Authentication (MFA): MFA adds an extra layer of security, reducing the likelihood of false positives from unauthorized access attempts.
• Conduct Regular Audits: Periodically review and audit GuardDuty findings to ensure that detection rules and response strategies are effective.
• Customize Alert Thresholds: Tailor alert thresholds based on the organization’s risk tolerance and security posture.

Maintaining Effective Security

Differentiating between true positive and false positive GuardDuty alerts is essential for maintaining an effective security posture in your AWS environment. By leveraging contextual analysis, integrating threat intelligence, utilizing machine learning, and relying on human expertise, security teams can focus on genuine threats, reducing the risk of alert fatigue and enhancing overall security.

Implementing best practices for managing alerts will ensure that your organization remains vigilant and responsive to emerging threats.

About the author:

Kirsten Doyle has been in the technology journalism and editing space for nearly 24 years, during which time she has developed a great love for all aspects of technology, as well as words themselves. Her experience spans B2B tech, with a lot of focus on cybersecurity, cloud, enterprise, digital transformation, and data centre. Her specialties are in news, thought leadership, features, white papers, and PR writing, and she is an experienced editor for both print and online publications. She is also a regular writer at Bora.