Using Synthetic Monitoring to Detect Service Degradation During DDoS Attacks

 

In the modern digital landscape, maintaining consistent service availability is a top priority for any organization. Distributed Denial of Service (DDoS) attacks threaten this availability, overwhelming servers, networks, or applications and causing downtime or degraded performance. While traditional monitoring methods track real user activity, synthetic monitoring offers a proactive approach to detect issues—even before users notice them.

This blog explores how synthetic monitoring works, why it’s effective against DDoS-related degradation, implementation strategies, and best practices to keep services resilient.

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What Is Synthetic Monitoring?

Synthetic monitoring, sometimes called active monitoring, involves using scripted tests to simulate user interactions with your services. These synthetic checks run from multiple locations across the network, generating traffic that mimics real users, such as:

• Loading web pages or API endpoints

• Performing login or transaction workflows

• Fetching content from static or dynamic sources

• Checking server response times, error rates, and availability

Unlike passive monitoring, which relies on actual user traffic, synthetic monitoring gives organizations a controlled, continuous view of system performance—allowing issues to be detected even if real user traffic hasn’t yet revealed them.

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Why Synthetic Monitoring Matters During DDoS Attacks

During a DDoS attack, servers and networks can degrade gradually or unevenly. Some impacts may be subtle at first:

• Slight increases in latency on certain endpoints

• Sporadic connection failures or timeouts

• High error rates on resource-intensive pages or APIs

Synthetic monitoring is particularly valuable because it:

1. Detects degradation before users notice: By generating traffic consistently, synthetic checks can reveal increasing response times or error rates early.

2. Provides baseline comparisons: Continuous synthetic tests help you detect anomalies relative to normal performance.

3. Measures geographic and network impacts: Running checks from multiple locations reveals localized or ISP-specific degradation caused by targeted attacks.

4. Supports automated alerting: Teams can receive early warnings of potential DDoS impacts, even before customer complaints spike.

In essence, synthetic monitoring acts as a canary in the system, flagging performance issues before they escalate.

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How Synthetic Monitoring Detects DDoS-Related Degradation

DDoS attacks can cause a variety of performance issues that synthetic monitoring can detect:

1. Latency Increases

• What happens: Attacks saturate bandwidth or overload server resources, slowing request processing.

• Synthetic detection: Scripts measuring response times for critical pages or API calls will detect increases beyond normal thresholds.

For example, if a login endpoint normally responds in 200 milliseconds, but synthetic checks report 1–2 second delays, it may indicate resource strain due to malicious traffic.

2. Failed Requests or Errors

• What happens: Under attack, servers may drop connections or return 5xx errors.

• Synthetic detection: Monitoring scripts can track the percentage of failed requests. A sudden spike signals potential DDoS activity, even if total user traffic is low.

3. Connection Drops and Timeouts

• What happens: Connection table exhaustion or network congestion leads to dropped connections.

• Synthetic detection: Scripts repeatedly attempting TCP or HTTPS connections will reveal failures or timeouts, allowing teams to correlate with attack activity.

4. Geographic or Network-Specific Impacts

• What happens: Some DDoS attacks are localized to specific regions, targeting regional servers or ISPs.

• Synthetic detection: Running checks from multiple locations identifies geographic patterns in latency, error rates, or availability.

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Implementing Synthetic Monitoring for DDoS Detection

To leverage synthetic monitoring effectively, organizations should follow a structured approach:

Step 1: Identify Critical Workflows

Start by identifying the most business-critical endpoints that, if degraded, would impact revenue or customer experience. Examples include:

• Login or authentication flows

• Payment processing

• Checkout or transaction endpoints

• Key API endpoints for partner integrations

Focus your synthetic scripts on these flows to ensure early detection of DDoS-related degradation.

Step 2: Choose Diverse Monitoring Locations

DDoS attacks can affect specific network paths or geographic regions. To detect these impacts:

• Deploy synthetic checks from multiple global locations or cloud providers.

• Include different ISPs or network segments where possible.

• Monitor for discrepancies in response times, error rates, or throughput between locations.

Diversity ensures you catch localized or route-specific performance degradation.

Step 3: Define Baselines and Thresholds

Before relying on synthetic monitoring, establish baseline performance metrics for each endpoint:

• Average response times

• Normal error rates

• Typical connection success rates

Set thresholds that, when exceeded, trigger alerts. For example:

• Latency exceeds baseline + 50%

• Error rate exceeds 2% of requests

• Connection failures exceed normal variance

Baselines are critical because they allow teams to distinguish DDoS-related anomalies from legitimate traffic spikes.

Step 4: Automate Continuous Checks

• Run synthetic scripts at consistent intervals, from minutes to hours, depending on criticality.

• Automate logging, alerting, and reporting.

• Ensure alerts are actionable and routed to operational teams immediately.

Automation ensures early detection even outside normal business hours.

Step 5: Correlate with Other Monitoring Signals

Synthetic monitoring is most effective when integrated with other monitoring data, such as:

• Real user monitoring (RUM) metrics

• Network traffic patterns (bps, pps, connection rates)

• Firewall and intrusion detection logs

• Backend server health metrics

Correlating synthetic results with other indicators helps teams distinguish DDoS attacks from legitimate traffic spikes or system issues.

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Best Practices for Effective Synthetic Monitoring

1. Use realistic scripts: Simulate actual user behavior rather than simple ping checks to detect application-layer degradation.

2. Test multiple endpoints: Monitor not only the homepage but also APIs, login pages, checkout flows, and partner integrations.

3. Implement graduated alerting: Avoid alert fatigue by categorizing anomalies into warning and critical tiers.

4. Monitor trends, not just spikes: Subtle, progressive degradation can be more dangerous than sudden failures.

5. Integrate with incident response: Link synthetic monitoring alerts directly to mitigation playbooks for faster response.

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Advantages Over Reactive Monitoring

Synthetic monitoring provides unique advantages compared to reactive methods:

• Proactive detection: Identify problems before real users report them.

• Controlled testing: Repeated, predictable checks provide reliable comparison data.

• Insight into attack vectors: Detect unusual latency patterns, geographic effects, and endpoint-specific issues.

• Reduced business impact: Early warning allows teams to activate mitigation before downtime affects customers or revenue.

By contrast, reactive monitoring relies on user reports or passive logs, which often lag behind the onset of a DDoS attack, increasing operational and reputational risk.

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Integrating Synthetic Monitoring into a DDoS Strategy

Synthetic monitoring should be part of a layered DDoS defense approach:

• At the perimeter: Combine synthetic checks with network-level monitoring (bps, pps, flow anomalies).

• Application layer: Use scripts to validate API and web application performance.

• Mitigation validation: Synthetic monitoring can also confirm that mitigation rules are functioning correctly and not blocking legitimate traffic.

• Reporting and executive visibility: Summarize synthetic monitoring results in dashboards for technical and business teams to track trends and incidents.

By integrating synthetic monitoring across layers, organizations gain a comprehensive view of both attack activity and system resilience.

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Challenges and Considerations

While synthetic monitoring is powerful, teams should be aware of challenges:

1. False positives: Network congestion or maintenance activities may trigger alerts; baselines and thresholds must be carefully tuned.

2. Coverage gaps: Scripts cannot perfectly emulate all user behaviors, so gaps may exist in detection.

3. Resource consumption: Overly aggressive synthetic checks can themselves add load; balance frequency and impact.

4. Integration complexity: Correlating synthetic results with logs, RUM data, and network metrics requires thoughtful architecture.

Careful design, tuning, and integration minimize these challenges and maximize the value of synthetic monitoring.

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Case for Multi-Layered Observation

Synthetic monitoring is most effective when combined with other observability tools. For instance:

• Real User Monitoring (RUM) captures actual user experiences.

• Network monitoring detects volumetric DDoS patterns.

• Log analysis and anomaly detection uncover subtle protocol-level attacks.

Together, these layers provide a 360-degree view of both attacks and their operational impact, enabling faster, more informed responses.

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Conclusion

DDoS attacks are increasingly sophisticated, and their impact can range from subtle service degradation to full outages. Synthetic monitoring offers organizations a proactive tool to detect performance issues before real users experience disruptions. By simulating user behavior across multiple endpoints and locations, establishing baselines, and integrating with alerts and mitigation workflows, businesses can detect anomalies early, respond quickly, and maintain trust.

Ultimately, synthetic monitoring does not replace other DDoS defenses—it enhances them by providing early visibility, validating mitigation rules, and helping teams understand service performance under stress. In an era where uptime is directly tied to revenue, customer trust, and brand reputation, this proactive approach is not just a technical advantage—it’s a business imperative.