Production traffic can often be unpredictable, and distinguishing genuine user interactions from mere noise becomes a pivotal step in comprehensively grasping the types of requests and workflows occurring within your deployment.
One important concept to explore in this context is shadow traffic, which plays a significant role in analytics and cybersecurity but is often misunderstood or rarely discussed. Shadow traffic consists of the unseen, unaccounted for, or wrongly attributed web traffic that can skew metrics, lead to faulty decision-making, and potentially indicate more profound technical or security problems within your deployment.
In this blog post, we’ll unpack shadow traffic—its features, origins, and effects. You’ll learn how to detect, assess, control, and minimize it in your systems by article’s end.
What is Shadow Traffic
Simply put, shadow traffic refers to web traffic that standard analytics tools fail to accurately capture, analyze, or record. This traffic can arise for a variety of reasons, but ultimately they reflect a dark data source which is ill-defined and hard to surface – hence the term “shadow”.
Unlike regular traffic, which is straightforward to track and measure, reflecting actual data flows, shadow traffic operates in a blind spot difficult to detect with standard systems like Google Analytics. This traffic could include requests and data transfers from bots, misconfigured endpoints, untracked redirects, errored loops, or even basic errors in tagging and tracking implementations.
This type of traffic often bypasses conventional detection mechanisms, resulting in discrepancies between actual activity and the reported metrics collected around that activity. While certain types of shadow traffic, like legitimate users accessing content through unconventional or outdated methods, may be harmless, other forms could represent significant security risks or suggest vulnerabilities in system implementation.
Shadow traffic carries substantial long-term consequences. Beyond the immediate concern of poorly sourced and tracked data, it can introduce significant performance problems, including repeated queries, unchecked attack vectors, and reduced system observability.
Characteristics of Shadow Traffic
Shadow traffic may arise for multiple reasons, but they typically have some well-defined attributes regardless of source of origination.
Shadow traffic can arise from multiple reasons but generally shares some well-defined attributes, regardless of its origin.
Firstly, shadow traffic is typically untrackable or misattributed. Since shadow traffic arises from sources that are unclear or ill-defined, they may be fully misattributed within your analytics systems. In some cases, the source may not even be something you can track in your current stack, such as requests coming from deprecated endpoints which are nonetheless accessible, representing a blindspot which is pointedly not tracked or ingested into the analytics engine.
This lack of source identity is a huge problem. With unclear origins, such as those from unidentified bots, scrapers, or outdated endpoints, it can become exceedingly difficult to track origination. This is especially true in complex or distributed environments, which already make tracking difficult.
Shadow traffic also tends to exhibit sporadic patterns and broadly intermittent behavior. Since these shadow traffic requests are often coming from deprecated feature sets, duplicated systems, or rogue bots, they tend not to operate under best practices. In some cases, shadow traffic can even arise from attack vectors from malicious actors which are only partially controlled. On the whole, then, these shadow traffic sources tend to not be reliable or consistent, resulting in sporadic data both in terms of total flow and scale of problematic traffic. This can make it hard to identify actual behavior classes to be tracked and mitigated.
This is also true of shadow traffic arising from technical abnormalities. Not all shadow traffic is malicious or intentional – in some cases, traffic can become shadow traffic due to misconfigured DNS settings, server errors, cache overflow, redacts that fail to propagate tracking codes, and much more. These issues can compound significantly, making two technical abnormalities much more impactful than the sum of their parts might suggest.
Causes of Shadow Traffic
Shadow traffic can rise for a variety of reasons. While these reasons can show up in different environments, they can often happen at the same time in parallel, so it’s worth deeply investigating cases of shadow traffic to figure out true causes.
Technical Misconfigurations
Technical misconfigurations are a leading cause of shadow traffic, as they arise from common and simple misattributed legitimate traffic sources. For example, valid traffic can become shadow traffic if tracking codes are missing or incorrect. This type of traffic isn’t malicious, but it’s mislabeled due to tracking errors.
This issue may also arise from redirects that remove tracking parameters or direct traffic to the wrong but still functional endpoints. Such actions validate the traffic but complicate tracking the data back to its original source. This problem is quite common, yet it can be easily addressed by checking the configurations of your data sources.
Bot Traffic
When data flows into the system from automated sources, it can be hard to figure out the source of the traffic through typical means. Automated traffic exhibits distinct behaviors compared to manual traffic, particularly from the backend’s viewpoint. Consequently, when analyzing this traffic, it can be mistakenly categorized due to its unique characteristics.
This is particularly true given that the automated bot traffic exhibits distinct behaviors compared to manual traffic, particularly from the backend’s viewpoint. Consequently, when analyzing this traffic, it can be mistakenly categorized due to its unique characteristics.
Proxy and VPN Usage
When users access services via a proxy or a VPN, it complicates tracking because it obscures the true origin of the traffic, challenging the ability to trace specific user journeys. For instance, a user might be in North America (NA) but using a proxy that presents an European Union (EU) IP address to access content. If they attempt to retrieve an NA-cached item while presenting an EU region code, the resulting traffic doesn’t align cleanly with either geographic category. This discrepancy makes it difficult to accurately assess or trace the user’s activity, as it blends characteristics of both regions and avoids straightforward classification.
More seriously, some proxies can lock down the traffic itself, stripping out additional material that can help identify user information, regions, etc. In this case, the systems outright evade standard tracking metrics and systems, making it all but impossible to track with basic toolsets.
Cross-Domain Tracking Issues
Cross-domain tracking can be challenging when dealing with shadow traffic, since traffic analytics usually expect a single source or a clear path of referral. This assumption can complicate the accurate analysis of traffic that spans multiple domains. If cross-domain tracking configurations are overly complex or misconfigured, they can easily miss other sources or flows, resulting in untracked or minimally tracked sessions.
Custom Applications, Ad Blockers, and Privacy Tools
Custom applications, ad blockers, and privacy tools can create the most complex shadow traffic issues. These tools often block analytics scripts and systems, which can create blind spots that make it hard to track internal sourcing and routing. Traffic originating from custom applications can also be problematic when they use non-standard headers or payloads that can bypass or confuse tracking mechanisms.
Impact of Shadow Traffic
The consequences of shadow traffic can range from minor inconveniences to significant business implications.
Shadow traffic primarily leads to inaccurate analytics, which can skew metrics right from the start. This distortion results in faulty insights, misguided decision-making, and unreliable metrics for comparison across various deployments.
The effects extend beyond direct tracking. Misguided traffic assumptions and learnings can lead to missed opportunities and operational inefficiencies, such as improper server load management. Additionally, it can result in further traffic mismanagement and even the creation of new sources of shadow traffic.
Shadow traffic also introduces a lot of security risks. Shadow traffic can arise from malicious activity such as bot attacks and data scraping, which significantly reduces observability. This decrease makes it challenging to identify harmful patterns and traffic sources that could threaten internal resources or compromise.
Finally, this can also have major challenges in monetization. Untracked traffic can reduce the perceived value of premium offerings and advertising spaces, ultimately leading to lower value conversion.
Identifying and Analyzing Shadow Traffic
Pinpointing shadow traffic requires a combination of tools, techniques, and vigilance.
Using a traffic replay tool like Speedscale can be a significant aid in this process. These tools allow you to collect and capture real traffic, which can then be replayed in a local environment for better identification. By capturing real traffic over time, you can replay it to uncover potential shadow traffic sources and test strategies for managing it. This data collection aids in refining release patterns for multiple versions, helping to develop more effective policies for managing shadow traffic.
Another valuable approach in this process is deploying enhanced analytics configurations. Shadow traffic can arise from failing to properly ingest traffic into analytics systems. Thus, adopting more advanced tagging and tracking methods, like custom trackers or server-side analytics, can minimize these blind spots.
Employing sophisticated heuristics and analytics systems can enhance the detection of patterns and anomalies that standard approaches might overlook, leading to more effective traffic categorization. Utilizing solutions like Cloudflare, Imperva, or Akamai helps to minimize non-human traffic and control bot activity through your own authentication and authorization mechanisms, facilitating clearer and more direct tagging.
As a common best practice, you should regularly review DNS and server configurations to ensure proper routing and tracking, and commit to periodic audits of traffic sources, session durations, and user behaviors to get an overall sense of activities happening on your services.
How to Manage Shadow Traffic
Effective management of shadow traffic involves proactive measures and continuous monitoring. You can help manage shadow traffic by implementing solutions such as server-side tracking, which shift tracking responsibilities from the client-side to the server-side. You can also deploy firewall and CDN solutions to help filter unwanted traffic before it reaches your servers, further reducing overall load.
As a general approach, you should enhance your data validation methods by implementing rules and checks to verify the authenticity and integrity of incoming traffic data. Dealing with shadow traffic effectively involves minimizing false traffic and properly sorting valid traffic. The earlier you implement these measures in the data traffic flow, the better.
Preventing and Reducing Shadow Traffic
To minimize shadow traffic, organizations should focus on prevention strategies. Regularly monitoring traffic patterns and updating tracking configurations helps to swiftly adapt to changes. This flexibility enables timely updates that can prevent shadow traffic from becoming a major issue.
Further, educate your teams about shadow traffic and the best ways to mitigate its impact. Development should focus on clarity and preemptively addressing potential tracking issues early in the lifecycle, as “an ounce of prevention is worth a pound of cure”. Learning to develop effective and clear tracking for new code as well as continual improvement of existing code is a significant step forward.
Conclusion
Shadow traffic poses a unique challenge for businesses striving to maintain accurate and actionable web analytics. By understanding its causes, characteristics, and impacts, organizations can take proactive steps to identify, analyze, and manage shadow traffic effectively.
As the digital ecosystem continues to evolve, staying ahead of these hidden traffic patterns will remain a critical aspect of strategic online management. Speedscale can help you capture real traffic, allowing for deep inspection and observability to guide development and iteration in the right direction. Start your Speedscale free trial today and get set up in just a few minutes!