❓ What is a Active users : definition, examples of use.

What is Active users?

In digital advertising, “Active Users” refers to a security concept, not a performance metric. It involves actively analyzing a user’s real-time behavior and technical attributes (like IP, device, and mouse movements) to distinguish between legitimate human engagement and automated or fraudulent activity, such as bots and click farms.

How Active users Works

Ad Click → [Data Collection Point] → +--------------------------+
                                     │ Active User Analysis Engine│
                                     +--------------------------+
                                                 │
                                                 ↓
                                     ┌──────────────────────────┐
                                     │   Behavioral & Technical │
                                     │       Rule Matching      │
                                     └──────────────────────────┘
                                                 │
                                (Is behavior human-like? Is data valid?)
                                                 │
                                                 ↓
                                 ┌───────────────┴───────────────┐
                                 │                               │
                           [VALID TRAFFIC]                  [INVALID TRAFFIC]
                                 │                               │
                                 ↓                               ↓
                          Allow to Landing Page              Block & Log

Data Collection

When a user clicks on an ad, a traffic security system immediately captures a wide range of data points before the user is redirected to the final landing page. This collection is the foundation of active user analysis. Key data sources include network information like the IP address and ISP, device details such as user agent, operating system, and screen resolution, and session data like the timestamp of the click and the referring site. This information provides the raw material needed to build a comprehensive profile of the visitor for fraud assessment.

Real-Time Analysis

The collected data is instantly fed into an analysis engine where it is processed against a series of rules and models. This engine performs active validation by cross-referencing information and looking for anomalies. For example, it might check if the IP address belongs to a known data center (a common source of bot traffic), if the user agent string is malformed or inconsistent with the device’s supposed operating system, or if the click pattern from that user or IP is unnaturally frequent.

Behavioral Heuristics

A core component of active user analysis is the application of behavioral heuristics to determine if the interaction is human-like. This involves examining patterns that are difficult for simple bots to replicate. Techniques include analyzing mouse movement patterns, scrolling behavior, and the time between page load and the click event. An immediate click with no mouse movement might be flagged as suspicious, whereas natural, slightly irregular mouse paths and variable timing are more characteristic of genuine users.

Decision and Enforcement

Based on the combined analysis of technical data and behavioral heuristics, the system makes a real-time decision. If the user is deemed legitimate, their request is seamlessly passed through to the advertiser’s landing page. If the activity is flagged as fraudulent or invalid, the system takes a protective action. This usually involves blocking the request, redirecting it to a honeypot, or simply logging the fraudulent event and excluding the source from future ad targeting without alerting the fraudster. This entire process happens in milliseconds to avoid disrupting the user experience for legitimate visitors.

🧠 Core Detection Logic

Example 1: Inconsistent User-Agent Filtering

This logic checks for discrepancies between a user’s declared device (via the user-agent string) and their browser’s actual capabilities, a common sign of a spoofing bot. It’s used at the entry point of traffic analysis to quickly filter out unsophisticated bots that fail to mimic a real device environment convincingly.

FUNCTION checkUserAgent(request):
  userAgent = request.headers['User-Agent']
  platform = request.headers['Sec-CH-UA-Platform'] // Client Hint

  // Rule: A user agent claiming to be macOS should not have a Windows platform hint.
  IF "Macintosh" IN userAgent AND "Windows" IN platform:
    RETURN "FRAUDULENT: User-Agent and Platform Mismatch"
  
  // Rule: A mobile user agent should not have a desktop screen resolution.
  resolution = request.screenResolution
  IF "Android" IN userAgent AND resolution.width > 1200:
    RETURN "FRAUDULENT: Mobile User-Agent with Desktop Resolution"

  RETURN "VALID"

Example 2: Rapid-Click IP Throttling

This logic identifies and blocks IP addresses that generate an unnaturally high frequency of clicks in a short period. It serves as a frontline defense against automated click bots and simple click farm arrangements that repeatedly hit ads from a single source to deplete budgets.

FUNCTION checkClickFrequency(ipAddress, timestamp):
  // Define time window and click threshold
  TIME_WINDOW_SECONDS = 60
  MAX_CLICKS_PER_WINDOW = 5

  // Get historical click timestamps for the IP
  recentClicks = getClicksForIP(ipAddress, within_seconds=TIME_WINDOW_SECONDS)
  
  // Add current click to the list for this check
  addClick(ipAddress, timestamp)

  // Rule: If clicks from this IP exceed the threshold in the time window, flag it.
  IF count(recentClicks) + 1 > MAX_CLICKS_PER_WINDOW:
    RETURN "FRAUDULENT: Exceeded Click Frequency Threshold"
  
  RETURN "VALID"

Example 3: Data Center IP Blocking

This logic checks if a click originates from an IP address registered to a known data center or hosting provider rather than a residential or mobile network. Since legitimate users typically don’t browse the web from servers, this is a strong indicator of non-human, bot-driven traffic.

FUNCTION checkIPOrigin(ipAddress):
  // Query an external or internal database of data center IP ranges
  isDataCenterIP = queryDataCenterDB(ipAddress)

  // Rule: Block any traffic originating from a known data center IP.
  IF isDataCenterIP == TRUE:
    RETURN "FRAUDULENT: Traffic from Data Center"
  
  RETURN "VALID"

📈 Practical Use Cases for Businesses

Campaign Shielding – Automatically blocks clicks from known bots, data centers, and suspicious proxies in real time, ensuring that ad spend is directed exclusively toward reaching genuine potential customers and not wasted on fraudulent interactions.
Analytics Purification – Filters out non-human and invalid traffic before it pollutes marketing analytics platforms. This ensures that metrics like Click-Through Rate (CTR), conversion rates, and user engagement data reflect true human behavior, enabling more accurate decision-making.
Lead Generation Integrity – Prevents fake form submissions and sign-ups generated by bots. By ensuring that only genuinely interested users can submit information, businesses improve the quality of their lead funnels and save sales teams from wasting time on fabricated leads.
Retargeting Audience Refinement – Excludes fraudulent users from being added to retargeting audiences. This prevents businesses from spending money to re-engage bots that have interacted with their site, leading to more efficient and effective retargeting campaigns with a higher ROI.

Example 1: Geolocation Mismatch Rule

// This logic prevents fraud from users whose IP location doesn't match their device's stated timezone.
// It's useful for blocking clicks from users trying to mask their location via proxies or VPNs.

FUNCTION checkGeoMismatch(ip_location, device_timezone):
  expected_timezone = getTimezoneForLocation(ip_location)

  IF device_timezone != expected_timezone:
    // Action: Add the IP to a temporary blocklist and flag the click as invalid.
    BLOCK_IP(ip_location.ip_address)
    LOG_FRAUD('Geo Mismatch', ip_location.ip_address)
    RETURN FALSE // Invalid Click
  
  RETURN TRUE // Valid Click

Example 2: Session Behavior Scoring

// This logic scores a user's session based on human-like behavior.
// It helps differentiate between an engaged human and a bot that only clicks an ad.

FUNCTION scoreSession(session_data):
  score = 0
  
  // Award points for human-like interactions
  IF session_data.mouse_moved_before_click:
    score += 40
  
  IF session_data.scrolled_down_page:
    score += 30

  // Penalize for bot-like interactions
  IF session_data.time_on_page < 2_SECONDS:
    score -= 50

  // A score below a certain threshold indicates likely fraud
  IF score < 20:
    FLAG_FOR_REVIEW(session_data.user_id)
    RETURN "SUSPICIOUS"
  
  RETURN "VALID"

🐍 Python Code Examples

Example 1: Detect Abnormal Click Frequency

This script analyzes a list of click events to identify IP addresses that exceed a defined click threshold within a short time frame. This is a common technique for catching basic bots or manual fraud attempts that generate numerous clicks from the same source.

from collections import defaultdict

clicks = [
    {'ip': '81.2.69.142', 'timestamp': 1672531201},
    {'ip': '81.2.69.142', 'timestamp': 1672531202},
    {'ip': '192.168.1.10', 'timestamp': 1672531203},
    {'ip': '81.2.69.142', 'timestamp': 1672531204},
    {'ip': '81.2.69.142', 'timestamp': 1672531205},
]

def find_rapid_click_fraud(click_data, max_clicks=3, time_window_sec=10):
    ip_clicks = defaultdict(list)
    fraudulent_ips = set()

    for click in click_data:
        ip = click['ip']
        timestamp = click['timestamp']
        
        # Remove clicks outside the time window
        ip_clicks[ip] = [t for t in ip_clicks[ip] if timestamp - t < time_window_sec]
        
        # Add the new click
        ip_clicks[ip].append(timestamp)
        
        # Check if the number of clicks exceeds the max limit
        if len(ip_clicks[ip]) > max_clicks:
            fraudulent_ips.add(ip)
            
    return list(fraudulent_ips)

fraud_ips = find_rapid_click_fraud(clicks)
print(f"Fraudulent IPs detected: {fraud_ips}")

Example 2: Filter Suspicious User Agents

This code checks a user agent string against a list of known non-browser or bot-like signatures. It's a simple but effective way to filter out traffic from common web scrapers and automated scripts that don't attempt to hide their identity.

def filter_suspicious_user_agent(user_agent):
    suspicious_signatures = [
        "bot", "crawler", "spider", "headless", "scraping"
    ]
    
    # Convert to lowercase for case-insensitive matching
    ua_lower = user_agent.lower()
    
    for signature in suspicious_signatures:
        if signature in ua_lower:
            return True # Flag as suspicious
            
    return False # Likely a legitimate browser

user_agent_1 = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
user_agent_2 = "Python-urllib/3.9 (a simple scraping bot)"

is_suspicious_1 = filter_suspicious_user_agent(user_agent_1)
is_suspicious_2 = filter_suspicious_user_agent(user_agent_2)

print(f"User Agent 1 is suspicious: {is_suspicious_1}")
print(f"User Agent 2 is suspicious: {is_suspicious_2}")

🧩 Architectural Integration

Placement in Traffic Flow

Active user analysis systems are typically positioned as an intermediary layer between the ad click and the final destination URL. When a user clicks an ad, their request is first routed to the analysis server instead of the advertiser's website. This inline placement allows the system to inspect the click in real time and decide whether to block it or allow it to proceed to the landing page. This prevents fraudulent traffic from ever reaching the advertiser's site or analytics platforms.

Data Source Dependencies

The system relies on data collected directly from the incoming user request. Essential data sources include the web server's logs and HTTP headers, which provide information like IP addresses, user-agent strings, timestamps, and referral URLs. For more advanced analysis, the system may deploy JavaScript on the publisher's page to collect browser-level data, such as screen resolution, installed fonts, and behavioral metrics like mouse movements and key presses, providing deeper insight into the user's authenticity.

Integration with Other Components

This analysis engine integrates with multiple components of the ad-tech stack. It communicates with the ad platform (like Google Ads) to receive click information and, in turn, can use the platform's API to automatically update IP exclusion lists. It sits behind a web server or reverse proxy (like Nginx or HAProxy) that forwards traffic for inspection. For reporting, it sends processed data to an analytics backend or data warehouse, where marketers can view dashboards on traffic quality and blocked threats.

Infrastructure and APIs

The core infrastructure is often a high-availability server cluster capable of handling large volumes of click traffic with low latency. Communication is typically handled via standard web protocols. For instance, an advertiser might integrate the service by modifying the ad's destination URL to point to the analysis system's endpoint. The system itself might use a REST API to query external threat intelligence databases (e.g., for IP reputation) or to communicate its findings to the advertiser's own systems via webhooks.

Operational Mode

Active user analysis primarily operates in an inline, synchronous mode. The decision to block or allow a click must be made in milliseconds to avoid negatively impacting the user experience for legitimate visitors. While the real-time blocking is synchronous, deeper analysis and model training can occur asynchronously. Data from all clicks, both valid and fraudulent, is logged and processed in the background to identify new fraud patterns and update the detection algorithms, ensuring the system adapts to evolving threats.

Types of Active users

Heuristic Rule-Based Analysis: This method uses predefined rules and thresholds to identify suspicious behavior. It flags traffic based on static indicators like clicks from a known data center IP, a user-agent string associated with bots, or an unrealistic number of clicks from one user in a short time.
Behavioral Analysis: This type focuses on assessing whether the user's actions are human-like. It analyzes dynamic data such as mouse movements, scroll velocity, and keyboard input patterns to distinguish between natural human interaction and the rigid, automated actions of a script or bot.
Statistical Anomaly Detection: This approach uses statistical models to establish a baseline of "normal" traffic patterns. It then flags deviations from this baseline as potentially fraudulent, such as a sudden, unexpected spike in traffic from a specific country or an unusually high click-through rate from a single publisher.
Device & Browser Fingerprinting: This technique collects a combination of attributes from a user's device and browser (e.g., operating system, browser version, screen resolution, installed plugins). It creates a unique ID to track users, identifying bots that use inconsistent configurations or try to spoof multiple devices from one machine.
IP Reputation & Geolocation Analysis: This method checks the click's IP address against databases of known malicious actors, proxies, and VPNs. It also analyzes the geographic location of the IP, flagging traffic that originates from regions outside the campaign's target area or locations known for high levels of fraudulent activity.

🛡️ Common Detection Techniques

IP Address Analysis: This involves examining the source IP address of a click to check if it belongs to a known data center, a proxy/VPN service, or is on a reputation blacklist. Repeated clicks from a single IP are a strong indicator of automated fraud.
User-Agent and Header Inspection: This technique analyzes the HTTP headers of a request, particularly the User-Agent string. It identifies bots by spotting known bot signatures, inconsistencies (e.g., a mobile browser reporting a desktop OS), or missing headers that normal browsers include.
Behavioral Analysis: This method assesses whether a user's on-page actions appear human. It tracks mouse movements, scroll speed, time on page, and click patterns to differentiate between the natural, varied behavior of a person and the predictable, mechanical actions of a bot.
Click Frequency and Timing Analysis: This technique monitors the rate and timing of clicks from a user or IP address. An unnaturally high number of clicks in a short period or clicks occurring at perfectly regular intervals are flagged as clear signs of automated scripts.
Device Fingerprinting: This technique creates a unique identifier by combining various attributes of a device and browser, such as OS, browser version, screen resolution, and plugins. It detects fraud by identifying when a single entity tries to mimic multiple users or uses inconsistent device profiles.
Geolocation Verification: This method compares the geographic location of a user's IP address with other data points, such as their browser's timezone or the campaign's targeting settings. A significant mismatch can indicate that the user is masking their true location to commit fraud.
Honeypot Traps: This involves placing invisible links or form fields on a webpage. Since human users cannot see or interact with these elements, any clicks or submissions are immediately identified as originating from a bot that is programmatically interacting with the page's code.

🧰 Popular Tools & Services

Tool	Description	Pros	Cons
ClickPatrol	A real-time click fraud detection tool that monitors ad traffic, identifies invalid clicks from bots and competitors, and automatically blocks fraudulent IPs. It is designed to protect PPC campaigns on platforms like Google Ads.	Real-time monitoring and blocking; customizable rules; user-friendly interface and provides detailed reports for refund claims.	Primarily focused on PPC protection; may require some setup for full effectiveness.
ClickCease	An automated click fraud detection and blocking service that supports Google Ads and Facebook Ads. It uses detection algorithms to identify and block fraudulent IPs in real-time and provides detailed analytics on blocked traffic.	Supports multiple ad platforms; features session recordings and VPN/proxy blocking; offers industry-specific detection settings.	Can have limitations on the number of clicks monitored in lower-tier plans; some advanced features may require a learning curve.
ClickGUARD	Offers granular control for PPC managers to set custom rules for fraud detection. It analyzes traffic in real-time to identify and block all types of invalid click activity, including from sophisticated bots and competitors.	Highly customizable rules; provides in-depth monitoring and forensic analysis; supports multiple platforms.	The high level of customization might be complex for beginners; can be more expensive than simpler solutions.
Spider AF	An ad fraud detection tool that scans device and session-level data to identify signs of bot behavior. It installs a tracker on websites to analyze traffic metrics like referrers, IP addresses, and user agents to block invalid activity.	Comprehensive data analysis; effective at identifying sophisticated bots through behavioral patterns; offers a free diagnosis.	Requires installing a tracking script on all website pages for maximum effectiveness; focus is more on detection and analysis than just blocking.

💰 Financial Impact Calculator

Budget Waste Estimation

Industry Ad Fraud Rates: Invalid traffic can account for 10% to over 40% of clicks, depending on the industry and traffic sources.
Example Wasted Spend: On a $10,000 monthly ad budget, this translates to between $1,000 and $4,000 being spent on clicks with zero potential for conversion.
Hidden Costs: Beyond direct ad spend, wasted budget also includes the administrative and analytics overhead spent managing and analyzing worthless traffic.

Impact on Campaign Performance

Inflated Cost-Per-Acquisition (CPA): Fraudulent clicks increase your costs without adding conversions, which artificially inflates your CPA and makes campaigns appear unprofitable.
Corrupted Analytics Data: Invalid clicks skew key metrics like click-through rates (CTR) and conversion rates, leading to poor strategic decisions and misallocation of marketing resources.
Reduced Marketing Agility: When data is unreliable, it becomes difficult to make quick, informed decisions to optimize campaigns, hindering your ability to respond to market changes.

ROI Recovery with Fraud Protection

Direct Budget Savings: By blocking fraudulent clicks, businesses can immediately reclaim 10-40% of their ad spend, which can be reinvested into reaching genuine customers.
Improved ROI: With cleaner traffic, conversion rates become more accurate and CPAs decrease, leading to a significant and measurable improvement in return on investment (ROI).
Increased Campaign Effectiveness: By focusing the entire budget on real users, the overall reach and impact of marketing efforts are maximized, leading to sustainable growth.

Implementing active user analysis is a strategic investment that directly enhances budget efficiency, restores trust in performance data, and maximizes the financial return of digital advertising efforts.

📉 Cost & ROI

Initial Implementation Costs

The setup costs for an active user analysis system can vary significantly. For a SaaS solution, this may involve a monthly subscription fee ranging from a few hundred to several thousand dollars, with minimal integration effort. A custom in-house build would require significant upfront investment in development, infrastructure, and threat intelligence data feeds, potentially ranging from $20,000 to $100,000+ depending on scale and sophistication.

Expected Savings & Efficiency Gains

The primary benefit is the direct recovery of ad spend that would otherwise be wasted on fraudulent clicks. Businesses can also see significant labor savings by automating the process of identifying and blocking bad traffic, freeing up marketing and data analysis teams to focus on strategy rather than manual data cleaning.

Budget Recovery: Up to 15-30% of paid media spend can be saved by eliminating invalid traffic.
CPA Improvement: A 10-20% reduction in Cost Per Acquisition due to cleaner, higher-converting traffic.
Operational Efficiency: Reduction in manual review and data analysis hours.

ROI Outlook & Budgeting Considerations

The Return on Investment (ROI) for click fraud protection is often high and immediate, typically ranging from 150% to over 400%, as the savings in ad spend directly offset the cost of the service. For small businesses, the ROI is seen in direct budget savings. For enterprise-scale deployments, it's measured in improved data integrity and more reliable strategic decision-making. A key risk is underutilization, where a powerful tool is not configured correctly, leading to either missed fraud or the blocking of legitimate users (false positives).

Ultimately, active user analysis contributes to long-term budget reliability and scalable ad operations by ensuring that marketing investments are directed only at authentic audiences.

📊 KPI & Metrics

To measure the effectiveness of active user analysis in fraud protection, it's crucial to track metrics that reflect both its detection accuracy and its impact on business outcomes. Monitoring these key performance indicators (KPIs) helps ensure the system is blocking bad traffic without inadvertently harming campaign performance or user experience.

Metric Name	Description	Business Relevance
Invalid Traffic (IVT) Rate	The percentage of total traffic identified and blocked as fraudulent or invalid.	Indicates the overall level of threat and the system's ability to reduce it.
False Positive Rate	The percentage of legitimate user clicks that are incorrectly flagged as fraudulent.	A critical metric for ensuring that the protection doesn't block potential customers.
Cost-Per-Acquisition (CPA) Change	The change in CPA after implementing fraud protection, expecting a decrease.	Directly measures the financial efficiency gained by filtering out non-converting traffic.
Conversion Rate Uplift	The increase in the conversion rate of the remaining (clean) traffic.	Shows the improved quality of traffic reaching the website.
Wasted Ad Spend Reduction	The total ad spend saved by blocking fraudulent clicks.	Quantifies the direct return on investment (ROI) of the fraud protection system.

These metrics are typically monitored through real-time dashboards provided by the fraud detection service or integrated into a company's own analytics platforms. Regular review of these KPIs allows teams to fine-tune the sensitivity of fraud filters, ensuring an optimal balance between aggressive protection and allowing all legitimate traffic to pass through.

🆚 Comparison with Other Detection Methods

Accuracy and Sophistication

Compared to static IP blacklisting, active user analysis is significantly more accurate. Blacklisting can block legitimate users if an IP is shared or reassigned, and it is ineffective against attackers who rotate IPs. Active user analysis, by contrast, evaluates behavior and technical markers for each click, allowing it to detect new threats and reduce false positives. It is more nuanced than signature-based detection, which can be evaded by modern bots that mimic legitimate browser signatures.

Real-Time vs. Post-Click Analysis

Active user analysis operates in real-time (inline), blocking fraud before it consumes a budget or corrupts data. This is a major advantage over methods that rely on post-click or batch analysis of server logs. While log analysis can identify fraud after the fact and help with refund claims, it does not prevent the initial damage to campaign momentum and data integrity. Active analysis provides proactive protection.

Effectiveness Against Bots

Compared to CAPTCHAs, active user analysis offers a better user experience and stronger security against modern bots. Advanced bots can now solve CAPTCHAs using AI, and the challenges introduce friction for legitimate users. Active analysis works invisibly in the background, identifying bots based on their intrinsic technical and behavioral attributes rather than forcing users to prove they are human, which is a more robust and user-friendly approach.

⚠️ Limitations & Drawbacks

While powerful, active user analysis is not a perfect solution and comes with its own set of limitations. These drawbacks can affect its efficiency, accuracy, and suitability for every scenario, especially as fraudsters develop more advanced evasion techniques.

False Positives: Overly aggressive rules can incorrectly flag legitimate users as fraudulent, especially if they use VPNs, privacy-focused browsers, or have unusual browsing habits, leading to lost business opportunities.
Sophisticated Bot Evasion: Advanced bots can mimic human behavior—such as plausible mouse movements and variable click timing—making them difficult to distinguish from real users through behavioral analysis alone.
Encrypted Traffic Blind Spots: The increasing use of encryption (HTTPS) can limit the visibility of certain data packets, making some forms of network-level inspection less effective for systems not designed to handle it.
High Resource Consumption: Real-time analysis of every click can be computationally intensive, requiring significant server resources to avoid adding latency to the user's journey, which can be costly at scale.
Integration Complexity: Integrating an active analysis system into a complex ad-tech stack can be challenging, requiring careful coordination between ad platforms, servers, and analytics tools to ensure data flows correctly.
Adaptation Lag: There is often a delay between the emergence of a new bot or fraud technique and the development of a rule or model to detect it, leaving a window of vulnerability for attackers.

Given these limitations, it is often best to use active user analysis as part of a multi-layered security strategy that includes other methods like IP reputation lists and statistical modeling.

❓ Frequently Asked Questions

How does active user analysis differ from simple IP blocking?

Simple IP blocking relies on a static list of known bad IPs. Active user analysis is more dynamic; it evaluates the behavior and technical properties of each click in real-time, such as mouse movement, device inconsistencies, and click frequency, to make a decision. This allows it to catch new threats from unknown IPs and reduces the risk of blocking legitimate users who might be sharing a flagged IP.

Can active user analysis stop all types of click fraud?

No method is foolproof. While highly effective against automated bots and unsophisticated fraud, it can struggle to detect very advanced bots that perfectly mimic human behavior or large-scale human click farms where real people are generating the invalid clicks. Therefore, it is best used as a core component within a broader, multi-layered anti-fraud strategy.

Will implementing active user analysis slow down my website for real users?

Professionally designed fraud detection systems operate inline with very low latency, typically adding only milliseconds to the redirect time. This process is imperceptible to legitimate human users. The goal is to provide robust security without introducing friction or degrading the user experience for genuine visitors.

Is this type of analysis compliant with privacy regulations like GDPR?

Reputable fraud detection services are designed to be compliant with major privacy regulations. They typically analyze data for security purposes without storing personally identifiable information (PII) long-term or using it for other means. However, it is crucial to verify the compliance of any specific tool, as data processing, especially of IP addresses, falls under the scope of regulations like GDPR.

What happens when a click is identified as fraudulent?

When a click is flagged as fraudulent, the system typically takes a protective action. This usually involves blocking the click from reaching your website, so you don't pay for it and your analytics are not affected. The fraudulent IP or device fingerprint is then logged and can be added to an exclusion list to prevent future clicks from that source.

🧾 Summary

In the context of fraud prevention, Active Users refers to a real-time analysis method used to validate the legitimacy of traffic interacting with digital ads. It functions by actively inspecting behavioral and technical signals from each user—such as mouse movements, device properties, and IP reputation—to differentiate between genuine humans and fraudulent bots or scripts. This is crucial for preventing click fraud, protecting advertising budgets, and ensuring marketing data remains clean and reliable.