Greenlight Review

How Greenlight Review Works

Incoming Traffic → [Greenlight Filter] →+→ Legitimate Traffic → (View Ad/Website)
(Clicks/Impressions)  │                   └─→ Blocked/Flagged Traffic → (Analytics Log)
                      │
                      └─ (Analysis: IP, Device, Behavior, Rules)

Greenlight Review operates as a frontline defense mechanism, scrutinizing digital traffic before it triggers a billable event like a click or impression. The system is designed to be a fast, real-time checkpoint that separates valid human users from fraudulent or automated traffic sources, such as bots. By doing so, it ensures that advertising budgets are spent on reaching genuine potential customers and that analytics data remains clean and reliable.

Initial Data Ingestion

When a user is about to interact with an ad, the Greenlight Review process begins. The system instantly collects a range of data points associated with the traffic source. This includes network-level information like the IP address and ISP, device-level data such as the user agent, operating system, and browser type, and contextual data like the time of day and geographic location. This initial data snapshot serves as the foundation for the analysis.

Real-Time Analysis Engine

The collected data is fed into an analysis engine that applies a multi-layered set of rules and models. This engine checks the traffic against known blocklists of fraudulent IPs (from data centers, proxies, or VPNs) and suspicious device signatures. It also employs heuristic analysis to spot anomalies, such as impossibly fast click speeds, non-human-like session patterns, or mismatches between the user’s purported location and their system settings. This is the core of the “review” where a decision is made almost instantaneously.

Decision and Routing

Based on the analysis, the system assigns a risk score to the traffic. If the traffic is deemed legitimate and low-risk, it is “greenlit” and allowed to proceed to the ad or website. This process is seamless and invisible to the genuine user. If the traffic is flagged as high-risk or definitively fraudulent, it is blocked. Instead of seeing the ad, the fraudulent source is either dropped or redirected, and the event is logged for further analysis without charging the advertiser.

Breakdown of the ASCII Diagram

Incoming Traffic

This represents the flow of all potential ad interactions (clicks, impressions) from various digital channels heading toward a protected campaign. It’s the raw, unfiltered stream of users and bots that the system must evaluate.

Greenlight Filter

This is the central decision-making component. It symbolizes the real-time analysis where multiple data points—IP reputation, device fingerprints, and behavioral rules—are checked to validate the traffic’s authenticity. It functions as a critical checkpoint.

Legitimate vs. Blocked Traffic

This shows the two possible outcomes. The greenlit path allows genuine users to proceed to the intended destination (the ad or website). The blocked path diverts fraudulent traffic away, preventing it from wasting ad spend and corrupting analytics data. This separation is the primary function of the review process.

🧠 Core Detection Logic

Example 1: Data Center IP Filtering

This logic prevents bots hosted on servers from accessing ads. Data center IPs are a common source of non-human traffic used for automated ad fraud. By blocking these IPs before a bid is placed or a click is registered, this rule filters out a significant volume of general invalid traffic (GIVT).

FUNCTION check_ip(ip_address):
  // Pre-defined list of known data center IP ranges
  DATA_CENTER_RANGES = ["69.171.251.0/24", "157.240.0.0/16", ...]

  FOR each range IN DATA_CENTER_RANGES:
    IF ip_address is_within(range):
      RETURN "BLOCK" // Traffic is from a known data center

  RETURN "ALLOW"

Example 2: Click Timestamp Anomaly

This logic identifies non-human behavior by measuring the time between an ad being rendered (impression) and the click event. Bots often click much faster than a human possibly could. Setting a minimum time threshold helps filter out this automated activity.

FUNCTION check_click_speed(impression_time, click_time):
  // Minimum realistic time for a human to react
  MIN_REACTION_TIME_MS = 100 // 100 milliseconds

  time_difference = click_time - impression_time

  IF time_difference < MIN_REACTION_TIME_MS:
    RETURN "FLAG_AS_FRAUD" // Click is too fast to be human

  RETURN "VALID"

Example 3: Geo Mismatch Heuristics

This logic flags sessions where the user's IP-based geolocation is inconsistent with their device's language or timezone settings. For example, an IP address from Vietnam paired with a device set to Central European Time and English (US) is suspicious and could indicate a proxy or a compromised device.

FUNCTION check_geo_consistency(ip_geo, device_timezone, device_language):
  // Check if IP location aligns with expected timezone
  expected_timezone = lookup_timezone(ip_geo.country)

  IF device_timezone != expected_timezone:
    // Mismatch could indicate proxy or VPN usage
    INCREASE_RISK_SCORE(value=2)

  // Further checks can be added for language consistency
  IF RISK_SCORE > threshold:
    RETURN "REVIEW_MANUALLY"

  RETURN "ALLOW"

📈 Practical Use Cases for Businesses

• Campaign Shielding: Proactively block invalid traffic from depleting PPC or CPM budgets on platforms like Google Ads, ensuring spend is allocated to reaching real potential customers.
• Analytics Integrity: Prevent bot and fraudulent activity from skewing key business metrics like click-through rates, conversion rates, and user engagement, leading to more accurate data-driven decisions.
• Lead Generation Filtering: Protect web forms and lead-generation campaigns from spam and bot submissions, ensuring the sales team receives higher-quality, legitimate leads.
• Return on Ad Spend (ROAS) Optimization: By eliminating wasteful spending on fraudulent clicks and impressions, businesses can significantly improve their ROAS and the overall efficiency of their advertising efforts.

Example 1: Geofencing Rule for Local Services

A local plumbing business running a campaign targeting only New York City can use Greenlight Review to automatically block clicks from outside its service area, saving money and improving lead quality.

RULE: Local_Campaign_Geofence
  // Define the target geographical area
  TARGET_CITY = "New York"
  TARGET_STATE = "NY"

  // Analyze incoming click's IP location
  click_location = get_geolocation(request.ip_address)

  // Block if outside the target area
  IF click_location.city != TARGET_CITY OR click_location.state != TARGET_STATE:
    ACTION: BLOCK_CLICK
    REASON: "Out of Service Area"
  ELSE:
    ACTION: ALLOW_CLICK

Example 2: Session Scoring for E-commerce

An online retailer can use a scoring system to evaluate traffic quality. A session gets a high-risk score if it exhibits multiple suspicious behaviors, such as using a known VPN and having an outdated browser, and is blocked before it can browse product pages.

FUNCTION score_session(session_data):
  risk_score = 0

  // Rule 1: Check for VPN/Proxy
  IF is_vpn_or_proxy(session_data.ip):
    risk_score += 3

  // Rule 2: Check for known bot user agent
  IF is_bot_user_agent(session_data.user_agent):
    risk_score += 5

  // Rule 3: Check for headless browser signature
  IF has_headless_signature(session_data.browser_properties):
    risk_score += 4

  // Decision
  IF risk_score >= 5:
    RETURN "BLOCK_SESSION"
  ELSE:
    RETURN "ALLOW_SESSION"

🐍 Python Code Examples

This Python function checks if a given IP address exists within a predefined set of blocked IPs. This is a simple but effective way to filter out traffic from known malicious sources.

# A set of known fraudulent IP addresses
IP_BLOCKLIST = {"1.2.3.4", "5.6.7.8", "9.10.11.12"}

def filter_by_ip_blocklist(click_ip):
    """
    Returns True if the IP is on the blocklist, False otherwise.
    """
    if click_ip in IP_BLOCKLIST:
        print(f"Blocking fraudulent IP: {click_ip}")
        return True
    return False

# Example usage:
filter_by_ip_blocklist("5.6.7.8")

This example demonstrates how to detect abnormally high click frequency from a single user. It simulates tracking clicks and flags a user as a bot if they exceed a certain number of clicks in a short time window, a common pattern in click fraud.

import time

# Dictionary to store click timestamps for each user
user_clicks = {}
TIME_WINDOW = 60  # seconds
CLICK_LIMIT = 10

def detect_click_frequency(user_id):
    """
    Detects if a user is clicking too frequently.
    """
    current_time = time.time()
    if user_id not in user_clicks:
        user_clicks[user_id] = []

    # Add current click time and remove old ones
    user_clicks[user_id].append(current_time)
    user_clicks[user_id] = [t for t in user_clicks[user_id] if current_time - t < TIME_WINDOW]

    # Check if click limit is exceeded
    if len(user_clicks[user_id]) > CLICK_LIMIT:
        print(f"High frequency detected for user: {user_id}")
        return True
    return False

# Example usage:
for _ in range(12):
    detect_click_frequency("user-123")

Types of Greenlight Review

• Pre-Bid Filtering

  This type operates within programmatic advertising environments. Before an advertiser even bids on an ad impression, the traffic source is analyzed. If it's deemed low-quality or fraudulent, no bid is placed, saving money and resources upfront in the supply chain.

• On-Click Real-Time Analysis

  This is the most common form, where the validation happens at the exact moment a user clicks an ad. The system makes a split-second decision to either allow the user to proceed to the landing page or block the click as invalid.

• Session-Based Heuristics

  Instead of a single event, this method evaluates an entire user session. It analyzes patterns like navigation flow, time on page, and mouse movements. A session might be flagged mid-way if behavior deviates from human norms, offering deeper but slightly slower analysis.

• Signature-Based Greenlighting

  This method relies on fingerprinting traffic by creating a unique signature from dozens of data points (browser type, plugins, etc.). It then compares this signature against databases of known good (human) and bad (bot) signatures to make a determination.

🛡️ Common Detection Techniques

• IP Fingerprinting

  This technique involves analyzing an IP address to determine its reputation and origin. It checks if the IP belongs to a known data center, proxy, or VPN service, which are frequently used to mask fraudulent activity.

• Device and Browser Fingerprinting

  This method collects various attributes from a user's browser and device, such as installed fonts, user-agent string, and screen resolution. These attributes create a unique "fingerprint" to identify and track users, detecting anomalies that suggest bot activity.

• Behavioral Analysis

  Behavioral analysis monitors how a user interacts with a webpage, including mouse movements, scrolling speed, and click patterns. Bots often exhibit non-human behaviors, such as perfectly linear mouse paths or instantaneous clicks, which this technique can identify.

• Heuristic Rule-Sets

  This involves applying a set of logical rules to identify suspicious traffic. For instance, a rule might flag a user if their browser's language setting doesn't match the language common to their IP address's geographic location, indicating a potential attempt to disguise their origin.

• Timestamp Analysis

  This technique measures the time elapsed between different events, such as an ad impression and the subsequent click. Clicks that occur too quickly (e.g., within milliseconds of the ad loading) are flagged as non-human, as they fall outside the range of normal human reaction time.

🧰 Popular Tools & Services

Tool	Description	Pros	Cons
Traffic Sentinel	An enterprise-grade platform offering pre-bid and post-click analysis. It uses machine learning to detect sophisticated invalid traffic (SIVT) across display, video, and mobile campaigns.	Highly effective against advanced bots; provides detailed forensic reports; integrates with major DSPs.	High cost; can be complex to configure without dedicated support; may require significant data volume to be effective.
PPC Shield	A tool focused specifically on protecting Google Ads and Bing Ads campaigns. It automates the process of identifying and blocking fraudulent IPs to reduce wasted ad spend on search networks.	Easy to set up; affordable for small to medium-sized businesses; provides clear, actionable dashboards.	Primarily focused on click fraud (PPC); less effective for impression-based or programmatic fraud.
BotDetect AI	A service that specializes in behavioral analysis to distinguish between humans and bots. It focuses on how users interact with a site, rather than just their technical attributes.	Adapts quickly to new bot behaviors; can identify sophisticated bots that mimic human fingerprints; low false-positive rate.	May introduce minor latency; can be more resource-intensive; its effectiveness depends on having sufficient traffic for behavioral modeling.
AdVerify Suite	A comprehensive ad verification service that includes fraud detection, viewability measurement, and brand safety. It provides a holistic view of campaign quality and integrity.	All-in-one solution; provides context beyond just fraud; trusted by major agencies and brands.	Can be expensive; reporting can be overwhelming for smaller advertisers; some features may be unnecessary for basic fraud protection needs.

📊 KPI & Metrics

To effectively measure the success of a Greenlight Review implementation, it is crucial to track metrics that reflect both its technical filtering accuracy and its tangible business impact. Monitoring these Key Performance Indicators (KPIs) helps justify the investment and provides the necessary feedback to fine-tune detection rules for optimal performance.

Metric Name	Description	Business Relevance
Invalid Traffic (IVT) Rate	The percentage of total traffic identified and blocked as fraudulent or non-human.	Indicates the overall volume of threats being neutralized and the necessity of the protection system.
False Positive Rate	The percentage of legitimate human traffic that was incorrectly flagged and blocked as fraudulent.	A critical balancing metric; a high rate means potential customers are being blocked, impacting growth.
Ad Spend Savings	The estimated amount of advertising budget saved by preventing fraudulent clicks or impressions.	Directly demonstrates the financial return on investment (ROI) of the fraud prevention tool.
Conversion Rate Uplift	The percentage increase in conversion rates after filtering out non-converting fraudulent traffic.	Shows that the remaining traffic is of higher quality and more likely to result in desired actions.

These metrics are typically monitored through real-time dashboards provided by the traffic protection service. Alerts can be configured to notify administrators of unusual spikes in fraudulent activity or a rising false positive rate. This continuous feedback loop is essential for dynamically adjusting filtering rules to adapt to new threats while ensuring a seamless experience for legitimate users.

🆚 Comparison with Other Detection Methods

Real-Time vs. Post-Click Analysis

Greenlight Review is a pre-emptive, real-time method that blocks fraud before the advertiser is charged. This contrasts with post-click (or post-impression) analysis, which identifies invalid activity after it has already occurred. While post-click systems are useful for securing refunds and understanding fraud patterns, they do not prevent the initial waste of ad spend and the corruption of real-time campaign data.

Rule-Based Systems vs. Pure Machine Learning

While Greenlight Review heavily incorporates rules (e.g., IP blocklists), it is most effective as a hybrid system that also uses machine learning. Purely rule-based systems are fast but can be rigid and easily circumvented by new fraud techniques. Pure machine learning systems are more adaptive but may require more data to become accurate and can be slower. Greenlight Review aims for a balance, using rules for clear-cut cases and ML for nuanced, behavioral threats.

Active Filtering vs. Passive Challenges (CAPTCHA)

Greenlight Review is an active filtering method that makes a definitive block/allow decision behind the scenes. This differs from passive challenges like CAPTCHA, which only activate when traffic is already deemed suspicious. While CAPTCHAs can be effective at weeding out simple bots, they introduce friction into the user experience and are less effective against sophisticated bots. Active filtering provides a seamless experience for legitimate users and a hard stop for bots.

⚠️ Limitations & Drawbacks

While Greenlight Review is a powerful tool for fraud prevention, it has inherent limitations and is not a foolproof solution. Its effectiveness can be constrained by the sophistication of fraudulent actors and technical trade-offs, making it essential to understand its potential drawbacks in certain scenarios.

• False Positives – May incorrectly flag and block legitimate users who use VPNs, corporate proxies, or other privacy-enhancing tools that can mimic fraudulent patterns.
• Latency Introduction – The process of analyzing traffic in real-time, even if milliseconds, adds a small delay to ad serving or page loading, which could impact performance at a very large scale.
• Sophisticated Bot Evasion – Advanced bots are increasingly designed to mimic human behavior perfectly, making them difficult to distinguish from real users based on standard signals and potentially bypassing the review.
• Maintenance Overhead – Rule-based components require constant updates to keep pace with new fraud tactics, new data center IP ranges, and evolving bot signatures.
• Limited Scope – A Greenlight Review is typically focused on pre-click or pre-bid validation and may not catch fraud that occurs later in the user session or complex attribution fraud.
• Incomplete Data Picture – In some privacy-centric environments (like iOS), the system may have access to fewer data points, making it harder to make an accurate determination.

In cases involving highly sophisticated fraud or when zero user friction is required, a hybrid approach combining real-time filtering with post-analysis might be more suitable.

❓ Frequently Asked Questions

How does Greenlight Review differ from a web application firewall (WAF)?

A WAF primarily protects against website attacks like SQL injection and cross-site scripting by inspecting HTTP traffic. Greenlight Review is specialized for advertising, analyzing traffic signals like IP reputation and user behavior specifically to identify and block click fraud and invalid ad traffic.

Can Greenlight Review stop all ad fraud?

No solution can stop 100% of ad fraud. Greenlight Review is highly effective at blocking general and some sophisticated invalid traffic (GIVT and SIVT). However, the most advanced bots may still find ways to evade detection. It serves as a critical first line of defense in a multi-layered security strategy.

Does implementing this review process negatively affect user experience?

For legitimate users, the process is designed to be completely transparent and instantaneous, having no noticeable impact on their experience. The analysis happens in milliseconds before the ad or page content is fully loaded. The only users affected are the bots or fraudulent sources that are blocked.

How does the system handle new and emerging fraud techniques?

Effective Greenlight Review systems use machine learning and a continuous feedback loop. They analyze new patterns from blocked and allowed traffic to update their detection models. This allows the system to adapt and identify new types of threats as they emerge without requiring constant manual rule changes.

What happens to the traffic that gets blocked?

Blocked traffic is prevented from interacting with the ad and reaching the advertiser's website. The advertiser is not charged for the click or impression. The event is typically logged in an analytics dashboard, providing data on the source of the blocked traffic, which can be used for further analysis and reporting.

🧾 Summary

Greenlight Review is a preemptive ad fraud prevention method that validates digital traffic before it interacts with an advertisement. By analyzing data points like IP reputation, device characteristics, and user behavior in real-time, it filters out malicious bots and other invalid sources. This process protects advertising budgets, ensures the integrity of analytics data, and improves overall campaign effectiveness by allowing only legitimate users to proceed.

🔗 Related Articles

• What Is Click Fraud? How to Identify and Prevent It | DataDome – https://datadome.co/learning-center/what-is-click-fraud/
• Invalid Traffic (IVT) - XMP – https://www.xmp.com/support-center/invalid-traffic-ivt/
• How we prevent it - Google Ad Traffic Quality – https://adtrafficquality.withgoogle.com/how-we-prevent-it/
• Minimizing Click Fraud and Invalid Traffic: A Digital Advertiser's Checklist - HUMAN Security – https://www.human.security/blog/minimizing-click-fraud-and-invalid-traffic-a-digital-advertisers-checklist
• What is Pre Bid and Why It Matters in Programmatic Advertising? - Spider AF – https://spideraf.com/blog/what-is-pre-bid-and-why-it-matters-in-programmatic-advertising