Anura Docs

Anura Definitions

Here you will find common terminology used within Anura Script and Anura Direct in the analysis of traffic quality.

Anura Definitions - Script Results

Anura Script uses JavaScript and is our most comprehensive method for collecting visitor data. It excels at identifying the most sophisticated fraud in real-time. Ideally, Anura Script is used for conversions and post-click analysis.


The visitor passed all of our tests and is considered GOOD. We feel this is a real person, with real conversion value.


The visitor tripped a rule or two in our system. They are not definitely GOOD or BAD, so we mark them as a WARNING. The vast majority of the time this traffic represents real people with real conversion value. We have found that this accounts for our competitors false positives. In instances where it's been necessary, blocking WARNING has aligned our results with other system's confidence scores.


We are 100% confident that this is not a real visitor. Our successful clients block or otherwise divert BAD visitors to create the cleanest and most valuable traffic for themselves and their clients.

Anura Definitions - Direct Results

Anura Direct is designed to be used with clients who do not have the ability to place our JavaScript code on their web assets. It is best for server-to-server communication or when there is limited visitor information. Anura Direct delivers real-time analysis in time-sensitive situations, typically used for click analysis, pre-bid and programmatic campaigns.


Based on the visitor data received, Anura did not find abnormalities consistent with invalid traffic. The visitor should be considered NON-SUSPECT.


Anura detected issues with the received visitor data and considers the visitor to be SUSPECT. These issues will likely represent a risk to clients required key performance indicators.

Anura Definitions - Rule Sets

To make accurate judgments on visitor quality, Anura has built a continually growing list of rules and heuristics. To provide transparency as to why traffic is scored as BAD or WARNING, we have categorized each of these rules/heuristics into one of seven different Rules Sets. Each of these Rule Sets have multiple rules/heuristics to ensure the accuracy of the Rule Set.

Data Integrity (DI)

Anura monitors the consistency of visitor data by reviewing both the request and response data. This comprehensive approach guarantees that data integrity is ensured throughout the entire session. Any deviation from this standard is considered fraudulent activity and is promptly identified by Anura's fraud systems. The identification of such inconsistencies triggers the corresponding rule(s) within Anura's Data Integrity rule set, which helps to ensure the accuracy and reliability of visitor data quality assessments. Clients can rest assured that Anura's commitment to data integrity is unwavering, and that the data analysis process is designed to provide them with the highest level of assurance possible.

User Environment (UE)

Anura conducts a thorough investigation of a visitor's credibility by examining various factors, including the use of a Virtual Proxy Network (VPN) and other identifiable device and system components. This comprehensive approach enables Anura to identify any anomalies or inconsistencies that may suggest fraudulent activity. By leveraging this multifaceted strategy, Anura delivers a reliable assessment of visitor quality and provides clients with the assurance they need to make informed decisions.

Data Center (DC)

Anura scrutinizes a user's device and system-level identity for indicators of server-based criterion such as hardware, geo-location data, IP address and other factors commonly associated with data centers. By doing so, we can determine if a visitor originated from an established data center. If identified, Anura promptly flags these visitors as fraudulent, providing clients with definitive assessments of visitor quality.

Traffic Origin (TO)

Anura analyzes a visitor's journey to discern their origin prior to visiting your web asset. Our system examines prior origin data to determine if a user came from known fraudulent sites or if they show signs of questionable origination. This approach enables Anura to identify and flag any potentially fraudulent activity effectively, providing clients with reliable assessments of visitor quality based on known malicious origin.

IP Integrity (IP)

An IP address is similar to a physical address and can be associated with fraudulent human activity and automated bots. This can lead to harmful activities targeting your assets. Anura validates each visitor's IP address for integrity using a combination of proprietary factors, as well as referencing historical data of known malicious activity.

Spoofing (SP)

Spoofing involves disguising oneself as a known and trusted visitor. Common methods include spoofing of IP addresses, User-Agents, Devices as well as masking DNS server origins. To combat such malicious activities, Anura utilizes multiple data points to accurately identify spoofed information, providing robust defense against even the most sophisticated spoofing attacks and user manipulation.

Web Crawler (WC)

Web crawlers are internet bots that index the contents of websites. While web crawlers are generally used legitimately, they may also have the potential to be used by fraudsters to scrape sensitive data or otherwise harm a client's assets. In addition to ensuring visitors are not known web crawlers, Anura also checks for suspicious behavior that may indicate malicious intent from bots.

Anura Definitions - Invalid Traffic Types

Invalid traffic, often known as IVT, refers to any form of web traffic that is derived from a non-human source. In some cases, this kind of traffic exists for a good reason, like search engine crawlers. However, most of the time, IVT is used to refer to fraudulent traffic.

General Invalid Traffic (GIVT)

General Invalid Traffic or GIVT refers to bots, crawlers, spiders, or any of the kind of non-human traffic typically routed from a data center IP address. GIVT can also apply to activity-based filtration or browsers that pre-render pages. Most of the time, GIVT is easy to identify and exclude from results.

Sophisticated Invalid Traffic (SIVT)

SIVT stands for Sophisticated Invalid Traffic. SIVT techniques are far more challenging to detect. This can include advanced bots that closely mimic human traffic, hijacked devices, malware, invalid proxy traffic, cookie manipulation techniques, like cookie stuffing, or human fraud farms.

Anura Definitions - General Definitions


Any time you query Anura to scan and analyze traffic. This can represent an impression, a click, a form fill, a credit card transaction, and many other forms of interactions with your web assets.


A Drop occurs when Anura Script does not correctly receive the required data to properly analyze the visitor. This may occur for a number of reasons such as a slow internet connection or device, an integration issue, or intentional blocking or altering of our data package to avoid screening.

Ad Blocker

Anura Script will detect and report if the visitor is using an ad blocker.


Anura will detect and report if the visitor is using a mobile device.


Variables containing dynamic data that are passed to Anura to be used within the analytics dashboard.


If a client does not pass a Source or Campaign to Anura, it's value will be returned as (undefined).


If Anura is unable to successfully identify a metric, it's value will be returned as (non-identifiable).

Fraud Rates

Fraud Rates represent the percentage of fraud across your instances, sources, and campaigns.

Fraud Trends

Keep track of the changes in Fraud Rate over time. This metric can be used to judge the effectiveness of your fraud mitigation strategy.

Potential Savings

Estimated amount of money saved by implementing a trusted, real-time ad fraud solution.

Traffic Acquisition Cost (TAC)

The monthly budget spent to drive relevant traffic to your web assets.

Additional Data

Data provided by an Anura client that is to be associated with an individual request.

Additional Data Definition

A self-mapped label that can be inserted into a Raw Data Report column header in place of a default value.

Raw Data

Data associated with an individual request.