What is SIVT?

How SIVT Works

SIVT represents the most advanced category of Invalid Traffic. Fraudlogix IVT Detection uses advanced behavioral analysis and machine learning to identify SIVT that evades basic detection methods. While simple bots can be identified through basic signatures—data center IPs, known user agents, obvious automation patterns—SIVT is specifically engineered to evade these detection methods.

The sophistication comes from multiple layers of obfuscation and mimicry. SIVT operators use residential IP addresses from real ISPs, not data centers. They rotate user agents and device fingerprints to appear as different legitimate users. They introduce random delays and variations to avoid pattern detection. Most critically, they use hijacked real devices or sophisticated emulation to generate interaction patterns that look genuinely human.

This creates a detection challenge: SIVT traffic exhibits many characteristics of legitimate users. It comes from residential IPs, uses standard browsers, moves the mouse, scrolls pages, and generates seemingly natural interaction patterns. Traditional fraud filters that rely on IP lists, user agent matching, or simple behavioral rules struggle to identify SIVT without generating excessive false positives.

Types of SIVT

SIVT manifests in several sophisticated forms, each using advanced techniques to evade detection:

Hijacked Devices & Malware

Real user devices infected with malware can be remotely controlled through botnets to generate fraudulent traffic. These devices have legitimate residential IPs, real browsing histories, and authentic device fingerprints—making them extremely difficult to distinguish from genuine users. The device owner typically has no idea their computer or phone is participating in fraud.

Advanced Bot Networks

Sophisticated bots that use machine learning to mimic human behavior patterns. They vary their click timing, mouse movements, scroll patterns, and navigation paths. Some even train on datasets of real user interactions to replicate authentic behavior at scale.

Residential Proxy Networks

Fraudsters route traffic through residential IP addresses using proxy services or compromised devices. This allows bot traffic to appear to come from legitimate home internet connections rather than obvious data center IPs. Each request can use a different residential IP, making IP-based blocking ineffective.

Device Emulation

Advanced systems that perfectly emulate real devices—generating authentic-looking device fingerprints, WebGL signatures, canvas fingerprints, and other technical identifiers. These emulators can create thousands of "unique" devices that appear completely legitimate to standard verification.

Cookie Manipulation & Session Hijacking

Stealing or synthesizing legitimate user cookies and session data to generate traffic that appears to come from authenticated, trusted users. This allows fraudsters to bypass protections that rely on user authentication or session history.

Human Fraud Farms

While technically human traffic, organized click farms that employ low-wage workers to manually perform fraudulent actions are classified as SIVT because they're designed to generate fraudulent activity while appearing legitimate. These operations can scale to hundreds or thousands of workers.

How to Detect SIVT

Detecting SIVT requires sophisticated, multi-layered analysis that goes far beyond simple IP or user agent checks:

Behavioral Analysis

Examine interaction patterns for subtle anomalies that indicate non-human behavior. Even sophisticated bots exhibit patterns—perfect timing intervals, unnatural mouse movements, impossible interaction speeds, or statistical distributions that differ from genuine users. Machine learning models can identify these subtle signatures.

IP Risk Scoring

While SIVT uses residential IPs, IP Risk Scoring can still identify suspicious characteristics—residential IPs with unusual hosting patterns, IPs associated with proxy services, IPs showing signs of malware infection, or IPs with historical fraud patterns. Real-time scoring catches SIVT that simpler IP blocking misses.

Device Fingerprinting

Analyze device characteristics at a deep level—not just user agent strings, but canvas fingerprints, WebGL signatures, sensor data, timing attacks, and dozens of other technical identifiers. SIVT often has subtle inconsistencies in these fingerprints that reveal emulation or manipulation.

Traffic Pattern Analysis

Look at aggregate traffic patterns rather than individual sessions. SIVT often generates traffic at unusual volumes, with suspicious timing patterns, or from geographic distributions that don't match expected user demographics. Statistical analysis can identify these anomalies.

Conversion & Engagement Analysis

SIVT generates traffic but rarely converts or engages meaningfully. Traffic sources with high volume but near-zero conversion rates, no repeat visits, or no downstream engagement often indicate sophisticated fraud.

Pre-Bid IP Blocking

Deploy Pre-Bid IP Blocklists that continuously update with known SIVT sources. While SIVT uses diverse IPs, many operate from identifiable proxy networks, malware-infected ranges, or compromised hosting infrastructure that can be blocked at the bid level.

SIVT vs GIVT: Key Differences

Understanding the distinction between SIVT and GIVT (General Invalid Traffic) is crucial for effective fraud prevention:

Detection Complexity

GIVT can be identified through routine means—known bot signatures, data center IPs, standard user agents, obvious automation patterns. SIVT requires advanced behavioral analysis, machine learning, and often human review to identify.

IP Characteristics

GIVT typically originates from data centers, hosting providers, and known bot networks. SIVT uses residential IPs from legitimate ISPs, making IP-based blocking much more challenging without sophisticated risk scoring.

Behavior Patterns

GIVT exhibits obvious non-human patterns—perfect timing, no mouse movement, impossibly fast interactions. SIVT mimics human behavior closely, with realistic mouse movements, varied timing, and believable interaction patterns.

Scale & Sophistication

GIVT operations can be massive but are relatively simple—large botnets running basic scripts. SIVT operations require significant technical sophistication, investment in proxy networks or device hijacking, and continuous adaptation to evade detection.

Economic Impact

While GIVT accounts for more total volume, SIVT causes disproportionate financial damage because it's harder to detect and often targets higher-value inventory. Advertisers may lose 2-3x more per fraudulent impression from SIVT versus GIVT.

How to Prevent SIVT

Effective SIVT prevention requires comprehensive, layered protection that combines multiple detection methods:

1. Programmatic IVT Detection

Deploy Programmatic IVT Detection with pixel-based monitoring that analyzes behavior, device characteristics, and interaction patterns in real-time. This catches SIVT through behavioral anomalies that simpler detection methods miss—identifying sophisticated fraud before it impacts your campaigns.

2. IP Risk Score API

Implement IP Risk Score to evaluate every traffic source in real-time. Even though SIVT uses residential IPs, risk scoring identifies suspicious characteristics—proxy patterns, malware signatures, historical fraud associations, and behavioral anomalies—that reveal sophisticated fraud sources.

3. Pre-Bid IP Blocklist

Use Pre-Bid IP Blocklists that continuously update with known SIVT sources including proxy networks, compromised device ranges, and identified fraud infrastructure. This prevents SIVT traffic from ever entering your ad auctions, saving budget before impressions are served.

4. Multi-Layered Verification

Implement multiple verification methods simultaneously—behavioral analysis, device fingerprinting, IP scoring, and conversion tracking. SIVT may evade one detection method, but combining multiple layers makes evasion exponentially more difficult.

5. Machine Learning Models

Deploy machine learning systems trained on both legitimate and fraudulent traffic patterns. These models can identify subtle statistical anomalies and evolving fraud techniques that rule-based systems miss.

6. Real-Time Blocking

Block identified SIVT sources in real-time, not just post-campaign. Pre-bid blocking and real-time filtering prevent you from paying for fraudulent impressions rather than requesting refunds after the fact.

7. Continuous Monitoring

SIVT evolves constantly, so detection systems must update continuously. Regular analysis of campaign performance, traffic quality metrics, and conversion patterns helps identify new SIVT techniques as they emerge.

Frequently Asked Questions