How to detect device farm fraud and prevent automated attacks

Fraud teams today face a persistent challenge: stopping attackers who use sophisticated automation and large-scale device networks to blend in with legitimate users. 

Device farms — clusters of physical and virtual devices controlled by fraudsters — are among the most formidable tools in this space. These setups can overwhelm standard defenses and allow fraudulent activity to slip through undetected. Understanding how device farms operate and recognizing the signals they generate are key to building defenses that keep these threats at bay.

If you’re looking to strengthen your defenses while improving user experience, now is the perfect time to start a free trial or contact our sales team to learn how Fingerprint can protect your business

What exactly is a device farm?

A device farm is a collection of devices — ranging from racks of smartphones and tablets to virtual environments running on emulators — that fraudsters use to mimic real user behavior at scale. With this infrastructure, attackers can automate everything from fake account creation to bonus abuse and payment fraud. By cycling through different devices and resetting them, or leveraging emulators, fraudsters make it difficult for basic security tools to spot patterns or block repeat offenders.

Device farms dramatically raise the stakes because they enable coordinated attacks that are difficult to distinguish from legitimate activity. For example, a fraudster operating a device farm can create thousands of accounts, each appearing to originate from a different device, and use them to exploit promotions, drain loyalty programs, or conduct credential stuffing. The automation and scale possible with device farms make them a top concern for fraud teams, especially as attackers continue to refine their techniques.

Why fraudsters rely on device farms

Fraudsters turn to device farms for several reasons. Most anti-fraud systems limit the number of actions — such as sign-ups or bonus claims — that can originate from a single device. By controlling hundreds or thousands of devices, attackers can easily bypass these limits and scale their operations far beyond what would be possible from one device.

Device farms also enable the automation of fake behavior, from scripted logins to simulated user activity, all while cycling devices and accounts to avoid detection. This approach not only increases the efficiency of attacks but also helps fraudsters blend in with legitimate users. By distributing activity across many devices, attackers conceal the coordinated nature of their fraud, making it much harder for basic defenses to connect the dots.

Why standard IP checks fall short

Many organizations rely on IP address monitoring or rate limiting as a first line of defense against automated attacks. However, device farm operators are adept at sidestepping these controls. They use proxies, virtual private networks (VPNs), and large pools of mobile data connections to mask the true origin of their devices.

Each device in a farm can appear to come from a different IP address or even a different region, making it difficult to spot patterns based on network data alone. As a result, relying on IP-based solutions alone leaves fraud teams unable to see the scale and coordination of device farm activity.

Key signals that expose device farm activity

Modern device intelligence platforms go beyond basic IP analysis, focusing instead on signals that are much harder for fraudsters to fake or rotate. Certain indicators are especially valuable for surfacing device farm activity:

• Repeated or low-variance device attributes: Device farms often spin up devices that share the same hardware and operating system attributes. When the same device characteristics recur across different accounts or sessions, it suggests the same duplicated setup is being reused.
• Emulator and cloned app detection: Many device farms rely on Android emulators or cloned app instances to automate attacks. Detecting these setups helps uncover traffic that isn’t coming from genuine hardware and may be trying to appear as multiple different devices.
• Network patterns: Farmed devices often route traffic through shared infrastructure: shared hosting providers, VPNs, proxies, or rotating IP pools. If many users come from the same IP ranges, especially from datacenter-class providers, or you observe rapid IP churn while device characteristics remain constant, that can signal a device-farm setup.
• Abnormal velocity: Since farms aim to maximize volume, you might see patterns such as many installs, sign-ups, or clicks happening in tight clusters, especially at odd hours, or many new accounts engaging quickly without typical user warm-up behavior (like browsing, idle time, gradual engagement). Farms by design optimize for throughput over realism, so behavior tends to look uniform, fast, and high-volume. Proximity-based anomalies: Even when device fingerprints or IPs vary, modern solutions can detect when multiple devices operate from the same physical area. This kind of clustering suggests the devices are being controlled together rather than by independent users and helps surface fraudulent activity that might otherwise look unrelated. For example, Fingerprint’s Proximity Detection Smart Signal can help surface clustering that’s typical of a device farm. 

By monitoring these signals, fraud teams can spot the telltale signs of device farm activity, even when attackers try to blend in with legitimate users.

How device intelligence outsmarts device farms

Stopping device farm fraud requires solutions that see through the tactics fraudsters use to cover their tracks. Fingerprint’s device intelligence platform combines persistent identification with a suite of Smart Signals to deliver a comprehensive view of every device and session.

Fingerprint uses more than 100 signals to identify devices and browsers, generating a unique visitor ID for each web visitor. Our Smart Signals provide real-time risk indicators such as Emulator Detection, Residential Proxy Detection, Proximity Detection, and more. By correlating these signals with patterns like repeated device attributes, abnormal velocity, and clustering, our platform uncovers device farm activity — even when attackers reset devices, swap IPs, or use advanced evasion tools.

This layered approach allows fraud teams to confidently block coordinated abuse without disrupting legitimate users. Instead of relying on a single signal, our device intelligence evaluates the full context, reducing false positives and surfacing the most relevant threats.

Take action: Build defenses that keep device farms out

Device farms are a fast-evolving threat, and traditional defenses like IP checks are no longer enough to keep fraud at bay. By focusing on signals such as repeated device attributes, emulator usage, proxy usage behavior, velocity anomalies, and clustering patterns, fraud teams gain the visibility needed to detect and stop device farm attacks before they cause real harm.

Our team at Fingerprint is dedicated to helping organizations build smarter, more resilient fraud defenses. If you're looking to strengthen your fraud prevention strategy, we make it easy to get started with a free trial or connect directly with us to discuss your unique challenges. Whether you want to explore how Fingerprint fits into your existing workflow or see our device intelligence in action, we’re ready to help you take the next step.