As fraud attacks grow increasingly sophisticated, enterprises face a critical challenge: basic device tracking is no longer enough. Today’s threat landscape demands a deeper understanding of not just who is accessing your systems, but how they’re behaving and why their activity raises red flags. This distinction lies at the heart of two complementary approaches—device identification and device intelligence. While device identification answers “who is this?”, device intelligence reveals “what are they doing, and does it look suspicious?” Organizations seeking robust protection against modern fraud need solutions that harness the strengths of both approaches.

Understanding Device Identification

Device identification creates unique fingerprints to recognize returning devices across sessions. By collecting attributes like browser version, operating system, screen resolution, and hardware configurations, these systems build consistent device “signatures” that persist over time. Combined with cookies and other persistent identifiers, device identification excels at tracking known entities.

In fraud prevention, this capability proves invaluable for specific use cases. Security teams rely on device identification to track known bad actors across multiple sessions, detect account takeover attempts from previously flagged devices, and recognize repeat offenders attempting to circumvent basic security measures. When a device has been associated with fraudulent activity, identification systems can instantly flag or block subsequent access attempts.

However, device identification has inherent limitations. Sophisticated attackers can spoof device attributes, clear cookies, or cycle through different device configurations. Additionally, these systems struggle to differentiate between legitimate device changes—like software updates or new browsers—and malicious attempts to mask identity.

Understanding Device Intelligence

Device intelligence takes a fundamentally different approach through real-time behavioral analysis and contextual assessment. Rather than focusing solely on static attributes, intelligence systems analyze dynamic behavioral signals including interaction patterns, mouse movements, typing cadence, and navigation flows. They detect environmental anomalies like emulators or virtual machines, assess the consistency of claimed device attributes, and generate risk scores based on multiple contextual factors evaluated simultaneously.

This behavioral focus enables device intelligence to shine in detecting sophisticated threats. It identifies suspicious patterns indicative of automation, uncovers emulators and virtual machines attempting to appear as legitimate mobile devices, and reveals fraud farms operating with constantly rotating device profiles. Because device intelligence evaluates how users interact with applications rather than just what device they claim to use, it adapts effectively to attackers who continuously evolve their tactics and becomes significantly harder to circumvent through simple attribute manipulation.

Different Attack Types Require Different Approaches

The fraud landscape encompasses vastly different attack methodologies, each requiring specific defensive capabilities. Volumetric bot attacks—including credential stuffing campaigns, inventory hoarding, and aggressive web scraping—rely on speed and scale. These rapid-fire attacks create detectable patterns that device intelligence catches effectively: automated interaction speeds, behavioral inconsistencies across requests, and telltale signs of scripted activity. Device identification supports these efforts by blocking known bot infrastructures and preventing previously identified malicious devices from accessing systems.

Conversely, low-and-slow attacks originating from human operators or fraud farms take a fundamentally different approach. These attacks deliberately mimic legitimate user behavior, spreading fraudulent activities across extended timeframes to avoid detection thresholds. Here, device identification becomes the primary defense, tracking devices across multiple subtle fraud attempts over days or weeks and connecting seemingly unrelated suspicious activities to common sources. Device intelligence complements this by identifying environmental anomalies common in fraud farm operations—inconsistencies in device configurations, location spoofing attempts, and subtle behavioral patterns that distinguish manual fraud from genuine customer activity.

Relying exclusively on either approach leaves dangerous blind spots. Attackers continuously adapt their strategies, and a defense optimized only for fast-moving bots or only for persistent device tracking will inevitably miss emerging threats.

Enterprise Considerations: Why Integration Matters

Today’s fraud landscape demands layered defense strategies. Solutions combining device intelligence and device identification deliver comprehensive visibility across the entire attack spectrum, from lightning-fast bot swarms to patient, human-driven fraud campaigns. This integrated approach reduces false positives through corroborating signals—when both behavioral analysis and device history indicate risk, confidence in the assessment increases dramatically. As threats evolve, combined systems adapt more effectively than single-approach solutions.

For enterprises, the business impact extends beyond security metrics. The right solution protects user experience while stopping fraud, avoiding the friction that drives legitimate customers away. It scales to handle enterprise transaction volumes without degrading performance. And it provides actionable insights that help security teams understand not just what happened, but why, enabling continuous improvement of fraud prevention strategies.

When evaluating vendors, enterprises should ask pointed questions about technology integration: How do behavioral signals inform device risk scores? Can the system detect both automated and manual fraud? Does the solution adapt to new attack patterns without requiring constant manual rule updates?

Arkose Labs: Leading with Comprehensive Protection

Arkose Labs has established itself as an industry leader by seamlessly integrating both device intelligence and device identification within a unified platform. Their bot management and device identity solutions combine real-time behavioral analysis with persistent device tracking, delivering protection against both volumetric bot attacks and sophisticated fraud farm operations. This comprehensive approach continuously adapts to emerging attack vectors, ensuring customers stay protected as the threat landscape evolves. With proven effectiveness across diverse industries and recognition for innovation in fraud prevention, Arkose Labs demonstrates that the future of enterprise security lies not in choosing between device intelligence and identification, but in leveraging the power of both.

*** This is a Security Bloggers Network syndicated blog from Arkose Labs authored by Casey Joyce. Read the original post at: https://www.arkoselabs.com/blog/are-you-only-identifying-devices-or-actually-understanding-them/