Understanding behavioral data signals with device intelligence

Behavioral biometrics and device intelligence are advanced techniques that analyze unique user behavior patterns and device characteristics, which is critical for advanced fraud prevention. These techniques play a crucial role in identifying fraudulent activities and protecting against unauthorized access. SEON is integrating behavioral data signals into its device intelligence solution to strengthen fraud detection and enhance security measures across various applications.

Understanding behavioral patterns for form fill-out, mouse movement and keystroke dynamics enhances the security and user experience. By monitoring these unique interactions, SEON's Device Intelligence solution can identify typical behaviors and detect anomalies. These techniques are essential for fraud detection, continuous authentication and user interface optimization. Leveraging these insights helps prevent unauthorized access and improve the overall design and functionality of web applications.

Form Fill-out Analysis

Monitors how a user interacts with and fills out forms, including the time taken to complete each field, the order of field completion and the corrections or edits made. It analyzes typing patterns, pauses and the overall flow of data entry.

Mouse Movement Analysis

Monitors how a user moves their mouse, capturing data on speed, acceleration and the specific paths taken. This analysis includes tracking cursor positions, movement patterns and interaction with different elements on a webpage.

Keystroke Dynamics

Captures and analyzes the unique patterns of a user's typing behavior, including the timing of key presses and releases, typing speed and rhythm. This analysis involves measuring dwell time (the duration a key is pressed) and flight time (the interval between key presses).

SEON implements behavioral pattern analysis into its device intelligence SDK to enhance fraud detection. By continuously monitoring form fill out, mouse movement, and keystroke dynamics, the SEON's Device Intelligence solution  identifies anomalies and untypical user behaviors. It dynamically generates a "suspicious flags" field, which aggregates signals of potentially fraudulent behavior. This real-time flagging system helps detect and prevent fraud more effectively, providing robust security for web applications.

New Suspicious Behavior Signals

Suspicious keypress characteristics

Detects and flags irregular typing patterns, such as abnormal key press durations, inconsistent typing speeds, or unusual key sequences, which may suggest fraudulent behavior or automated inputs. This feature helps identify deviations from normal typing patterns that could indicate potential threats.

Suspicious mouse movement

Identifies and flags unusual cursor behavior, such as erratic or rapid movements, irregular paths, or atypical interaction patterns, which may indicate fraudulent activity or automated scripts. This analysis helps differentiate between human and automated interactions, enhancing security.

Suspicious form fill out

Detects and flags unusual or atypical patterns in how users complete forms, such as rapid entry, inconsistent field completion, or unexpected input behaviors. This feature helps recognize potentially fraudulent or automated form submissions, ensuring data authenticity.

Paste used

Identifies and records instances where users paste text or data into form fields instead of typing it manually. This detection helps flag potential automation or suspicious behavior that deviates from normal user interactions.

Autofill used

Detects and logs instances when form fields are automatically populated by the browser or a password manager, rather than manually entered by the user. This feature helps distinguish between legitimate user inputs and automated data entry methods, contributing to overall fraud prevention.

Device intelligence captures a snapshot of a device's attributes in a point in time. To enhance this, continuous behavioral data collection — such as keystroke dynamics, mouse movement, and form fill out patterns — provides deeper insights and more accurate detection of fraud.

Every website has a unique structure, with varying designs and user interaction elements. When implementing form fill out and behavioral analytics, it is crucial to target the specific elements of your user interface (UI) that are most relevant to your security needs. SEON’s SDK offers the flexibility to customise and focus on the most important inputs, ensuring precise and effective behavior monitoring.

Customizing Behavioral Data Collection

Our SDK allows you to specify which input fields and forms should be targeted for behavioral analysis. This customisation ensures that you capture the most critical user interactions, enhancing fraud detection and providing a tailored security solution for your website.

How to Customize Targets

By default, the SDK analyzes user interactions across the entire page. However, you can narrow down the focus to specific elements using the behavioralDataCollection configuration option.

Setting up rules

In the Scoring engine the suspicious behaviour signals is available in the suspicious_flags field.
If you want to apply a behaviour rule on the transaction you should include one the the following values in the compare rule:

• suspicious_keypress_characteristics
• suspicious_mouse_movement
• suspicious_form_fillout
• paste_used
• autofill_used
   

Tip: For detailed integration information check out our Javascript Agent and Fraud API documentation and our GitHub page.