Session Tampering Detection via Multi-Point Device Fingerprinting
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Solution Overview
Problem
Online transactions face significant risks of session tampering and hijacking due to the transmission of sensitive information over unsecured networks, leading to fraud and identity theft, despite existing security measures.
Innovation Solution
Collecting and analyzing multiple computer or device fingerprints and Session IDs throughout an online session to detect and prevent session tampering and hijacking by comparing fingerprints at various instances and time intervals, allowing for timely responses to prevent fraud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple device fingerprints are collected and compared throughout an online session, then session tampering detection capability is improved, but system complexity and processing overhead increase
Solution Approach 1:
The system collects device fingerprints at multiple predetermined points throughout the online session before tampering can occur. By establishing baseline fingerprints in advance at login and at intermediate checkpoints, the system prepares detection capabilities proactively rather than reactively, improving reliability without requiring complex real-time analysis infrastructure
Solution Approach 2:
The session monitoring is divided into discrete segments with fingerprints collected at specific intervals and checkpoints. This segmentation allows the system to manage complexity by processing small, manageable fingerprint comparisons at each segment rather than continuous monitoring, reducing overall system complexity while maintaining detection capability
2Measurement precision
If device fingerprints are collected at multiple time intervals during a session, then fraud detection accuracy is improved, but loss of time and processing overhead increase
Solution Approach 1:
The system implements periodic fingerprint collection at predetermined intervals and checkpoints during the session rather than continuous monitoring. This periodic approach achieves fraud detection accuracy by capturing device state at multiple points while minimizing time loss by avoiding constant processing, allowing the system to balance precision with efficiency
Solution Approach 2:
The system collects fingerprints at strategically selected checkpoints rather than at every possible moment. This partial action approach provides sufficient detection accuracy by focusing on critical session transitions (login, page navigation, form submission) without the excessive time cost of continuous fingerprint collection and comparison
3Reliability
If session monitoring is performed continuously, then session hijacking detection is improved, but use of energy and computational resources increase
Solution Approach 1:
The system establishes baseline device fingerprints and monitoring checkpoints in advance during normal session operation. By preparing detection frameworks beforehand rather than analyzing every session event in real-time, the system improves hijacking detection reliability while minimizing continuous computational resource consumption and energy usage
Data Source
AI summary
The invention provides methods and apparatus for detecting when an online session is compromised. A plurality of device fingerprints may be collected from a user computer that is associated with a designated Session ID. A server may include pages that are delivered to a user for viewing in a browser at which time device fingerprints and Session ID information are collected. By collecting device fingerprints and session information at several locations among the pages delivered by the server throughout an online session, and not only one time or at log-in, a comparison between the fingerprints in association with a Session ID can identify the likelihood of session tampering and man-in-the middle attacks.