In today’s digital world, attackers increasingly use compromised credentials to impersonate legitimate users during active sessions. Traditional systems may not detect these threats, as logins appear “valid.”

The Challenge:

• Valid credentials don’t guarantee a valid user

• Traditional methods struggle with subtle shifts in behavior

• Static rules miss evolving impersonation techniques

• Real-time detection requires fast and scalable analysis

Quantum-Inspired Solution

Quantum-inspired models are ideal for detecting tiny but meaningful behavioral changes that indicate impersonation, even when credentials are valid.

Behavioral Fingerprinting via Multi-Dimensional Modeling

HessQ builds a dynamic fingerprint for each user session using typing patterns, navigation flow, mouse movement, timing, and more

When a session starts to deviate from learned patterns, our QUBO engine flags it as a potential impersonation in real time

he model processes live behavior as it happens — allowing instant alerts or multi-factor authentication triggers when a session turns suspicious

QUBO-Based Pattern Deviation Detection

Zero-Lag Alerts

The Results:

• Detects impersonation within seconds of session deviation

• Identifies non-obvious behavior changes, like cursor hesitation or page sequence mismatch

• Reduces user friction by minimizing unnecessary alerts

• Cuts down the time attackers remain undetected in accounts