Cybercrime Deterrence Model: Risk vs Reward

Defensive Investment

Detection investment 55%

Punishment severity 50%

Target hardening 40%

Potential gain per attack 60

Live Readouts

Expected utility E[U]

0.0

Predicted attacker share

Attacks in flight

Arrests (cum.)

Value protected (cum.)

Value breached (cum.)

Legend

Idle attacker (deterred / waiting) Attack in progress Blocked or caught — defense held Successful breach — value lost

How it works

Each of the 180 agents in the ring represents a would-be attacker deciding, every instant, whether cybercrime is worth it — modelled on Gary Becker's economic theory of crime (1968), the standard rational-choice framework criminologists and policy analysts still use to reason about deterrence:

E[U] = P(success)·Gain − P(caught)·Penalty
P(blocked)  = hardening · 0.85
P(caught)   = detection · (1 − P(blocked))
P(success)  = 1 − P(blocked) − P(caught)

Each agent carries its own fixed risk threshold, sampled from a bell-shaped population spread (some are cautious, some reckless). An agent only attempts an attack once E[U] exceeds its personal threshold — this is why raising any single lever rarely stops crime outright, but shifts the fraction of the population for whom the sum stops adding up.

Detection investment — the probability an attack is traced back to its source (SOC monitoring, threat intel, forensics).
Punishment severity — the cost imposed once caught (prosecution, fines, sentencing).
Target hardening — technical controls (patching, MFA, segmentation) that block the attack outright before detection even matters.
Potential gain — the payoff an attacker expects from a successful breach; raising it (e.g. more valuable data) pushes more of the population past their threshold.

Watch the balance of colors at the center: a green-heavy ring means deterrence and hardening are winning; a red-heavy ring means the expected payoff still beats the risk for too much of the population — exactly the dynamic real prevention programs are built to shift.

attack rate: 0% t = 0s

⚠ Couldn't load the 3D engineThree.js failed to load from the CDN. Check your connection and reload.