To create a realistic probabilistic model for this risk, we'll treat it as a chain of conditional probabilities, similar to how crime victimization risks are modeled (e.g., the UK's annual personal crime victimization rate is around 2-3% overall, but drops to <<1% for specific violent crimes due to rarity of offenders, opportunity, and success factors). Cyber risks follow a similar pattern: the UK's annual cybercrime victimization rate for individuals is estimated at about 0.5-5% (based on reports of ~3-5 million personal incidents annually among ~60 million internet users), but MitM attacks are a niche subset (~19% of successful cyber incidents globally, per recent stats, making per-incident risk far lower).
MitM on public WiFi is opportunistic and rare because it requires physical proximity, technical skill, and low detection risk—much like a pickpocketing in a remote area. We'll focus on a single session (e.g., 1 hour at a remote cafe/hotel WiFi with unsecured access). The overall risk \( P \) is the product of independent probabilities:
This ignores \( P(\text{Same Time}) \) by folding it into \( P(\text{Hacker Present}) \), assuming your 1-hour session overlaps with the network's active users. All estimates are derived from available data (e.g., UK population ~69 million; cyber incidents ~8-10 million annually but mostly automated/phishing, not hands-on MitM; MitM ~19% of breaches; success rates 50-80% on unsecured WiFi if attempted). Sources include UK gov reports (e.g., Cyber Security Breaches Survey 2025) and global stats (e.g., JumpCloud 2025 trends).
Here's a breakdown with justifications. You can plug in your own values (e.g., for a busier location) to adjust.
| Factor | Symbol | Description | Realistic Estimate | Justification/Source |
|---|---|---|---|---|
| UK Population | \( N_{UK} \) | Total potential users/hackers in the UK. | 69,000,000 | Mid-2025 estimate from ONS/Worldometer. |
| Number of Skilled Hackers | \( N_H \) | People in UK capable of MitM (e.g., via tools like Wireshark + ARP spoofing; includes pros/script kiddies). | 1,000–10,000 | No direct stats, but UK cybersecurity workforce ~100,000 (mostly ethical); cybercriminals <<1% of that (global cybercrime "ecosystem" ~thousands of actors, per NCSC). Conservative low end for "skilled" MitM (not mass phishing). |
| Fraction of Hackers | \( f_H \) | \( N_H / N_{UK} \). | \( 1.45 \times 10^{-5} \) to \( 1.45 \times 10^{-4} \) (0.00000145–0.0000145) | Derived above; makes encounters rare, like skilled burglars (~5,000 active in UK). |
| Other Users on Network | \( K \) | Average concurrent users during your session (excl. you). Remote spot (e.g., rural cafe). | 5–20 | Assumption for remote/low-traffic; busier urban = 50+. |
| P(Hacker Present) | \( P_{HP} \) | Prob. at least one hacker among \( K \) users (approx. \( K \times f_H \) if low). Includes same-time overlap. | \( 7.25 \times 10^{-5} \) to \( 2.9 \times 10^{-3} \) (low: 1,000 hackers, 5 users; high: 10,000 hackers, 20 users) | Poisson approx. for rarity; aligns with low UK cyber opp. risk (~0.5% annual personal). |
| P(Hacker Attempts) | \( P_A \) | Prob. hacker tries MitM given presence (factoring risk/cost: detection, effort, low remote payoff). | 0.05–0.2 (5–20%) | High legal risk (up to 10 yrs jail under UK Computer Misuse Act); low gain in remote spot (few targets). ~10% of skilled hackers opportunistically scan public nets per session (est. from threat reports). |
| P(Attack Succeeds) | \( P_S \) | Prob. MitM works (e.g., ARP poisoning + traffic interception) on unsecured WiFi. | 0.5–0.8 (50–80%) | High if no VPN/HTTPS (common on public WiFi); drops with protections. 19% of all successful attacks are MitM, but conditional success ~50-80% per attempt (F5/ JumpCloud stats). |
| P(Meaningful Impact) | \( P_I \) | Prob. attacker extracts usable data (e.g., creds, not just junk traffic) and acts on it. | 0.2–0.5 (20–50%) | Most traffic encrypted (HTTPS); impact requires unpatched device + valuable session (e.g., banking). ~30% avg. from breach analyses. |
Example Calculation (Conservative Remote Scenario): 1,000 hackers, 5 users, mid-range other probs.
\[ P = (5 \times 1.45 \times 10^{-5}) \times 0.1 \times 0.7 \times 0.3 \approx 1.5 \times 10^{-6} \ (0.00015\% \ per \ session) \]
Interpretation: About 1 in 660,000 sessions. For 50 sessions/year (e.g., travel), annual risk ~0.00075%—far lower than general UK cyber risk (0.5%) or street crime (1-2%).
Optimistic (Higher Risk) Scenario: 10,000 hackers, 20 users, max probs → \( P \approx 8.7 \times 10^{-5} \) (0.0087%, or 1 in 11,500 sessions). Still very low.
Adjust the inputs below and calculate your estimated risk per session. (f_H is auto-calculated from N_H / N_UK)
This mirrors low baseline risks (e.g., UK lightning strike: ~1 in 1M/year) while scaling with factors. If you provide specifics (e.g., location size, your sessions), I can refine the numbers.