Where Do All the Attacks Go?

Where Do All the Attacks Go? Dinei Florencio and Cormac Herley Microsoft Research, Redmond

Why isn’t everyone hacked every day? • Webroot Survey: • 90% share passwords across accounts • 41% share passwords with others • 20% use pet’s name as password • Endless stream of new attacks every year • E.g. read LCD screens from reflections etc • If things are so bad, how come they’re so good?

Traditional Threat Model • Alice is a user • Charles attacks • Phishing, keyloggers, guessing, password-reuse • Malware, rootkits, • Physical side-channels, ………… • Security as good as weakest link Attacks • Charles • Charles • Alice

Problems with the threat model • It is numerically impossible (2 billion users) • At 1000:1 ratio (i.e. 2 million attackers) • Attackers = 1/3 as many as sw developers • US undergrad gets 50x more attention from Profs than Alice gets from Charles. • Idea that someone identifies/exploits weakest-link does not scale. • Fails to explain the observations • 20% choose dog’s name as password • Avoiding Harm ≠ Security

A Threat Model that Scales Internet Users Alice(i) Attackers Charles(j) • Population of users • Population of attackers • Attacker doesn’t know you from a honeypot • Attack when Expected{Gain} > Expected{Cost} Attacks

Attacks • Alice(i) exerts effort ei(k) against Attack(k) • Probability she succumbs: Pr{ei(k)} • Pr{ei(k)} monotonically decreasing with effort • Gain to Charles(j) from Alice(i): Gi • Cost for Attack(k), N users: Cj(N,k) Pr{ei(k)} Cost ei(k) # Users

Charles(j) Expected Return Uj(k) So, Charles(j) gain: (1-Pr{SP}) - (N,k) Uj(k) = Prob. fraud detected Prob. Alice(i) succumbs Gain from Alice(i) Cost of Attack(k) For N users • Charles(j) selects Attack(k) that maximizes Uj(k)

Sum-of-efforts Defense (1-Pr{SP}) ΣiPr{ei(k)} Gi - Cj(N,k) Sum over all attacked users of weighted efforts against Attack(k) • Recall as ei(k) increasesPr{ei(k)} decreases • Increasing effort from users decreases return

Followed by Best-Shot Defense (1-Pr{SP}) ΣiPr{ei(k)} Gi - Cj(N,k) Fraud detection at Service Provider: Charles(j) must evade all detection measures

So, where do all the attacks go?

Average Success Rate Too Low • Attack unprofitable if: (1-Pr{SP}) ΣiPr{ei(k)} Gi< Cj(N,k) • If average success = 1/N ΣiPr{ei(k)} is too low then whole attack unprofitable. • Even if many profitable targets exist • Similarly, if average value too low • i.e. Gi small

Attackers Collide Too Often Alice(i) • Recall attackers compete for vulnerable users • Suppose Attack(k) has deterministic outcome 1 if ei(k) < ε 0 otherwise • Example: brute-force using 10 popular pwds • abcdef, password, 123456, password1, etc • Every attacker who tries succeeds in same places • If ei(k) < ε Alice(i) ends up with M attackers in acct • In general share Gi with MPr{ei(k)} other attackers Charles(j) Pr{ei(k)} =

Attack(k) too expensive (relative to alternatives) • Attack(k’) is cheaper Uj(k) < Uj(k’) for all attackers • Example: real-time MITM vs. pwd stealing

Fraud Detection Too High (1-Pr{SP}) ΣiPr{ei(k)} Gi - Cj(N,k) • Pr{SP}  1 then return  0 • Example: • Alice(i)’s bank detects 99% of attempted fraud • True protection is not Alice(i)’s effort

The Free-Rider Effect • Suppose brute-forcing is a profitable attack • All-but-one Internet users (finally) decide to get serious and choose strong passwords • Alice(i0) continues with “abcdef” • Profitability of brute-forcing plummets • Alice(i0)’s risk of harm  0 (w\o action on her part)

Choosing Your Dog’s Name as Password • User chooses bank password = dog’s name • Easy money, right? • How many users have……… • Bank password = dog’s name? Say, 1% • Auto discover dog’s name? Say, 1% • Auto discover userID? Say, 1% • How many other Charles(j) use strategy? Say, 100 • Return is reduced by 108

Dog’s Name as Password • Suppose instead: • 10 mins to discover dog’s name • 10 mins to discover userID • Thus 20 mins on average to get 1% of accts. • Compete with 10 other attackers • Bank catches 90% of attempted fraud • At $7.25/hour acct should be worth Gi > (10x10x100/3)x7.25 = $24200 • Suppose he makes (US min wage)/10 • Needs: Gi > $2420/acct • Exercise: find profitable assumptions

Domino Effect of Acct. Escalation • Leveraging low-value accts to high • Password re-use across accts, etc. “One weak spot is all it takes to open secured digital doors and online accounts causing untold damage and consequences.” Ives etal 2004

Leverage Low-Value Account To High? • Is this profitable on average • Given N webmails… • X% are contact email for bank • Y% userID can be determined automatically • Z% of banks email pwd reset link • W% the Secret Questions auto determined • Return dramatically reduced. For example • 0.1 x 0.01 x 0.1 x 0.05 = 0.00005 (1 in 200,000) • So 5 bank accts for every million webmails

Diversity is more Important than Strength • Password is ………… • Dog’s name, cat’s name • Significant date, sports team • Written under keyboard • How common a strategy is matters more than how secure it is

Conclusions Alice(i) • Avoiding Harm ≠ Security • Internet attackers face sum-of-effort defense • Avoiding harm is much less expensive than being secure • “Thinking like an attacker” doesn’t end when an attack is found. Charles(j)

“And then what?”

Where Do All the Attacks Go?