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

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Presentation on theme: "Where Do All the Attacks Go? Dinei Florencio and Cormac Herley Microsoft Research, Redmond."— Presentation transcript:

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

2 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?

3 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 Charles Alice Attacks Charles

4 Problems with the threat model 8.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. 9.Fails to explain the observations 20% choose dog’s name as password Avoiding Harm ≠ Security

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

6 Attacks Alice(i) exerts effort e i (k) against Attack(k) Probability she succumbs: Pr{e i (k)} – Pr{e i (k)} monotonically decreasing with effort Gain to Charles(j) from Alice(i): G i Cost for Attack(k), N users: C j (N,k) Pr{e i (k)} e i (k) # Users Cost

7 Charles(j) Expected Return U j (k) Prob. Alice(i) succumbs Gain from Alice(i) Cost of Attack(k) For N users Charles(j) selects Attack(k) that maximizes U j (k) Prob. fraud detected U j (k) =

8 Sum-of-efforts Defense (1-Pr{SP}) Σ i Pr{e i (k)} G i - C j (N,k) Sum over all attacked users of weighted efforts against Attack(k) Recall as e i (k) increases Pr{e i (k)} decreases Increasing effort from users decreases return

9 Followed by Best-Shot Defense (1-Pr{SP}) Σ i Pr{e i (k)} G i - C j (N,k) Fraud detection at Service Provider: Charles(j) must evade all detection measures

10 So, where do all the attacks go?

11 Average Success Rate Too Low Attack unprofitable if: (1-Pr{SP}) Σ i Pr{e i (k)} G i < C j (N,k) If average success = 1/N Σ i Pr{e i (k)} is too low then whole attack unprofitable. Even if many profitable targets exist Similarly, if average value too low – i.e. G i small

12 Attackers Collide Too Often Recall attackers compete for vulnerable users Suppose Attack(k) has deterministic outcome 1 if e i (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 e i (k) < ε Alice(i) ends up with M attackers in acct – In general share Gi with MPr{e i (k)} other attackers Alice(i) Charles(j) Pr{e i (k)} =

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

14 Fraud Detection Too High (1-Pr{SP}) Σ i Pr{e i (k)} G i - C j (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

15 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(i 0 ) continues with “abcdef” Profitability of brute-forcing plummets – Alice(i 0 )’s risk of harm  0 (w\o action on her part)

16 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 10 8

17 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 G i > (10x10x100/3)x7.25 = $24200 Suppose he makes (US min wage)/10 – Needs: Gi > $2420/acct Exercise: find profitable assumptions

18 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

19 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

20 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

21 Conclusions 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. Alice(i) Charles(j)

22 “And then what?”

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